Hype Cycle for HR Technology, 2024
ARCHIVED
30 July 2024 - ID G00811342 - 145 min readBy Jeff Freyermuth
As HR leaders increasingly manage complex technology portfolios, they must evaluate new capabilities while prioritizing investments that provide the most business value. This Hype Cycle presents the latest innovations, providing insights on maturity levels of the various emerging technologies.
Strategic Planning Assumptions
By 2025, 60% of enterprise organizations will adopt a responsible AI framework for their HR technology and achieve greater employee experience and trust in the organization.
By 2028, organizations with employees successfully completing 90% of their HR tasks through a conversational interface will reduce HR team headcount by less than 5%.
Analysis
What You Need to Know
This year’s Hype Cycle reflects a limited number of new innovations as vendors and users struggle with generative AI (GenAI) early use cases and implications. In addition, we have witnessed an overfocus on GenAI across all tech providers, leaving limited room for major investment in other areas. Most innovations continue to work their way through the trough as they haven’t lived up to their overinflated expectations.
With the ongoing hype, Gartner is witnessing a significant uptick in interest and demand for AI and GenAI, so client conversations focus on efficiency, productivity while considering responsible AI. Gartner recently asked HR leaders how far along they were in planning and preparing for GenAI implementation in a live polling webinar in June 2023 and January 2024. The survey data showed 19% of HR leaders polled in June 2023 were either conducting pilots, already planning implementation or had already implemented GenAI. Gartner did the same survey in January 2024, and these same categories had a cumulative total of 38%. Uncertain labor, economic and geopolitical conditions add another layer of urgency to embrace innovations that support a flexible HR strategy. Meanwhile, the demand for employee experience and human-centric work design continues.
HR leaders must continue efforts to:
- Manage a technology portfolio of increased complexity without compromising the user experience. Multiple new AI-enabled applications (and sometimes vendors) must be budgeted, selected, implemented, piloted and potentially integrated alongside existing enterprise solutions.
- Evaluate the human capital management (HCM) suite for both current and expected future capabilities. Suite providers can provide “good enough” solutions in a number of innovation areas or plan to provide them at specific future intervals.
- Implement in an agile way through targeted experiments. Many of these innovations require behavioral and cultural changes that take time. Instead of waiting for change to happen, identify systems of record, systems of differentiation and systems of innovation, and then select the right areas inside the organization to pilot innovations that demonstrate business value.
The Hype Cycle
The technologies featured in this Hype Cycle respond to the need for greater flexibility, human-centric work and employee experience (EX). New entrants to the Hype Cycle include innovations for workstyle analytics and AI in HR. AI in HR has been included in other Gartner Hype Cycles in years past, but warrants inclusion in this report due to HR leaders’ growing interest.
GenAI and skills continue to experience massive hype. Especially relevant to HR is GenAI’s ability to generate documents and answer employee questions. Text generation can be used for candidate communications, job description creation, recruiting and coaching, plus various learning use cases. This includes course descriptions, quizzes and learning content creation. For additional use cases in learning and development (L&D), see AI Use-Case Comparison for Learning and Development.
Internal talent marketplaces (ITMs) and AI-enabled skills management have both experienced considerable adoption and increased hype. In fact, the market for ITM continues to expand and grow. For more detailed analysis of the ITM market, see Market Guide for (Internal) Talent Marketplaces. Additionally, the use cases for AI-enabled skills management continue to evolve, see Innovation Insight for AI-Enabled Skills Management.
With all the hyperbole around AI, it’s not surprising that the fast movers in this year’s Hype Cycle include generative AI in HR, HR virtual assistants, and internal talent marketplaces. Frontline worker EXTech has also moved quickly due to growing demands, especially for younger workers in select industries, such as retail and grocery.
Meanwhile, blockchain in HCM continues to disappoint as it fails to realize value. Although blockchain has the potential to cut down on time-consuming and redundant processes like background verification or processing payment approvals, those problems are not pressing enough to justify the considerable investment required to deploy blockchain in HCM. Similarly, responsible AI, and VR and AR in corporate learning have not matured as quickly as other innovations. For additional innovation areas in HR technologies, please see Hype Cycle for Talent Acquisition (Recruiting) Technologies, 2024.
The Priority Matrix
The Priority Matrix groups include technologies in terms of their potential level of benefit and the number of years until they reach mainstream adoption. For example, generative AI in HR has the potential to fundamentally change how work is done across business units, with responsible AI dictating how AI is managed throughout the organization. Internal talent marketplaces support new, agile ways of working. They impact how employees find work and growth opportunities, how managers and project managers find and use talent, and how staffing and personnel budget decisions are made.
Gartner has assigned a benefit rating to each technology according to the expected outcome of that technology’s deployment in most cases. For example, we believe investing in PaaS in HR technology will usually be moderately beneficial, but it might be transformational in some cases for organizations.
Priority Matrix for HR Technology, 2024
| Benefit | Years to Mainstream Adoption | |||
|---|---|---|---|---|
| Less Than 2 Years | 2 to 5 Years | 5 to 10 Years | More Than 10 Years | |
Transformational | ||||
High | ||||
Moderate | ||||
Low | ||||
Source: Gartner (July 2024)
Off the Hype Cycle
- Line manager comp allocation tools was the only IP to graduate from the Hype Cycle in 2024.
On the Rise
Blockchain in HCM
Analysis By: Ranadip Chandra
Benefit Rating: Low
Market Penetration: Less than 1% of target audience
Maturity: Embryonic
Definition:
A blockchain is an expanding list of cryptographically signed, irrevocable transactional records shared by all participants in a network. Blockchain in human capital management (HCM) refers to applications that leverage blockchain to authenticate and manage a shared version of employee data and processes across organizations.
Why This Is Important
The main advantages that blockchain brings to HCM technology are trust and transparency around employee data that can be shared across multiple organizations and can be controlled by the employees themselves. Added levels of trust supported by distributed ledgers can cut down many time-consuming and redundant processes in HCM. Such processes include manual background verification for shortlisted candidates, payment processing approvals, cross-border payment, currency adjustment and many others.
Business Impact
Most HR-relevant blockchain projects are carried out in the areas of identification and credential management and in payroll management (such as building applications for identification and verification or networks for data processing and management). Blockchain-technology-based applications are designed to streamline hiring, improve the verification of candidates’ qualifications, and transform the way worker credentials are shared and verified. Although due to unclear business value, blockchain in HCM has not progressed beyond the early stage.
Drivers
Blockchain adoption in enterprise applications remains low despite potential benefits. This is largely because most enterprises who try to adopt the technology set up private blockchains and do not have sufficient awareness about the time and resources required to run an enterprise blockchain HR application. Adoption would have improved if there were more off-the-shelf HCM blockchain applications that enterprises could easily access and use.
Some practical use cases behind the embryonic yet tangible development are:
- Employee career credentials: Authenticated independently by past employers and/or educational institutions, and can be reused by a candidate in each subsequent application. Tamper-proof credentials are useful for industries like healthcare, where the certifications and mandatory training are critical for candidate evaluation, but the window of assessment needs to be quick.
- Smart contracts for a gig economy: Specify the conditions agreed upon by the employer and the candidate. Once the contract is successfully completed, a prior approved sum held in an escrow account can be automatically released and credited to the professional’s account. This is particularly applicable for artists and creative professionals trying to sell their products on public platforms without any intermediary. They are paid for their work, based on contract terms that they set themselves.
- Blockchain payroll platforms: Transactions through cryptocurrency exchange enable payments directly to employees without requiring any bank’s involvement.
Obstacles
- Blockchain supports decentralized identity, where users own and control their identity data and decide who can view it. This runs counter to current business models where vendors and organizations own and sometimes monetize employee data.
- The biggest obstacle for blockchain adoption in enterprise applications has been the failure of permissioned blockchain platforms to gather any momentum. The vendor-controlled enterprise blockchain networks are neither decentralized nor do they address the default central weakness that makes them vulnerable to cyberthreats.
- Many vendors that offered HR technology use cases have rebranded and are using their blockchain platforms for applications such as supply chain management, where adoption is relatively higher.
- If the obstacles are not addressed successfully to reignite end user interest, blockchain in HCM will most likely head into obsolescence and will need to be removed from the Hype Cycle for HR Technology. The usage scenarios may reappear as web 3.0 applications in future.
User Recommendations
- Evaluate operational costs, technology debt, business benefits and the evolution of the blockchain technology landscape before investing.
- Pilot with not more than one application to observe the results in the near term and identify the most suitable use case from the list.
- Leverage the success to justify further investment in blockchain platforms in other areas within HCM technology.
- Participate in ecosystem-led enterprise software development opportunities, such as collaborative ecosystem product development, as beta users to gain early access to innovative development that may prove to be highly impactful in the long term.
- Prioritize the development of Web3 applications. Web3 is the next iteration of the World Wide Web, envisaged as being decentralized and powered by blockchain technologies. These innovations can help modernize HR applications.
- Experiment with decentralized ownership of employee data for specific use cases to evaluate the degree of change required and the business impact of such decentralized ownership.
Gartner Recommended Reading
Workforce Nudgetech
Analysis By: Rania Stewart
Benefit Rating: High
Market Penetration: Less than 1% of target audience
Maturity: Embryonic
Definition:
Workforce nudge technology (nudgetech) is a form of AI-enabled choice architecture designed to elicit behaviors aimed at accelerating targeted positive outcomes at the individual, team and organizational level. Nudgetech incorporates behavioral economic principles, hyperpersonalized through AI. Nudges come with the freedom of choice and are often based on a combination of worker profile and behavior data.
Why This Is Important
Nudgetech can be transformative in its potential to enable high-impact behavioral change, often with low-effort investment by the individual. Nudgetech is seeing traction in leading-edge people development, personal productivity and employee experience applications. Use-case relevancy continues to grow and expand, particularly where desired behaviors are not immediate or certain (requiring greater interpretation, judgment and agency of choice, hence benefiting from nudge guidance).
Business Impact
Nudgetech uses technology to drive small, beneficial behavioral changes that are perceived as being good for both workers and the organization. These small changes are designed to effectively compound to have a greater impact on a prioritized behavioral outcome, which is typically less tangible in nature, and on more attributes of an overarching organizational culture. Applied examples (still emerging) include nudging toward a more agile/adaptive/innovative culture, a more security-minded culture, a more development-oriented culture and even a more human-centric leadership culture.
Drivers
- Personalized guidance is invaluable to change, learning and improvement initiatives at every level (individual, team, department and organization). It is simultaneously difficult to scale, due to the combination of required subject matter expertise and contextual knowledge required of the individual and their team/organization.
- Targeted persona mapping is not granular enough for “trusted coach” or “human connection” nurturing, which many technology companies, in particular, are striving to improve. The proliferation of generative-AI-enabled assistants can particularly be improved by nudgetech in those applied use cases where the bar needs to be raised from assistant to personal coach.
- The scalability challenge drives the value proposition of nudgetech to close the behavioral gap from where you are today to where you ideally want to be tomorrow. The most concentrated workforce-targeted use-case applications observed to date include enabling the following outcomes — agile culture and adaptive teams, inclusion and belonging, manager and leader effectiveness, proficiency with digital tools, security-conscious culture, and well-being and personal effectiveness.
- The forgetting curve, the rate at which information is forgotten when not reinforced, can be a significant inhibitor to change adoption. Nudging humans to adopt new behaviors in the flow of work, for example, when sending an email to a colleague whose working hours are different, can provide a powerful reinforcement aiding in retention.
Obstacles
- Lack of definition: Nudgetech is not yet sufficiently far along to have a commonly accepted definition.
- Filter the nudge noise: A nudge is not a reminder or a notification by itself. Those are common delivery mechanisms that are often, understandably, referred to as “nudges,” but lack the systematic rigor of nudge technology.
- Is it really AI-enabled?: This can be difficult to uncover, in that the behavioral economics of nudge technology will likely present as more static, decision-tree logic. This should be complemented by AI-driven feedback loops, where the system learns which nudges work better for which people (completion rates) and outcomes (impact tracking).
- “Sludge” vs. nudge: Employees may develop “nudge fatigue” from too many nudges or ineffectual and/or less relevant nudges that ultimately deter progress.
- Choice is key: If there’s no option to pass, it is not a nudge, but rather a prescriptive action, which is less effective in achieving sustainable behavioral change. It also runs the risk of perceived behavioral manipulation when the choice is diminished.
- Lack of trust: Relevance (cultural sensitivity/preferences/appropriateness/timeliness) and autonomy are the lubricants of good nudges that serve first and foremost the individual and then, secondarily, the organization indirectly. Nudges must elevate beyond “first, do no harm” to a worker-centric “I serve you first and foremost” if they are to be trusted suggestion sources that can accelerate behavior change.
User Recommendations
- Prioritize which organizational outcomes may benefit the most from nudge technology. The ideal fit would be an outcome theme that enables you to start small, with easy but potentially high-impact outcomes (see Create Self-Sustaining Culture Hacks by Applying Nudging Techniques).
- Experiment selectively with isolated proofs of concept within your own organization. Depending on available in-house skills and expertise, it may be an option to pursue this as an internal build. Many larger organizations have the requisite data science capability. If yours does not, consider contracting with an organizational psychologist or related firm to create the nudge library.
- Encourage bidirectional discussions with prospective or existing vendors. How do you encourage select prospective vendors (or even current ones) to consider the pros and cons of investing in nudgetech? You ask them. You put it on their radar. You encourage bidirectional discussions.
- Listen to employees. Involve workers in the design and implementation of nudgetech to ensure transparency and communication about the purpose behind the nudge.
Sample Vendors
BetterUp; Digital Attitude; Enboarder; Microsoft; Perceptyx (Cultivate); Workday (Peakon)
Gartner Recommended Reading
Composable HR Application Frameworks
Analysis By: Sam Grinter, Helen Poitevin
Benefit Rating: High
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Definition:
A composable HR application framework (CHAF) is an architectural approach that enables quick and effective deployment of new employee experiences based on underlying packaged business capabilities (PBCs). This includes application PBCs dedicated to specific talent or administrative HR domains, data PBCs and analytics PBCs, which include reusable analytical and AI models. These components are surfaced through an application composition platform to deliver composed application experiences.
Why This Is Important
CHAFs are expected to ultimately become the dominant approach for deploying and managing human capital management (HCM) solutions. CHAFs deliver compelling advantages over HCM suites. Although CHAFs are still nascent and emerging, application leaders should begin shaping their HR technology strategies around this concept.
Business Impact
CHAFs deliver several advantages:
- User experience orchestration: CHAFs support the improved ability to deliver employee and manager experiences across multiple underlying systems or PBCs.
- Personalization: Shared data and AI assets allow for the delivery of personalized experiences across HR process domains. This includes decision support.
- Extensibility/customization: This architectural approach enables flexibility, lower reliance on single vendors for their innovation roadmap and a higher degree of responsiveness to business transformation.
Drivers
Cloud HCM suites are the result of a more than 20-year journey by vendors from discrete functionality to a broad consolidation of HR functionality in a single integrated suite. Functional gaps, however, remain a challenge.
- Cloud HCM suites generally do not support the most cutting-edge HR processes or universal coverage of local compliance needs.
- The current model of cloud HCM suites cannot respond quickly to new challenges by turning on/off functionality provided by third-party vendors because connections between applications are often delivered via custom integrations. This is a recent lesson learned for many HR and IT leaders owing to the challenges faced during the COVID-19 pandemic, followed by the talent crunch and the subsequent return to the office, and for organizations and employees impacted by the Russian invasion of Ukraine and the Israel-Palestinian conflict.
- During the next 10 years, Gartner expects cloud HCM suites to evolve or be replaced by CHAFs.
Obstacles
- Cloud HCM suites are still the dominant deployment approach, and the initial intention at deployment was that they would serve a client for 10 to 20 years or more. The appetite to rip out and replace a cloud HCM suite with a CHAF is low for most organizations. Instead, these organizations are much more inclined to augment the capabilities of the cloud HCM suite vendor through investment in multiple third-party vendors and integrate these systems as best they can. A key barrier influencing the potential adoption of CHAF is how long the “augmented cloud HCM suite” approach satisfies clients. Most organizations yet to deploy a cloud HCM suite would still likely be better served by a cloud HCM suite, rather than a CHAF, due to the comparable difference in market maturity.
- Current investments in CHAFs rely heavily on IT investments in application composition technologies and multiexperience development platforms, including low-code technologies. There aren’t many off-the-shelf-ready CHAF products on the market today, hence the need to self-build should an organization want to deploy a CHAF over the short term.
- There is some degree of misunderstanding surrounding the definition of CHAF in the market. Occasionally, a client will identify as running CHAF when in fact they are running an augmented cloud HCM suite, or even an on-premises human resources information system loosely integrated with a cloud talent management suite and a portal.
User Recommendations
- Evaluate the composability of their application portfolios by rating how easily they can compose employee experiences, utilizing functional components from different applications along with shared data and AI models.
- Include composability readiness when evaluating cloud HCM suite offerings, if such a solution is not yet in place. Do not consider an HR application framework as an alternative to a cloud HCM suite.
- Introduce application composition platforms into your HCM technology roadmap to increase composability and improve the employee experience. This applies to mature and well-executed cloud HCM suite deployments.
- Partner with other IT leaders to build out components of the composable HR application framework by prioritizing a set of employee experiences and leveraging general-purpose application and data composition solutions. Start this work on your own while vendor offerings mature.
Gartner Recommended Reading
Workstyle Analytics
Analysis By: Matt Cain, Helen Poitevin, Tori Paulman
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Workstyle analytics (WSA) is a discipline that synthesizes IT, HR and business data about how employees work to understand and optimize the complex relationship between technology investments, employee experience and business outcomes.
Why This Is Important
IT is increasingly being asked to calculate the ROI for new technologies. But, IT does not have access to business or workforce outcomes to derive meaningful conclusions. IT, therefore, must focus on identifying and tracking IT applications and devices signals and collaborating with business and HR leaders to develop data-driven ROI and return-on-employee insights.
Business Impact
The 2024 Gartner CIO and Technology Leader Priorities Survey finds that the most critical strategic priorities over the next two years are business outcomes, workforce strategies and technology investments. Each element has its own data stack, but data synthesis between stacks is rarely an organizational strength. To drive better business and workforce insights and outcomes, organizations must aggregate and analyze data to understand how technology investments impact employees and business results.
Drivers
- The introduction of transformative technologies such as generative AI (GenAI) is raising existential questions about technology’s effect on workers and business outcomes.
- The IT organization is being asked to assess the ROI and value of these new technologies, but they only have ready access to data about how the technology is being used and how employees feel about it.
- To get a true picture of ROI and workforce impact, the IT data must be blended with business outcome metrics, like KPIs, and workforce data, like engagement and intent to stay.
- Technologies like GenAI will drive substantial changes to employee skills, roles and business processes. Greater data-driven intelligence is needed to successfully navigate the change as we move into the era of everyday AI.
- There is an increasingly rich set of signals and data coming from the three relevant data stacks (IT, business results and employee sentiment), including data to help create operational efficiencies from process- and task-mining tools.
Obstacles
- WSA is a collection of insights from a multitude of sources and different vendors. Some environments may not be easily assembled to produce consistent insights.
- IT (and specifically the digital workplace) often lacks the charter, skills, tools or funding to identify, aggregate, analyze and deliver meaningful insights from technology usage and performance data.
- There is little organizational recognition of the need to synthesize data across IT, HR and business domains, and data owners may be reluctant to broadly share data.
- Many investments lack clear objectives, outcomes and key results, which makes it difficult to quantify the impact.
- Organizations may lack the data science skills and roles to do an effective job of synthesizing data across data domains.
- Irresponsible or nontransparent employee data collection can create distrust and, in some regions, industries or worker segments, is prohibited or regulated.
- Employee privacy compliance may hinder the usefulness of insights if data must be anonymized.
- There is limited to no integration or data sharing between tools used to collect and analyze data related to digital employee experience (DEX), employee sentiment, task or process efficiency, objectives and key results, or other measures of business outcomes.
- The link between technology investments and employee and business outcomes is generally limited to correlation. Few organizations have the resources to perform multivariate analysis or linear regression to prove causation.
- WSA may not be relevant to frontline workers who sometimes create few digital signals for analysis.
User Recommendations
- Assess digital workplace maturity to avoid prematurely investing in WSA, which aligns with Level 3 or above.
- Establish an analytics or data science role (it could be a partial full-time equivalent) that gathers, synthesizes and publicizes digital workplace analytics, including employee sentiment, to drive employee technology enablement and improve the DEX.
- Divide analytics into categorized scores for adoption, assistance, technology performance and experience.
- Address privacy concerns proactively through a policy of radical transparency by sharing what data is gathered, for what purpose, who has access to it and how long it is kept.
- Produce a dashboard that sums up data for the previous month (and quarter and year when appropriate). Hold a monthly meeting to report and strategize on the results; open the meeting to anyone in the company to drive interdisciplinary participation.
- Include a variety of sources, including DEX tools, digital adoption platforms, employee surveys, voice-of-the-employee or listening tools, applications and business-intelligence tools running against activity logs.
- Collaborate with the business unit and HR partners interested in the impact of technology investments on the employee experience and business outcomes.
- Explore task- and process-mining tools to add additional data about how work gets done.
- Start with a minimum viable product, and expect technology shifts to reset strategy.
Gartner Recommended Reading
AI-Enabled Skills Management
Analysis By: Helen Poitevin
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Definition:
AI-enabled skills management is a foundational capability within talent and day-to-day work contexts that applies natural language processing, knowledge graphs and other AI techniques to build a dynamic representation of skills data. It is used to automate skills inference for people, content, work tasks, career paths and jobs.
Why This Is Important
More dynamic skills data transforms how organizations manage their workforce and support talent processes. Improved and automated skills detection and assessment allow for significantly greater organizational agility. In times of uncertainty, or when competition is fierce, organizations with better skills data can adapt more quickly and be more dynamic in acquiring and deploying talent.
Business Impact
Properly architected and deployed AI-enabled skills management improves the following:
- Productivity and capacity utilization when used to prioritize and distribute work assignments.
- Quality of hire when used to match internal and external candidates to open positions.
- Strategy execution when used to support continuous strategic workforce planning activities.
- Impact of reskilling and upskilling initiatives when used to automate learning and career path recommendations.
Drivers
Although adoption progress can be hampered by data complexity and technical constraints, interest in AI-enabled skills management has increased due to the following:
- Skills-based talent management approaches: HR leaders are increasingly interested in applying skills data across talent processes. This includes using skills data to automatically tag and recommend learning content; more easily find and connect with experts across teams; dynamically propose career development options; and match talent to job opportunities in talent acquisition systems and internal talent marketplaces.
- Pace of change: High pace of change linked to technology and other disruptions drives the need for more visibility into skills in order to plan and respond effectively. Skills footprints are changing in many professions. AI and automation are further leading to uncertainty about the type of skills that will be needed in the future.
- Tight labor markets: Organizations can benefit from tapping into AI-enabled and skills-based labor market insights in support of recruiting and workforce planning efforts.
- Technology improvements: Graph techniques and technologies have improved in terms of availability and maturity. Increased capabilities in natural language processing techniques help to automatically detect and infer skills data in unstructured text, in multiple languages. In addition, more vendors are now employing a variety of AI-enabled skills management in their platforms.
Obstacles
- Insufficient access to data about what work is done to better codify skills. Data from HR systems is often low in detail. Internal data is often difficult to access and is inconsistent.
- Insufficient progress in natural language processing techniques for skills and proficiency inferences across highly varied datasets.
- Lack of processing power needed for the most detailed and complete skills ontologies.
- Variability of standards and language to describe the same skill across contexts.
- Difficulty in visualizing and analyzing skills data.
- Too many skills approaches from too many providers and difficulties in sharing data and models across systems.
- Lack of readiness to think of jobs and the organization of work in terms of skills.
- Fear that the skills inferences show inaccurate information, and desire to more tightly control the validation and assessment of skills.
- Attachment to existing, less-detailed competency frameworks.
User Recommendations
- Identify data sources that can be used to enhance skills detection and inference.
- Plan how to leverage AI to identify, infer and track skills instead of relying on competency libraries or time-consuming manual skills updates. Plan how employees can interact with skills data to improve quality over time.
- Leverage labor market analytics with in-depth skills analysis and forecasts to enhance and improve your strategic workforce planning efforts. Use this data to benchmark internal skills forecasts against broader market trends.
- Check your current vendor roadmaps for inclusion of skills data in their platforms across HR domains, and their use of AI. Evaluate their ability to both send and receive data from other systems.
- Evaluate providers’ ability to show users where skills inferences come from, and how skills data factors into various matching and recommendation algorithms.
Sample Vendors
Eightfold AI; Gloat; JANZZ.technology; Lightcast; Phenom; Reejig; retrain.ai; SkyHive; TechWolf; Visier
Gartner Recommended Reading
Coaching/Mentoring Applications
Analysis By: Laura Gardiner
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Definition:
Coaching and mentoring tools maximize the effectiveness of corporate mentoring and coaching programs. These applications are used by both sides of the coaching/mentoring arrangement (coaches/learners, mentors/mentees) and HR. In addition to digital coaching/mentoring applications that connect users for live sessions, innovations in generative artificial intelligence (GenAI) have introduced AI coaching tools that mimic elements of the leadership coaching experience, without a human coach.
Why This Is Important
Coaching and mentoring applications enable the optimal program matchup, execution of sessions, auxiliary resources and reporting/analytics. As use cases quickly expand to include diversity, equity and inclusion (DEI), as well as reverse mentoring from early career employees and entire teams or new hires, so does the importance of scalability and the need for technology support. The emergence of AI-enabled coaching raises questions about new opportunities for cost-effective coaching for all employees.
Business Impact
Coaching/mentoring opportunities can transform workforce development and engagement. Hence, these programs are likely to expand to become a critical part of the development cycle for the entire workforce. Coaching and mentoring technology to match and track pairings enables scaled program operations, but, due to cost constraints, it may be limited to target populations — e.g., management and high potential (HIPO). AI coaches may alleviate some limitations, but are unlikely to replace human coaches.
Drivers
- Coaching and mentoring have been applied in the corporate world for years, but with a narrow focus (typically covering senior executives) and through internal resources, small service firms or individuals. Demand to expand programs across the workforce has increased in recent years.
- Workforce expectations are evolving to see coaching and mentoring as part of the career journey at all levels, and potentially being more impactful in early careers.
- Coaching/mentoring apps increase access to a diverse network of coaches and mentors. This alleviates the burden on underrepresented talent to be the sole providers of identity or affinity-based mentoring and coaching.
- HR faces challenges to scale coaching and mentoring programs. These include the increasing volume of potential matchups and sessions, integration into other talent management activities, and making the business case for program expansion.
- HR needs better metrics that display the connection between the time invested, the cost, and the impact of these coaching/mentoring arrangements and the overall program, without compromising confidentiality of coaching/mentoring sessions.
- In addition to technology to manage programs, organizations might require a pool of external coaches or mentors. Some require services to help with the optimal program design and launch.
- AI coaches can augment human coaches to increase their capacity, or they can expand coaching programs to audiences previously excluded.
- Organizations with specific language and geographic requirements can also use these applications to connect their own pool of approved external coaches and mentors to opportunities.
Obstacles
- Most vendors cover one of the two areas, so the category effectively contains two subsegments — one for mentoring and one for coaching applications.
- In the coaching subsegment, most vendors do not meet all needs: technology to deliver programs; access to networks of certified coaches; services for design and management of programs; and AI to assist, augment or replace human coaches.
- Only a few vendors have achieved scale, particularly in the mentoring subarea.
- For coaching applications, the quality of coaching services and the effectiveness of the corresponding vetting process of external coaches by the vendor are paramount.
- Gaps in regional or language coverage remain, particularly for coaching that requires a pool of coaches per location/language.
- Reporting and analytics lack depth, which hinders HR’s ability to make decisions to continue, suspend or expand coaching/mentoring programs.
User Recommendations
- Establish the scalability of the coaching/mentoring vendor solution as an important assessment criterion, because programs can quickly expand, particularly in large organizations.
- Check overlaps with incumbent adjacent vendors. Some talent management applications, and more recently human capital management (HCM) suites, include mentoring features.
- Check that vendors have a multistage vetting process for hiring coaches, as well as a quality control process (including learner ratings) to use for coach retention decisions.
- Evaluate content and advice offered by the vendor to drive quick program adoption, particularly for programs related to a specific topic (e.g., DEI mentoring and group coaching). This includes evaluation of AI-enabled recommendations and insights.
- Determine the role an AI coaching tool could fulfill in the wider development portfolio.
- Assess the multilingual and regional capabilities of candidate vendors, to enable rapid expansion of your programs across multiple locations.
Sample Vendors
BetterUp; Chronus; CoachHub; EZRA; Landit; MentorcliQ; PushFar; Rocky Robots; Sounding Board; Torch Leadership Labs
Gartner Recommended Reading
VR and AR in Corporate Learning
Analysis By: Travis Wickesberg
Benefit Rating: Moderate
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Definition:
Virtual reality (VR) and augmented reality (AR) are different yet related technologies. VR provides a computer-generated 3D environment (supporting both computer graphics and 360-degree video) that surrounds a user and responds to an individual’s actions in a natural way, either through immersive head-mounted displays (HMDs), computing devices or room-based systems. AR technologies use HMDs to overlay digital information on the physical world to enhance it and guide action.
Why This Is Important
Initial VR and AR use cases have shown promising results that showcase higher levels of engagement and accelerated time to proficiency. This, in turn, translates to reduced training costs and long-term behavior changes that drive business outcomes.
Business Impact
In corporate learning, VR and AR can help:
- Enable learners to practice complex tasks or experience realistic scenarios in a controlled and safe environment that’s free of consequence.
- Drive higher retention and engagement of relevant information through interactive advanced graphical visualization and simulations.
- Reduce training time and cost by quickly replicating an environment without the need to rebuild physical props, recreate high-risk situations or schedule equipment downtime.
Drivers
- Readily available off-the-shelf content makes implementation faster and more cost-effective
- Organizations need alternatives to face-to-face (F2F) training on use cases that are too dangerous or expensive to replicate in an F2F environment.
- VR is well-aligned to support complex scenarios in the military, healthcare (surgeries), aviation (flight simulations) and various safety training environments.
- Owing to market maturity, organizations are adopting VR and AR for sales training, customer service, product and a variety of soft-skills topics.
Obstacles
- VR and AR tools are still in the early adoption phase in corporate learning.
- Hardware, software and infrastructure investments to support AR/VR can be barriers for organizations on a strict or small budget.
- Learning and development buyer adoption has been slow due to a combination of technical maturity and change management challenges.
- Only a few vendors have invested in simple learning management system (LMS) integrations, but complete integration is still a challenge and often requires you to administer multiple platforms.
User Recommendations
- Evaluate VR and AR as emerging, effective and often less risky options to replace F2F training in selective circumstances where F2F training is resource-intensive, but not providing the training presents increased risk.
- Identify and leverage areas where vendors and training service providers have prebuilt, out-of-the-box, high-quality content that meets your specific requirements.
- Start by running experiments and pilots based on performance challenges., Determine whether the product, platform and hardware are a good fit that provide additional value beyond traditional corporate training methods.
- Evaluate compatibility with existing learning and talent technologies to ensure integration and continuity across platforms.
Sample Vendors
Bodyswap; immerse.io; Mursion; PIXO VR; PTC; Roundtable Learning; Saritasa; Strivr; Talespin; VR Vision
Gartner Recommended Reading
At the Peak
Global Employer of Record Solutions
Analysis By: Nicole Paripurana
Benefit Rating: High
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Definition:
Global employer of record (EoR) solutions help organizations hire and manage workers in a new geography without having to set up a legal entity in each country. The EoR provider is the full legal employer of these workers and assumes all employer-related responsibilities and tasks on behalf of their customers.
Why This Is Important
Global EoR solutions are attractive when organizations seek to quickly expand in new markets without having to incur the upfront cost of setting up a legal entity and hiring people to perform HR and other administrative activities. More recently, the talent shortage around specific skills (such as technology, programming and graphic design) and broader adoption of remote work enabled organizations to expand their talent reach beyond their existing or planned operations and associated legal entities.
Business Impact
The most important business impacts are legal compliance and speed to hire. Organizations don’t need to spend time understanding or applying various country-specific legal frameworks that only apply to a few workers at a time. Their expanded outreach for talent also helps to quickly acquire critical skills and enhance diversity (often at a lower cost too). Organizations can offer more flexible work locations for employees when the nature of the job allows, which increases employee engagement and retention.
Drivers
- Scarce skills give rise to borderless talent: Recruiters increasingly embrace borderless recruiting in response to the changes in the global market for talent and continuing skills shortages in the market. Many executives express interest in working with a borderless workforce, and several leaders have actually implemented such a model to some extent. In recent years, organizations have continued the practice of borderless hiring, especially in sectors and jobs of high talent scarcity (such as software developers in technology).
- Volatile economic and political conditions call for agile business expansion strategies: Many enterprises now review their strategy at least twice per annum, with some adjusting their strategy at least twice yearly. New market expansion or operating model changes are often on the table, but these activities need to be executed quickly while optimizing the associated cost base. Alternatively, the decision to withdraw from or downsize presence in specific markets is often unavoidable but can incur significant costs due to existing legal and administrative setup when an entity is owned versus the accommodation offered by an EoR.
- Expansion in use cases: Existing global organizations are challenged by the payroll or global mobility aspects of a global workforce and the various compliance requirements. Some EoR vendors have expanded their offerings for services that touch payroll solutions, benefit partnerships, global mobility and guidance in the transition to a client-owned entity from the EoR or a professional employer organization (PEO) vendor.
Obstacles
- Organizations often fail to create the same conditions for employee experience, inclusion and belonging as with those workers they directly employ. This impacts the engagement and retention of EoR-managed workers.
- While all providers manage legal compliance and administrative aspects, not all global EoRs support extended capabilities in recruiting, onboarding, compensation or other aspects of employment that may be needed by clients.
- EOR providers often depend on in-country partners for some services, such as payroll. When in-country partners are not seamlessly integrated, the result is suboptimal service and employee experience.
- Unless using APIs to common HR technology, global EoR providers utilize their own proprietary platforms to manage their service scope, while the employer organization would use other systems for performance management, talent management and daily work. This variance can create risk of inconsistent HR support and gaps in related workforce and talent insights.
User Recommendations
- Define workforce needs in connection with business requirements: The scarcity of talent to fulfill specific roles is often underestimated during planning. Tight connection between business planning and workforce planning is critical to spot cases where a global EoR needs to be considered early.
- Consider the plan and subsequent steps in the evolution of global EoR usage to gradual establishment of an owned entity: Very often, utilizing the services of a global EoR provider is a temporary step, and considerable cost, until a specific employee threshold (typically around 25 to 40+ employees) is reached to set up a local legal entity. Include important checkpoints in the roadmap and contractual process with the providers (such as minimum scope or contract early-termination clauses). Have a clear indication of the use case(s) needed for EoR and risk mitigation of entity ownership to justify the cost of the EoR supplier.
- Define service priorities and map them to the operational model of each provider: Having clarity in what scope of services matters most (such as payroll compliance versus recruitment) will make the global EoR selection and ongoing partnership more harmonious and operate with less friction.
Sample Vendors
Atlas Technology Solutions; Deel; Globalization Partners; Neeyamo; Omnipresent Group; Oyster HR; Papaya Global; Remote Technology; Safeguard Global; Velocity Global
Gartner Recommended Reading
Responsible AI
Analysis By: Svetlana Sicular, Philip Walsh
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Responsible artificial intelligence (RAI) is an umbrella term for aspects of making appropriate business and ethical choices when adopting AI. These include business and societal value, risk, trust, transparency, fairness, bias mitigation, explainability, sustainability, accountability, safety, privacy, and regulatory compliance. RAI encompasses organizational responsibilities and practices that ensure positive, accountable and ethical AI development and operation.
Why This Is Important
Early exploitation of generative AI resulted in the re-emergence of RAI as a key AI topic. As AI amplifies at a huge scale, with both good and bad outcomes, RAI enables the right outcomes by ensuring business value while mitigating risks. RAI can employ a set of tools and approaches, including industry-specific methods, adopted by vendors and enterprises. More jurisdictions introduce new regulations that drive and challenge organizations to adopt RAI practices.
Business Impact
RAI assumes accountability for AI development and use at the individual, organizational and societal levels. If AI governance is practiced by designated groups, RAI extends its reach to all stakeholders involved in the AI process. RAI helps achieve fairness, even though biases are often baked into the data; gain trust, although transparency and explainability methods are evolving; and ensure regulatory compliance, despite the AI’s probabilistic nature.
Drivers
RAI helps AI participants develop, implement, utilize and address the various drivers they face. With further AI adoption, the RAI drivers are becoming more important and are better understood by vendors, buyers, society and legislators:
- The adoption of GenAI raises new concerns, such as hallucinations, leaked sensitive data, copyright issues and reputational risks that bring new actors in RAI (for example, in security, legal and procurement).
- Leading vendors are offering indemnification of their GenAI offerings, making customers more confident as part of their RAI approaches: although a good step, these are still incomplete.
- The organizational driver of RAI assumes the need to strike a balance between the business value and associated risks within regulatory, business and ethical boundaries. This includes considerations such as reskilling employees to adapt to AI technologies and safeguarding intellectual property.
- The societal driver includes resolving AI safety for societal well-being versus limiting human freedoms. Existing and pending legal guidelines and regulations, such as the EU’s Artificial Intelligence Act, make RAI a necessity.
- The customer/citizen driver is based on fairness and ethics and requires reconciling privacy with convenience. Customers/citizens may be willing to share their data in exchange for certain benefits.
- AI affects all ways of life and touches all societal strata; hence, the RAI challenges are multifaceted and cannot be easily generalized. New problems will continue to arise with rapidly evolving technologies and their uses.
Obstacles
- Poorly defined accountability for RAI makes it look good on paper but renders it ineffective in reality.
- Organizations lack awareness of AI’s unintended consequences. Many turn to RAI only after they experience AI’s negative effects, whereas prevention is simpler.
- Most AI regulations are still in draft. AI products’ adoption of regulations for privacy and intellectual property makes it challenging for organizations to ensure compliance and avoid all possible liability risks.
- Rapidly evolving AI technologies, including tools for explainability, bias detection, privacy protection and some regulatory compliance, lull organizations into a false sense of responsibility, while mere technology is not enough. A disciplined AI ethics and governance approach is necessary, in addition to technology.
- Measuring success is difficult. Creating RAI principles and operationalizing them without regularly measuring the progress makes it hard to sustain RAI practices.
User Recommendations
- Publicize consistent approaches across all RAI focus areas. The most typical areas of RAI in the enterprise are fairness, bias mitigation, ethics, risk management, security, privacy, reliability, sustainability and regulatory compliance.
- Designate a champion for each use case who will be accountable for the responsible development and use of AI.
- Define the AI life cycle framework. Address RAI in all phases of this cycle. Address hard trade-off questions.
- Provide RAI training to personnel. Include AI literacy and critical thinking as part of the training.
- Operationalize RAI principles. Ensure diversity of participants and enable them to easily voice AI concerns.
- Participate in industry or societal AI groups. Learn best practices and contribute your own because everybody will benefit from this exchange. Ensure that policies account for the needs of any internal or external stakeholders.
Sample Vendors
Adobe; Arthur; Fiddler AI; Google; H2O.ai; IBM; Microsoft; Responsible AI Institute; SolasAI; TruEra
Gartner Recommended Reading
Labor Market Intelligence
Analysis By: Emi Chiba, Rania Stewart
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Labor market intelligence (LMI) solutions offer external data on labor markets to help organizations conduct strategic workforce planning and make informed planning, hiring and skilling decisions. Using job postings, publicly available résumés or talent profiles, census data, or dynamic skills taxonomies, they provide insight on labor trends (compensation, unemployment, available workforce size/composition) and skills trends (supply, demand, availability by location, receding/emerging skills).
Why This Is Important
In volatile market conditions, organizations rely on strategic workforce planning to align strategy and workforce initiatives. This alignment ensures the right mix of talent, technologies and employment models. Labor market insights are essential to making strategic, data-informed workforce planning decisions at scale. They provide a more complete picture of the relative gaps between talent supply and demand, both inside and outside the organization.
Business Impact
LMI platforms help organizations make smarter build, buy or borrow talent investments and enable resiliency and adaptability. They are especially valuable for organizations introducing new products or expanding to new markets, as they make workforce considerations (e.g., skills availability) an active part of strategy planning. Without LMI, these considerations appear only in the later execution phase of a change initiative. Thus, LMI helps mitigate sizable, late-stage risk that can thwart success.
Drivers
- A need for skills insight both inside and outside the organization: As the new currency for talent, skills are central to many talent strategies. Without a complete view of skills availability in the greater market, or an understanding of how skills are viewed in the market, organizations cannot adequately plan their own talent strategies.
- A need for diverse data sources: LMI platforms use a variety of external data sources to provide insights. These insights include skills availability based on location or industry, pay and titles associated with certain skills, competitor or industry trends for skills supply, and competition or difficulty in recruiting for certain skills.
- Strategic workforce planning initiatives: Labor market insights are vital to strategic workforce planning. Separate strategic workforce planning solutions are a growing market, but many organizations supplement their existing operational workforce planning solutions with LMI platforms.
- Competitive labor markets that require changing hiring patterns: To support hiring in competitive or tight labor markets, many organizations are moving from location-based hiring to skills-based hiring, regardless of geography.
- Tailored, relevant insights: Improvements in machine learning and natural language processing enable these platforms to take large amounts of unstructured data and automatically detect and contextualize skills across geographies, providing relevant insights unavailable from labor market or economic data alone.
Obstacles
- Limited utility in isolation: While labor market insights offer a more complete picture of skills and labor availability, they alone do not solve workforce planning questions. They are part of a greater set of tools, but provide limited utility on their own. Furthermore, not all organizations need strategic workforce planning and may instead focus on operational workforce planning or workforce optimization. For those organizations, insights into the broader labor market may not be necessary.
- Lack of relevant data: Not all jobs or industries may be represented in publicly available data. Therefore, they can be difficult to track.
- Limited support for languages or APIs for diverse data sources: Some LMI vendors lack APIs to transfer and combine internal data with external insights. Language support outside of English may be limited, or the language used to describe skills may vary widely.
- Inconsistent AI adoption for dynamic skills: Not all providers use AI for dynamic and emerging skills sensing, instead relying on fixed skills taxonomies.
User Recommendations
- Identify the required scope and type of workforce planning activities by engaging with business leaders and executives to review their priorities. Decide whether strategic workforce planning and, thus, labor market insights are necessary.
- Focus on data sources, skills ontologies, languages and data privacy when evaluating labor market insight platforms.
- Pair labor market analytics with in-depth skills analysis and forecasts to enhance and improve strategic workforce planning efforts. Use this data to benchmark internal skills forecasts against broader market trends.
Sample Vendors
Coresignal; Draup; Horsefly; LaborIQ; Lightcast; LinkedIn; Magnit; People Data Labs; SkyHive; TalentNeuron
Gartner Recommended Reading
AI in HR
Analysis By: Helen Poitevin
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Artificial intelligence applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions. In the HR domain, AI is used across HR process domains to provide predictive insights, make recommendations, infer information, generate text or provide a conversational user interface.
Why This Is Important
AI in HR is becoming ubiquitous as a capability built into or bolted onto a broad range of HR applications. A new generation of tools are AI-native, meaning they are architected from the data and AI models first, before specific workflow and user experiences are designed. More established providers have also introduced AI capabilities into their roadmaps. These advances transform user expectations when interacting with HR solutions. HR solutions without AI will increasingly lag the market.
Business Impact
AI is used in HR to automate and improve operations, transform user experience and provide insights to support decision making. AI capabilities often come embedded in HR applications and are used for:
- Matching talent supply and demand or predicting recruitment success.
- Recommending learning content, mentors, career paths and adaptive learning paths.
- Answering queries, providing guidance and executing processes in a conversational interface.
- Inferring skills through AI-enabled skills management.
Drivers
- HR leaders are under pressure to deliver greater impact on the organization, improved employee experience and operational excellence, but they must also meet demands for greater flexibility, personalized support and equity. They turn to AI-driven functionality as the best technology to make this possible at scale.
- The explosion of interest in ChatGPT and generative AI has accelerated investment in AI across HR processes. New expectations emerge around conversational interfaces, access to summarized insights or information, and the ability to automatically generate text.
- AI has become increasingly pervasive across many applications, and HR technology providers have been investing in applying AI within their applications for at least the past five years. These embedded capabilities are not always highlighted in product demos or marketing collateral.
- Individual models or applications can appear quite small on their own — for example, an algorithm to predict what topic an employee is asking about in a conversational interface. The cumulative effect, however, when multiple models are combined, is much greater. The result leads to changes in how HR works with data, delivers services and can support the business with advanced insights.
- AI-native applications — those architected from their inception to leverage AI techniques at the core of their applications — have emerged in domains such as recruiting, internal talent marketplaces and AI-enabled skills management.
- A small number of HR teams have hired AI application designers who build AI models and the applications that use those models to drive user experience.
- AI can increase efficiency and transform the employee experience.
Obstacles
- Overly inflated and unrealistic expectations about AI can lead to hasty, low-value investments.
- An evolving regulatory landscape makes investment more complex, especially in domains such as recruiting, where concerns remain high around fairness in candidate selection and hiring.
- Bias is an unavoidable challenge. It is important to mitigate the harm caused by bias. AI explainability is especially critical when AI is applied to decisions directly affecting a person’s livelihood or position within their peer group. Even in cases where AI is “only” inferring skills without further judging fit or aptitude, bias can be introduced simply by the fact that skills will be easier to infer for those with a greater web presence or digital footprint.
- Skills within HR to effectively adopt, manage, track and monitor the use of AI within HR are often lacking.
- The introduction of AI changes how work tasks are accomplished and cumulatively leads to shifts in roles and the skills required to perform these tasks.
User Recommendations
- Learn how AI-native applications change expectations for application life cycles and user experience by exploring, testing and investing in existing AI-native applications in the talent acquisition, skills and talent marketplace domains.
- Build data and AI literacy skills in HR to evaluate AI use cases and vendor solutions.
- Focus AI investments on automating tasks, transforming experience or providing insights for employees, managers, executives and HR.
- Leverage embedded AI where possible. Evaluate the data, assumptions, model maintenance steps and adaptability of solutions that have AI.
- Check for bias based on the data, assumptions, models and degree of adaptability. Test outcomes during and after implementation to ensure fairness of outcomes. Monitor perception and impact.
- Review vendors’ documentation of ethical usage guidelines, enforcement methods, known vulnerabilities and weaknesses, and disclosed harmful behavior and misuse scenarios.
Gartner Recommended Reading
Internal Talent Marketplaces
Analysis By: Emi Chiba
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Internal talent marketplaces (ITMs) are intelligent platforms that match workers to work and to development opportunities without human resources involvement. They provide personalized recommendations aligned with workers’ unique skills and experiences. Opportunities include gigs, time-boxed projects, stretch assignments, mentoring or full-time roles. ITMs also offer career exploration and skills gap information to inform workers’ development activities.
Why This Is Important
The ever-changing demand for new skills requires talent agility. Combined with worker demands for increased mobility and development opportunities, this has driven the adoption of ITMs. ITMs provide valuable insight into skills present in the organization and provide workers with equitable insight into available growth opportunities. They are key to enabling adaptability, resilience and experiential learning.
Business Impact
- Improve internal mobility by providing workers curated recommendations for new skills and positions.
- Understand workforces through a new lens focused on the skills needed, rather than the role.
- Gather skills data and support talent through experiential learning and hands-on opportunities.
- Encourage and track employee development and collaboration with a focus on skills.
- Address rapidly changing business priorities by redeploying or reskilling employees to improve organizational sustainability.
Drivers
- Skills-based talent management: The desire for deeper insight into more varied skills via AI has led many organizations to adopt ITMs as an entry point into artificial intelligence (AI)-enabled skills management. This is because it provides a tangible outcome of what to do with skills data.
- Business agility and composability: Agile and composable organizations require more flexible deployment of workers across projects, products and other initiatives. Composable businesses are architected for real-time adaptability and resilience in the face of uncertainty. They need people with learning agility to adapt to changing skills demands. They also need to be able to align a highly networked workforce to the work that needs to get done in a dynamic way.
- Talent visibility: HR and other organizational leaders benefit from the data and insights from ITMs to support workforce planning and other talent processes. Organizations’ team, project and product leaders benefit from more flexible staffing and improved visibility into talent.
- Worker demand for growth opportunities: Deployed correctly, ITMs provide employees and contingent workers with better visibility into work and growth opportunities. They can stretch and build their skills and experiences to grow their portfolio of work and careers.
- Technology availability: The market for ITMs has proliferated, and includes human capital management (HCM) suite providers, talent acquisition vendors, learning platforms and specialist point solutions. Maturity in applying AI to detect, infer and map relationships among skills has increased, as has the use of AI techniques to automatically match talent to work opportunities.
Obstacles
Organizational challenges impeding adoption include:
- Lack of cultural readiness for more dynamic and adaptive organizational models and project- or gig-based work.
- Talent hoarding. Managers may discourage team members from seeking outside opportunities as they only see talent engaging in work for other teams, and fear not having enough talent to get assigned work done on their own team.
- Lack of psychological safety. Workers may not be confident enough to bid on projects or gigs for fear that they will not be selected. Uncertainty can also exist regarding how performance on projects will affect annual performance reviews.
Data-related challenges include:
- Access to data regarding knowledge skills and worker experiences.
- Use of organization-specific and more-granular skills to enable better matching.
- Difficulties in balancing privacy and the need for a significant amount of talent data to enable a better user experience (UX) through more-relevant matching.
User Recommendations
- Pilot ITMs within business units that use adaptive or agile organization models, or work with progressive talent management leaders ready to deliver agile skills development.
- Invest in design thinking, work design and workplace ethnography. Allowing workers to bid for projects and gigs represents a significant change to management practices.
- Inventory current skills ecosystem and data sources to decide what may feed into matches and recommendations in the ITM prior to vendor evaluation.
- Evaluate vendors by assessing UX, ability to incorporate diverse sources of data and skills ontologies. When evaluating vendors with similar capabilities, prioritize UX, because user adoption is critical to the adoption and success of an ITM.
- Market the ITM, as it gets adopted in your organization, as an essential, growth-focused part of your differentiated employer brand.
Sample Vendors
365Talents; Degreed; Eightfold AI; Fuel50; Gloat; Oracle; ProFinda; SAP; Workday
Gartner Recommended Reading
Digitally Enabled DEI
Analysis By: CV Viverito
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Digitally enabled diversity, equity and inclusion (DEI) includes a range of technology solutions to enhance DEI in organizations. These solutions aim to maximize data-driven decision making and specific value drivers — such as transparency, accountability and efficiency — across people, processes and daily work.
Why This Is Important
Business leaders face intense external pressure to meet DEI and environmental, social and governance (ESG) measures, while continuous changes in the political, social and work environment create new challenges to successfully deliver DEI. Digitally enabled DEI — or, when technologies are positioned as integral to DEI program design — serves to increase the scalability and effectiveness of DEI programs, drive data-informed insights, and embed DEI technology capabilities across functions.
Business Impact
Human-centric work — combining flexibility, collaboration and empathy — is significantly improving employee performance and intent to stay. Sustainable business that incorporates ESG factors into decision making is fast becoming an integral part of business strategy, affecting all organizational functions. DEI can be seen as the amplifier to deliver both human-centric work design and sustainable business.
Drivers
Digitally enabled DEI addresses several key business needs and requirements, including:
- Obtain a real-time, granular view of diversity in its many forms across the employee base to compare with DEI strategy goals.
- Provide a continuous feedback loop with employees to understand how individuals perceive the state of inclusion and its embodiment in the organizational culture.
- Scale employee resource groups (ERGs) to cover multiple aspects of DEI and geographies (making them “glocal”).
- Reduce bias across talent sourcing and selection activities through the use of artificial intelligence (AI).
- Increase the funnel of potential candidates through outreach in diverse talent pools.
- Tailor candidate experience to the needs of various aspects of diversity across candidates.
- Proactively address (or at least detect and fix) pay and workplace inequity.
- Comply with the growing body of pay transparency legislation in the U.S., EU and elsewhere.
- Develop inclusive leaders across all levels of the organization.
- Ensure that the digital workplace is accessible to people requiring accommodations for various types of disability (including loss of sight, hearing or motor skills).
- Enhance emotional proximity for those employees that have weak physical proximity due to remote/hybrid work.
- Empower increased workforce digital dexterity for people with a diverse set of skills, experiences and backgrounds.
Obstacles
- Narrow DEI focus: Organizations often focus on a single aspect of diversity (gender or ethnicity) or on compliance needs as opposed to broader business impacts.
- Technology as an afterthought: Many DEI programs compromise technology use by leveraging it at later stages instead of during early design. The 2023 Gartner DEI Functional Benchmarking Survey shows that only 8% of respondents will prioritize DEI technologies in the next 12 to 18 months.
- Disconnects between global and local ownership: Conflicts in prioritization between the global team and local operations lead to local purchases that need consolidation at a later stage.
- Lack of integration between technology tools: Narrow vendor vision or insufficient ability to execute an integration roadmap.
- Hyperfocus on HR/DEI as the primary user: Technology purchases need to balance functionalities and user experience targeting the HR/DEI function with functionalities that are mostly performed by managers and employees.
User Recommendations
- Increase the scalability and effectiveness of DEI programs by establishing technologies as an integral part of the initial design. Don’t wait until these programs hit the scalability wall.
- Map out all potential owners of DEI programs. These include DEI leaders, HR leaders, operational leaders and especially the CEO.
- Align technologies not just to HR and DEI professionals, but also to operational leaders, line managers and employees.
- Team up with local HR and operations leaders too. Progressive organizations increasingly blend local variations in their DEI programs to fit local objectives and culture (i.e., make them “glocal”).
- Familiarize DEI leaders with the benefits of available technologies. Very often, leaders have limited understanding of what is possible.
- Pursue opportunities for DEI technology consolidation and alignment to employee experience by continuously evaluating large HCM vendor offerings and integrating among HCM suites and dedicated DEI applications.
Sample Vendors
ADP; Diversio; Espresa, Oracle; SAP; SeekOut; Syndio; UKG; Visier; Workday
Gartner Recommended Reading
Frontline Worker EXTech
Analysis By: Ranadip Chandra, Sam Grinter
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Frontline worker EXTech is an approach that delivers distinctive employee experiences to frontline workers by unifying a collection of applications that promote staff engagement and a sense of community. Applications typically include administrative support, recognition, well-being, internal communications and personal development processes. These apps are primarily designed for use via smartphones and tablets with digital signage sometimes complementing the approach.
Why This Is Important
Frontline workers far outnumber desk-based workers in sectors like retail, healthcare, manufacturing and logistics. Chief supply chain officers and retailers note that their frontline workers are dissatisfied with the flexibility they receive compared to their peers at other organizations or hybrid workers. Yet, technology initiatives have focused solely on desk-based workers. Addressing this gap is a significant opportunity for improving the experience of this underserved group.
Business Impact
- Frontline jobs face extreme stress and burnout. Improving daily business application experiences could reduce stress and improve retention. Employee experience technology (EXTech) allows for proactive monitoring of frontline worker fatigue or burnout, adjusting approaches as needed.
- Quick access to training or procedures to repair broken equipment is beneficial for logistics and manufacturing industries.
- Frontline worker EXTech could consolidate over 10 different daily applications, replacing inefficient homegrown portals.
Drivers
Drivers in individual application categories used by frontline workers are:
- Radical flexibility: Frontline workers desire flexibility in scheduling, which continues to drive development of core workforce management apps. Examples include self-scheduling that allows employees to select shifts based on their availability and personal preferences, accrual of additional paid time off (PTO) beyond standard PTO and using them when needed.
- Benefits and recognition platforms: These applications enable frontline workers to receive rewards that are easily redeemable while someone is on the road and facilitate immediate acknowledgments of co-workers across teams.
- Well-being/experience for frontline workers: These applications track health through wearables or offer stress reduction for employees dealing with a high volume of customers directly. Based on employer selection, these apps also allow retirement planning assistance, suggest healthy food options in the company cafeterias, or enable gym facilities on-site.
- Employee communication applications (ECA): These applications include internal communication channels for organizational communications and are often better designed to meet the needs of frontline workers than mainstream consumer-based communication platforms. These channels also integrate with schedules and include the ability to create communities based on common interests or work.
- Frontline worker safety: Early examples of this kind include Vinnie, a software AI system developed by Newmetrix, that photographs construction sites to spot hazardous conditions and predict accidents before they happen; and TopMax, heads-up displays that provide fighter pilots with crucial information directly in their line of sight.
- Learning platforms for frontline workers: Retail and hospitality frontline workers are increasingly leveraging dedicated frontline worker learning solutions to access job-specific learning bytes.
Obstacles
- Similar to workforce management technology, the frontline worker experience initiative lacks ownership at executive levels. Some initial projects led by application leaders are maturing from the early adoption stage, but most are stand-alone deployments by department heads.
- For safety, some industries prohibit frontline workers from using mobile applications throughout their shifts.
- Solutions need to prove the nontracking of time during off-shift hours to build greater trust.
- Providing a compelling frontline worker experience involves combining applications from different markets and/or often vertical-specific products, making it difficult to navigate the market or recommend best practices for deploying and managing the portfolio.
- Many industry-specific applications are crucial for the frontline worker in the short term, but adoption and usage decrease over time due to lack of improvements.
- Following a surge of investments due to COVID-19, change fatigue has set in, and additional investments in this space have slowed down.
User Recommendations
- Evaluate solutions based on their ability to work uninterruptedly for hours in the background and provide significant value in little interaction time. Many frontline workers would only access the application between time-consuming tasks.
- Set a criterion that any solution that “needs more than two minutes to complete a moderate complexity use case” or “takes more than five clicks/form parameters” should not be considered.
- Analyze the employee engagement metrics filtered to identify the specific figure for frontline workers. Establish frontline worker engagement as a key metric for the success of the employee experience strategy of the organization.
- Balance the content of frontline EXTech applications between critical tasks and communications with well-being and diversity, equity and inclusion (DEI) announcements.
- Explore how frontline worker EXTech can coexist with applications that meet more stringent needs, such as clinical collaboration or purpose-built tools for certain operational work.
Sample Vendors
Blink; DaysToHappy; Flip; Headspace; Perkbox; Site Diary; SparkPlug; Workstream; Wyzetalk; YOOBIC
Gartner Recommended Reading
Generative AI in HR
Analysis By: Eser Rizaoglu, Helen Poitevin, Hiten Sheth, Jackie Watrous
Benefit Rating: Transformational
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Generative AI (GenAI) technologies can generate new derived versions of content, strategies, designs and methods by learning from large repositories of original source content. GenAI has profound business impacts, including on content discovery, creation, authenticity and regulations; automation of human work; and customer and employee experiences.
Why This Is Important
GenAI in HR is currently being explored and is in the early stages of utilization. The majority of HR leaders will gain access to GenAI through their HR software providers, which have been heavily focused on integrating capabilities into their solutions. One of the key innovations of GenAI is in natural language generation, which can be used in text generation and for conversational capabilities. GenAI in HR may be able to drive greater efficiency and effectiveness of the HR function.
Business Impact
GenAI in HR will primarily focus on text generation. This includes job description creation, recruiting marketing and communication, learning and onboarding content, HR policies, performance reviews and coaching recommendations, and “humanlike” HR virtual assistants through conversational AI. A few HR subfunctions, such as learning and development (L&D), will extend its use to speech, image or video.
Drivers
- The hype around GenAI has remained strong. HR leaders are showing interest in experimenting with these tools and see it as a way to drive increased HR productivity and improved employee experience.
- HR technology vendors, across the board, have started to roll out initial use cases in their products or will most likely release solutions this year. This allows HR functions to easily consume GenAI capabilities embedded in HR applications.
- Many IT functions have started to explore enterprisewide GenAI solutions resulting in additional opportunities for HR to gain access to GenAI capabilities, which are not restricted only to what HR technology vendors are rolling out.
- Heightened expectations for HR team productivity gains, through GenAI, is driving interest in GenAI for HR service delivery, recruitment and L&D.
- HR processes and information can be confusing for employees, managers and new HR team members. GenAI is poised to help make that information more accessible and to decrease the amount of time needed for nonspecialists to complete HR tasks. GenAI will be a key part of HRIT strategies targeting high value and satisfaction ratings while significantly decreasing the amount of time spent on HR related tasks.
Obstacles
- GenAI best practice and solution maturity is still developing, leading to a potential lower near-term ROI.
- Currently, there is a siloed approach to GenAI utilization with use cases being task- and platform-specific. Without greater HR data standardization and orchestration of multiple GenAI tools, the true potential of GenAI will remain limited.
- There may be a gap in perceived expectations of use cases and value drivers for applying GenAI versus actual reality leading to deflated motivations.
- Implementation challenges include data accuracy, reliability and transparency, risk mitigation uncertainties (especially employee data privacy) and employee resistance. Until challenges are alleviated, greater utilization may be hindered.
- GenAI can produce inaccuracies and hallucinations and will therefore require customization, governance and, often, human supervision.
- Fragmented and specialized technology offerings can lead to a combination of tools resulting in the complexity of the technology stack.
User Recommendations
- Develop a stance on how GenAI can positively impact your HR function and determine initial use cases where you can rely on purchased capabilities or partner with vendors.
- Introduce an HR AI product lead to navigate HR’s approach to GenAI utilization.
- Plan for GenAI’s impact on HR staff and work to upskill or reskill staff to be able to effectively manage and utilize GenAI solutions.
- Quantify the pros and cons of GenAI in improving HR service delivery, employee and candidate experience. Improve existing processes, which could benefit from enhanced text generation, while meeting HR’s risk appetite by having a human-in-the-loop approach.
- Prioritize vendors who promote the responsible deployment of models by publishing usage guidelines, enforcing those guidelines, documenting known vulnerabilities and weaknesses, and disclosing harmful behavior and misuse scenarios.
- Work with legal and compliance to understand existing and emerging AI regulations, and mitigate any risks which may potentially arise.
Gartner Recommended Reading
PaaS in HR Technology
Analysis By: Chris Pang
Benefit Rating: Moderate
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Platform as a service (PaaS) provides extensibility options that complement a cloud human capital management (HCM) solution. PaaS allows customers to gain functionality that may not be in the vendor’s core offering or roadmap, such as integration to third-party products and HR-centric process “journeys.”
Why This Is Important
Despite HR technology vendor efforts to cater to all the needs of their target segments, customers rarely use or want everything all from one vendor. Extensibility provides customers with bidirectional integration from the HR technology to third-party and in-house built systems. It allows customers to gain specific functionality that would be unlikely to be developed by their HCM vendor.
Business Impact
PaaS in HCM helps end customers get a more “complete” solution that integrates with other applications and/or delivers extended functionality. For closing functionality gaps, PaaS can be more economical than buying and integrating a third-party point solution.
Drivers
- AI services via PaaS are increasingly being made available for customers, allowing them to leverage AI in customer-specific use cases.
- Customers receive the benefits of “customization” without the implementation cost and complexity, and fewer maintenance and challenges of the past.
- PaaS provides support for organization-specific process “journeys.”
- Implementation providers are making available packaged solutions that use PaaS.
- HCM maturity and marketing by HR technology providers are evolving.
- Marketplaces that offer PaaS extensions are being established.
Obstacles
- Regulations surrounding AI, such as the EU AI Act and organization readiness to leverage AI in their HCM processes.There is limited end-customer understanding and internal resources to manage HCM PaaS capabilities.
- Limited availability of skilled implementation/consulting resources in HCM PaaS.
- Cost of using and managing extensibility options; most PaaS offerings are sold as an ongoing subscription in addition to the base cost of the HR tech offering.
- Limited freedom. HCM PaaS does not offer unlimited freedom to customers. Most vendors have established “guardrails” and limitations on what is possible, which will curtail some complex use cases.
User Recommendations
- Check that HCM PaaS adheres to any organizational data processing and security requirements, especially when leveraging AI services that consume, process and return actions/data.
- Use HCM PaaS to support processes that are not possible by configuring an underlying software-as-a-service (SaaS) application and/or when using a third-party point solution is not ideal due to cost, integration complexity or functional fit.
- Budget for preimplementation and ongoing regression testing and evolution. Budget for ongoing training and certification of internal resources, because it is an evolving technology.
- Determine if using HCM PaaS will be temporary (one to three years) or more permanent (more than three years) by comparing your needs with the vendor’s roadmap. Review each use case annually to determine if it should be maintained, evolved or retired in the next cycle.
- When using an implementation partner, ensure there is sufficient proficiency of staff trained in HCM PaaS available to you. Most vendors have separate certifications for PaaS offerings.
Sample Vendors
Cegid; Cornerstone OnDemand; Darwinbox; Dayforce (formerly Ceridian); Oracle; SAP; ServiceNow; UKG; Workday
Gartner Recommended Reading
Sliding into the Trough
Flexible Earned Wage Access
Analysis By: Ron Hanscome
Benefit Rating: Moderate
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Definition:
Flexible earned wage access (FEWA) enables workers to receive a portion of their earned income in advance of their employer’s actual payday. Providers market this capability to employers, who deploy it as an optional benefit. The cost the employer subsidizes (usually a monthly, per-employee subscription) can vary by customer. The employer’s ability to specify available wage ratios varies by provider, as does the range of disbursement options (such as pay card, bank account or digital wallet).
Why This Is Important
Continued disruption by global events, inflation and economic uncertainty continue to affect employees on a worldwide basis. Thus, many hourly paid workers continue to have little financial reserve and cope with unforeseen expenses by resorting to various expensive, short-term borrowing options. From its origins in 2016, FEWA continues to manifest as a cost-effective alternative that helps employees meet urgent unexpected needs and, thus, reduce their financial stress.
Business Impact
Early adopters primarily saw FEWA as a benefit, expecting workers to view it as evidence of care. Avoiding usurious payday loans may improve productivity by reducing “presenteeism” and sharpening focus on job tasks. Retention may also improve, as workers are less likely to leave for another employer without FEWA. Those deploying FEWA as part of a broader financial well-being solution may also improve usage of savings plans due to employees building their future discretionary income base.
Drivers
- FEWA is being driven by three main provider categories:
- Point solutions serve as an overlay to customers’ existing payroll and WFM solutions, and facilitate the FEWA transaction from request to disbursement. Many of these partner with existing payroll solution providers and are part of their “marketplace” of ancillary offerings that leverage standard APIs for integration.
- Some North American midmarket HCM suites and mainstream payroll providers are beginning to deliver FEWA as an optional product feature.
- Providers of financial well-being solutions are providing FEWA as the “borrowing” component of a holistic educational and coaching approach.
- Most vendors initially targeted U.S. employers with predominantly hourly workers, but these offerings have been shown to also pertain to salaried staff dealing with unplanned expenditures.
- Emerging market activity and customer adoption is taking place in several countries in EMEA, with the U.K. leading the way. FEWA may be especially attractive in the European market, as monthly pay cycles are more common in this region. This increases the employee appetite for more flexibility in access to their earned wages.
- Early interest and adoption is manifesting in APAC, led by Australia and New Zealand (ANZ). Some variations of FEWA coexist with digital wallets in emerging APAC countries, such as Indonesia and Malaysia.
- Some providers are reducing the cost to the employee by specifying that a certain number of FEWA transactions monthly or weekly are included. Others reduce cost to both employer and employees by taking a percentage of the paycard transaction fees charged to merchants.
- FEWA provides more saving and spending options to unbanked employees, usually via an integrated pay card.
- FEWA is being used in some industries to enable a smoother transition during current economic instability.
- Some point solutions are expanding beyond FEWA to support other areas, such as payment reimbursement or retirement savings contributions.
Obstacles
- The current solution provider landscape is extremely country-specific, and adoption is predominantly in the U.S. and U.K. markets, along with some uptake in ANZ. The timeline for a robust, competitive market in other countries remains several years out. This limits applicability if an employer has hourly workers in multiple countries and wants to make this capability available to all.
- Legal and compliance requirements for FEWA vary substantially by country, and even by state or jurisdiction in complex countries such as the U.S.
- Maturity of FEWA varies, particularly where it has been recently deployed as part of an HCM suite.
- Administering FEWA often requires some form of reconciliation to the standard pay run, which may result in additional administrative burden on payroll staff. This depends on the comprehensiveness of the solution, but at worst may cause staff resistance or require additional resources.
User Recommendations
- Work with HR and operational leaders to assess the potential positive impacts of FEWA implementation on employee experience, productivity and retention.
- Determine which of the three vendor approaches is most suitable for your organization, as one size doesn’t fit all.
- Scrutinize the relative maturity of solutions, especially when considering the North American midmarket HCM suites where this capability is either being planned or is in early adoption.
- Vet how each provider ensures ongoing compliance with sometimes volatile country wage laws (and, in the U.S., state and local regulatory requirements as well).
- Confirm that the provider’s approach matches your internal legal risk tolerance and requirements.
- Evaluate the impact on current time approval processes, which could shift from pay-period-based approvals to a daily frequency.
- Determine how FEWA will affect existing payroll processes and staffing requirements.
Sample Vendors
ADP; DailyPay; Dayforce; FinFit; FlexWage; Hastee; Instant; One Finance; Payactiv; Wagestream
HR Virtual Assistants
Analysis By: Ranadip Chandra, Eser Rizaoglu
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
HR virtual assistants (HRVAs) are software applications that work with human voice (or text) commands to assist employees in completing HR tasks or requests. This multimodal interaction between human and machine takes place via smartphone, tablet, computer or specific device. HRVAs can be either integrated with other human capital management (HCM) applications or natively provided.
Why This Is Important
HRVAs help employees access information and complete transactions via conversational queries. This results in enhanced HR process efficiency and employee experience by increasing the value of interactions. With ChatGPT setting the benchmark for engagement through a conversational interface, HR departments are looking at HRVAs to provide a similar employee experience. An employee-facing chat interface is likely to be a top-priority use case for generative AI (GenAI) application in HR over the next six to 12 months.
Business Impact
Virtual assistants (VAs) are becoming an important layer in many HR functions — particularly gaining maturity in recruiting, HR service management, enrollment for benefits, onboarding and HR functional insights (e.g., talent analytics insights). VAs with HR-specific capabilities can initiate communication with the workforce in response to event-triggered conditions. This facilitates a timely response to changing business conditions by removing the need for employees to initiate transactions, thus improving the employee experience.
Drivers
- Most HRVA providers are incorporating large language models (LLMs) — ranging from proprietary LLMs to open-source options such as LLaMA, BLOOM, BERT — as a core part of their technology offerings, accelerating the availability of GenAI capabilities in HRVAs.
- Some HRVAs in the broader market are progressively refining their sophistication. These VA platforms can now initiate transactions of moderate complexity in administrative HR and other modules in response to natural language user commands. This includes delivering more sophisticated and easy-to-consume talent analytics insights.
- Many cloud-based HCM suites and extended ecosystem vendors are investing in HRVAs. Many of these VAs can also be deployed as a wrapper, or as the underlying model in a “bot within a bot” framework, thus opening up possibilities to coexist with other VAs deployed in the organization.
- HR tasks can be time-consuming and confusing for employees, managers and new HR team members. HRVAs are increasingly used to significantly reduce the time it takes to get support, information and complete HR tasks. HRVAs are effective tools for promoting the value of HR and talent processes in supporting individual, team and organizational success.
- A growing number of VAs use robotic process automation (RPA), no-code or low-code integrations to enable full workflow processing through the VA interface. This provides more value than simpler FAQ-focused chatbots.
Obstacles
- HRVAs currently lag behind the overall market in supporting advanced use cases. The benchmark set by consumer applications or publicly available chatbots (e.g., ChatGPT, Gemini) is difficult to match in the near term. The inherent complexity of HR tasks makes them more challenging than tasks in other domains.
- High perceived value by users is achievable only with HRVAs that require significant implementation effort, which HR technology leaders often struggle to execute.
- Hundreds of vendors populate the chatbot and VA market landscape with many smaller, niche vendors often overpromising capabilities. This can leave users frustrated if the VA cannot understand the user’s intent behind the interaction.
- Successful deployment of VAs requires equal maturity in three areas — natural language query processing, an HR knowledge base that connects commands to relevant information, and the ability to integrate sometimes with outdated systems with limited integration capabilities (e.g., payroll and timekeeping). Many solutions only address the first area and lack training in domain-specific semantics and integration elements.
- Many HR leaders face the decision of whether to acquire an HR-specific solution or join enterprisewide virtual assistant initiatives, typically led by IT departments. Enterprisewide solutions may be marketed as easily accommodating HR use cases; however, these solutions may have less HR domain expertise.
User Recommendations
- Decide which VA approach is suitable for your organization — a centralized, platform-based approach of deploying an enterprisewide conversational AI, or an HCM-contextualized VA approach. A centralized, platform-based approach provides consistency in chatbot implementation, operations and conversational management. HCM-contextualized VAs will offer a deeper understanding of HR processes.
- Determine the HRVA use cases (e.g., shift reminder, learning content suggestion) that will result in maximum benefit to employees.
- Explore different technologies leading the transformation of next-generation conversational AI. Balance the hype surrounding GenAI with ethical considerations such as responsible AI models.
- Assess HRVA solutions on their ability to self-train based on the historical records of employee transactions. Additionally, any solution’s ability to resolve a query based on variations of phrases, misspellings and keywords of the same question should be a “litmus test” for its effectiveness.
Sample Vendors
Acuvate; Amelia; The Bot Platform; Espressive; Leena AI; Moveworks; Simpplr (Socrates.ai)
Gartner Recommended Reading
Learning Experience Platforms
Analysis By: Jeff Freyermuth
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
A learning experience platform (LXP) is the front-end layer that typically sits on top of a learning management system (LMS). LXPs are used to enhance an individual learner’s interactions and engagement via greater personalization, content curation and expanded breadth of content.
Why This Is Important
Organizations prefer open-learning platforms that offer more consumer-grade personalization. LMSs have traditionally focused capabilities on the scheduling, registering and tracking of a learner’s activities. LXPs go a step further by personalizing learning experiences through the use of AI, hoping to deliver more relevant learning paths, channels, content and collections based on learner preferences, interests, profiles, skills, system activity and collaborative interactions.
Business Impact
LXPs enable companies to improve the learners’ experience and engagement by providing them with a more open, interactive and effective way to learn. Organizations that offer a wide range of learning and development opportunities often correlate with increased engagement, which then translates to less voluntary attrition and greater productivity.
Drivers
- Organizations cannot afford to treat all learners the same. When a workforce is spread across geographies and consists of various cultures, jobs and preferences, the one-size-fits-all approach is less than optimal.
- Employees are demanding a wider range of resources and upskilling options beyond their traditional, role-focused development. Multidimensional learning and growth opportunities are increasingly seen as must-haves, thus becoming key elements of an organization’s employer brand, and critical for talent mobility and retaining talent.
- Learners do not want to be limited only to accessing content that complies with industry standards, such as Sharable Content Object Reference Model. They demand access to a wider range of publicly available (and subscription-based) content sources.
Obstacles
- Over time, new technologies such as generative AI may reduce organizational dependencies or needs for an extra LXP layer.
- The provider landscape for LXPs is in transition. Recent (and potential future) consolidation in the market adds a layer of uncertainty and risk, coupled with a greater number of LMS vendors building in LXP functionalities.
- Organizations have historically provided employees with a structured, often compliance-centric and focused set of learning resources. Shifting to a more open and personalized approach requires an increased investment in strategy and change management.
- Return on investment (and value-add) can be challenging to quantify. Driving stronger learner adoption and engagement can be tracked and measured; however, early adopters have often forgotten to align with business initiatives or specific business outcomes.
User Recommendations
- Ensure strategy alignment and conduct proper change management communications and make those investments prior to LXP deployment.
- Evaluate the strengths, weaknesses and roadmaps of the various providers to determine their fit for the organization’s culture and context. Consider their compatibility with existing HCM, workplace solutions, and LMS technologies to ensure integration and continuity across platforms.
- Pilot the LXP for a period of three to six months for a small targeted population of learners. Focus the initial pilot on learners who can quickly and clearly see the benefit to themselves, their teams and the overall organization.
- Continuously measure and align the LXP with learning and business outcomes across learning stakeholders and teams to support further optimization, integration and a broader enterprise rollout.
Sample Vendors
360Learning; Absorb Software; Cornerstone OnDemand; Degreed; Disprz; Fuse Universal; Microsoft; Skillsoft
Gartner Recommended Reading
Employee Productivity Monitoring
Analysis By: Helen Poitevin
Benefit Rating: Moderate
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Employee productivity monitoring technologies use automated data collection and analytics to report on employees’ activities, time spent, work locations and work patterns. They contribute to measuring and improving workforce productivity, well-being and experience but may cause workforce anxiety.
Why This Is Important
Client interest in employee productivity monitoring increased during the COVID-19 pandemic and the subsequent return to the workplace. It can provide insights into when employees are working, what work is being done and how much time is spent on different activities. Interest is likely to persist as organizations pursue productivity gains through generative AI (GenAI) deployments, despite negative press for such data collection and analysis.
Business Impact
Used well, insights from employee productivity monitoring can support efforts to improve organizational effectiveness, employee experience, worker well-being and working-time compliance. Used poorly, monitoring tools can present substantial employer brand risks, high costs due to erosion of trust, reduced employee engagement from worker backlash and a toxic work culture. They are best suited for roles where a large number of employees have similar and relatively routine tasks and activities.
Drivers
- Driven by hype, vendor marketing and early time-savings research, many executive leaders expect productivity improvements through the use of GenAI. Employee productivity monitoring tools may be considered to analyze impact. However, process or task mining tools are much more likely to be effective in evaluating the productivity impact of GenAI.
- Flexible work arrangements have driven many organizations around the globe to operate with a significant portion of their workforce working remotely. This has increased interest in monitoring employee activities and analyzing work patterns. Prior to the pandemic, these technologies were not widely considered or adopted.
- In some cases, interest in employee productivity monitoring is driven by a desire to ensure employee compliance and to limit the amount of time spent on nonwork activities. CEOs or other business leaders may be the ones demanding these solutions, and it is more frequent in organizations with low-trust and risk-averse cultures.
- Talent analytics teams and HR leaders have shown increased interest in analyzing data from outside of HR systems to understand worker behaviors. The intent, in general, is to increase worker well-being, identify burnout rates or improve employee experience. In some cases, the intent is to establish control over working-time compliance, especially in jobs or roles eligible for overtime pay.
- To limit survey fatigue, IT and HR are interested in combining behavioral data with existing sentiment data to understand and improve employee experience.
- Some investments in employee productivity monitoring aim to improve how teams work by identifying workload imbalances within teams and focusing on workforce optimization. They may also seek to improve productivity by advising leaders on how to communicate or how to better organize work.
Obstacles
- Many monitoring tools offer only basic categorization of activities (including applications, browser URLs or other activities) as either work-related or non-work-related. However, this data can be of limited value.
- Organizations must weigh the potential organizational and cultural costs of monitoring employees against the value of the data collected and the insights generated. Employees may feel a lack of trust or perceive that time and volume of activities matter more than outcomes or impact.
- Labor regulations in a number of countries will limit the ability to use these tools or require negotiations with workers’ councils to put them in place.
- Public opinion around privacy, in addition to privacy regulations, means that investments in employee productivity monitoring must be done with great care. Reasons for monitoring must be clearly aligned with employees’ performance development, as well as specific controls put in place to limit access to insights generated from the data and to define their purpose.
User Recommendations
- Inform your investment decisions through careful inquiry about data sources, user interface design, reporting features and the intended value you aim to get from the collected data.
- Ensure that the technology is implemented ethically by testing it against a key set of human-centric design principles. Mitigate risks through a carefully planned communication strategy and collaboration with legal and HR peers.
- Use a checklist to ensure that the purpose and scope of data collection are in line with its intended use and support employees in performing their best work.
- Minimize legal risk by complying with applicable privacy and personally identifiable information regulations and laws.
- Mitigate risk by ensuring that managers are fully trained on appropriate usage before they get access.
- Consider process and task mining technologies as an alternative because they focus on how work gets done rather than solely on how employees spend their time.
Sample Vendors
ActiveOps; ActivTrak; enaible; Insightful; Prodoscore; Sapience Analytics; Teramind; Time Doctor; WorkMeter
Gartner Recommended Reading
Digital Adoption Platforms
Analysis By: Melissa Hilbert, Stephen Emmott, Tim Faith, Maria Marino
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
A digital adoption platform (DAP) overlays internal applications (such as CRM, HR and ERP) and customer-facing applications and portals with in-application and cross-application guided learning, simulations and analytics. A DAP drives user adoption, proficiency and engagement. It supports digital transformation by streamlining and accelerating how employees or customers learn and engage with technologies. DAPs’ analytics drive actionable insights to improve employee or customer experience.
Why This Is Important
DAPs improve user productivity and efficiency by reducing digital friction and increasing user engagement and employee retention. Key employee use cases appear in sales, HR, ERP and digital workplace, but this technology applies to all functional areas in an organization. For external use cases, in which your company sells software, embedding a DAP can improve customer experience and loyalty. Use cases include onboarding, technology adoption and use, change management and process efficiency.
Business Impact
DAPs provide high ROI for organizations looking to improve the adoption of applications for employees and customer experience. The benefits of DAPs include:
- Reducing employee onboarding and training costs
- Speeding new-hire time to productivity
- Eliminating change-management-related training
- Reducing support tickets
- Improving user engagement, proficiency and efficiency
- Requiring minimal setup and low administrative overhead
- Enabling continuous improvement through usage analytics and insights
- Improving CSAT scores
Drivers
DAPs are relevant for any organization in any vertical. The most prominent application employee use cases to date include where sales force automation (SFA), HR, ERP, procurement or digital workplace solutions are used.
- The solutions in the market include platform capabilities, such as the use of partner ecosystems for prebuilt starter content.
- The need for cross-application guidance and analytics is critical to digital transformation and improved employee experience.
- DAPs address the need for multiple device types such as mobile, desktop, hybrid, web and on-premises hosted applications.
- DAPs are relevant for organizations selling software where user adoption and usage are critical to customer value realization, renewals and expansion.
- DAPs drive actionable insights to improve the user experience and maximize ROI from application investments.
Obstacles
- In addition to losing some analytics, on-premises applications behind firewalls are more difficult for vendors to connect to and will be more costly to deploy. Mobile application support is weak from many vendors; some do not offer it at all.
- Language translation for content varies greatly among vendors.
- Some vendors utilize a per-application (including varying pricing for application complexity) and per-user pricing model, which can increase costs when deploying at the functional or enterprise level.
- Some vendors do not support cross-application guidance and analytics.
- Governance and new DAP roles for guidance, content creation and maintenance are required. A partnership between product, customer success and IT teams is also essential.
- DAP tools need to evolve to help organizations make the connection between application use patterns and actual business outcomes such as the ROI of applications.
- GenAI-based co-pilots offer an alternative to DAPs, changing the way users interact with applications.
User Recommendations
Organizations should seek this technology if they are facing the following challenges:
- There is poor adoption of existing applications or high churn or growth of employees.
- Tasks are complex within and across applications.
- Tasks are performed infrequently but have a high organizational impact.
- Business processes are changing frequently and knowledge management is difficult.
- An application changes frequently; new feature adoption.
- Customers’ end users using your software have low engagement where adoption is closely correlated to renewal or growth.
To evaluate digital adoption platforms, organizations should:
- Create a rollout plan by functional area to incorporate DAPs by prioritizing high-impact applications such as CRM, ERP, human capital management or client-facing applications across the entire tech stack or product portfolio.
- Evaluate all applications for an employee’s work hub by documenting all applications an employee uses to get work done.
- Ensure analytics are deep at both a macro (aggregate) and a micro (workflow) level and can cross applications for a single workflow.
- Investigate multilanguage capabilities for application and content support.
- Design a governance plan by including new DAP roles or reallocating learning and development or subject matter expert roles to support content and a rollout across the organization.
Sample Vendors
Apty; myMeta; Pendo.io; SAP; SlideHub (KnowMore); Stonly; tts; Userlane; WalkMe; Whatfix
Gartner Recommended Reading
Hyperautomation in HR
Analysis By: Eser Rizaoglu
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Hyperautomation in HR involves orchestrated use of technologies, tools or platforms, such as AI (including generative AI [GenAI]), machine learning (ML), event-driven software architecture, robotic process automation (RPA), integration platform as a service (iPaaS), packaged software, and various types of decision, process and task automation tools. Hyperautomation in HR is a step toward autonomous HR that links a series of manual and semiautomated processes into a fully automated workflow.
Why This Is Important
Hyperautomation in HR provides opportunities to improve efficiency and reliability, particularly across transactions and workflows that are subject to manual data entry errors and delays, such as payroll, workforce management, recruitment and service operations. Hyperautomation in HR is among the top emerging technologies in the coming year and will be further fueled by recent hype around GenAI and AI.
Business Impact
Hyperautomation in HR positively impacts service delivery efficiency and effectiveness by reducing error rates and increasing overall staff availability. When used strategically, there is potential to accelerate organizational performance and reduce operational costs. Hyperautomation in HR is most effective when deployed across the full spectrum of business operation ecosystems. Positive effects on business operations include increased efficiency, scalability and reliability.
Drivers
- Hyperautomation in HR is likely to be of interest to high-transaction domains, as it has rapidly changed from being optional to vital because of the relentless demand to shift to digital business models.
- Hyperautomation in HR can boost organizational efficiency and effectiveness, which enables organizations to address operating in an environment with high volatility, uncertainty, complexity and ambiguity.
- Human capital management technology megavendors (such as Oracle and SAP) have invested in hyperautomation as stand-alone offerings. They have also built strong partnerships with major consultancies, systems integrators and business process outsourcing providers that can add hyperautomation use cases.
- Many service management systems (such as ServiceNow and Salesforce) have been focusing on improving workflow hyperautomation capabilities within their platforms to address employee and manager demands of HR services. A heavy focus this year will be the utilization of HR virtual assistants to achieve hyperautomation of HR service delivery.
- There is evidence of RPA in payroll driving some payroll processing alerts, along with data migration utilizing RPA to speed up data validation and implementation.
Obstacles
- HR has a fragmented HR technology stack with unstandardized processes and data. This in turn prevents scalable hyperautomation across the HR function and leads to siloed automation tools being used at task level resulting in reduced HR productivity benefits.
- Most vendors already use intelligent services, ML, adaptive intelligence and integrator connectors in a siloed fashion. However, they are yet to demonstrate combined hyperautomation broadly across the full suite.
- HR teams’ limited expertise with combined integration, business process management (BPM), RPA and other tools will be one of the biggest barriers to hyperautomation.
- Some hyperautomation projects are initiated to achieve a quick reduction in operational expenditure or staff. These initiatives often face challenges in scaling up and building a broad narrative of continuing business value.
User Recommendations
- Maximize hyperautomation success by architecting multiple concurrent HR technology initiatives. Aim for holistic mapping of related initiatives, rather than islands of administrative HR task automation.
- Focus on HR data consistency, system integrations and the utilization of an orchestration platform across the HR technology stack so that the right hyperautomation tools can be used in a coordinated manner not siloed to specific tasks.
- Continue to identify low-value work that can benefit from process optimization to reduce handoffs and labor costs. While repeatable and low-value workflows have already been automated, new opportunities lie in complex workforce management and employee experience technology that deliver maximum benefit to managers, employees and the organization.
- Engage experts from other parts of the organization, and build multidisciplinary fusion teams, to be able to use best-of-breed tools (such as BPM, PaaS and AI integration) to help guide and execute HR hyperautomation strategy successfully.
Sample Vendors
Automation Anywhere; Celonis; IBM; Microsoft; Pegasystems; SAP; SS&C Blue Prism; UiPath
Gartner Recommended Reading
Unified Multicountry Payroll
Analysis By: Ranadip Chandra
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Unified multicountry payroll is an approach to deploying an integrated solution by an organization that is present in a minimum of two countries to manage the data, processes and operations of the payroll function. The strategy can be to keep it “in house,” where software with sufficient localization is utilized for calculations, and/or “outsourced,” where a business process outsourcing (BPO) service provider or aggregator takes responsibility for processing payroll across multiple countries.
Why This Is Important
A unified multicountry payroll strategy will improve vendor management efforts, as it removes the complexities of managing multiple versions of SLA adherence, governance, maintenance and issue resolution processes. It will also pave the way for improved integration, consistency in compliance and fewer errors in calculation, and should better support organizations with (increasingly) global teams.
Business Impact
Unified multicountry payroll strategy removes process efficiency bottlenecks and gives an opportunity to add uniformity to the service metrics. A unified data reporting layer ensures granular visibility into payroll costs, such as overtime allocation and compliance breach settlements, thus helping with cost allocation and workforce planning. Unified multicountry payroll also enables improved business continuity planning through secondary processing units and infrastructure in “nearshore” countries managed by the same provider.
Drivers
- Maintaining integrations with multiple country payroll systems is cumbersome and makes real-time reporting and analysis difficult and slow. Most mainstream cloud HCM suites have partnership alliances with large multicountry payroll solutions with standardized integrations. There is strategic value in unified payroll, data reporting and analytics.
- Having unified payroll, data reporting and analytics can provide insights into labor costs, which can be used when planning to increase or decrease headcount based on the average cost of employment by country.
- Many multicountry payroll solutions have launched an updated centralized compliance library to help set up operations in a new country or keep pace with changes in the country specific regulations in the existing operations.
- Easier vendor maintenance through consolidation results in more opportunities for cost savings and improved service/product quality.
- The support of secondary data sites for failover processing, multiple delivery centers with similar setups and recovery time objective (RTO) metrics improves significantly under a unified operation versus a combination of disparate systems.
- Some regional providers have matured in their service and advanced technology capabilities, enabling organizations to create regional clusters to consolidate payroll operations and minimize the footprint.
- Multicountry payroll BPO vendors are replatforming their native payroll technology to improve integration, reporting and analytics, and to reduce dependency on legacy payroll engines.
Obstacles
- Mainstream HCM suites are adding new localizations at a relatively slow rate, thus making it difficult for organizations to unify HR administration with payroll using just suite functionality.
- Certain countries have made their data residency and reporting regulations difficult to comply with for global payroll outsourcing providers without overly relying on last mile subcontractors, posing a risk for data handling and possible breach.
- Government mandates on payment transactions with neobanks, digital wallets and alternative modes of payments remain a compliance challenge for multicountry payroll providers, despite increasing interest from end users.
- Countries affected by geopolitical events and government directives or embargoes are best managed by local solution providers, rather than a multicountry solution, to maintain business continuity.
- Geopolitical events and consequent risks have also forced many organizations to operate in a more decentralized, localized fashion that curtails a region’s or a country’s operation from being part of a unified strategy.
User Recommendations
- Develop a payroll transformation strategy that is suitable for your organization and prioritize execution based on your geographic footprint of providers, volume of workers and existing/planned HR application investments.
- Evaluate vendors on your roadmap to expand localization. If your organization has plans to expand its geographical footprint in the next five years, think ahead and partner with a payroll provider that will support this journey. Create a shortlist of suitable vendors that fit with your payroll strategy.
- Prioritize experience across your geographic footprint when selecting vendors, and demand transparency in terms of how payroll is delivered in each country.
- Consolidate global payroll solutions and data to improve reporting, auditing, and planning capabilities. This will enable easier vendor management and integration between payroll and other HR/finance applications, and improve internal payroll operations’ efficiency.
Sample Vendors
ADP; Alight; CloudPay; Deel; EY; Neeyamo; Papaya Global; Ramco Systems; SAP; SD Worx
Gartner Recommended Reading
Voice of the Employee
Analysis By: Laura Gardiner
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Voice of the employee (VoE) solutions collect, infer and analyze worker opinions and perceptions using surveys, feedback tools and other data sources. They deliver insights with actionable guidance to help improve employee engagement, experience, productivity and performance. When connected with human capital management and digital workplace technologies, VoE can become a key component of a firm’s sense and respond feedback loop.
Why This Is Important
As enterprises grapple with the ever-increasing pace of change, many still use an annual survey to primarily gather employee feedback. Additionally, some use periodic shorter surveys to capture more frequent changes in perception as their workforce reacts to organizational changes, work/life collisions and market events. VoE solutions include direct surveys and other feedback tools to better capture employee perceptions, feelings, opinions and ideas.
Business Impact
More robust collection and analysis of employee feedback with actionable guidance results in:
- Early problem spotting and quicker response due to faster data collection and direct delivery of insights to managers.
- Deeper feedback for managers on team perceptions and performance.
- Better data for longitudinal analysis.
- Improved employee engagement, learning, development and retention.
- Efficient idea management.
- Enhanced employee experience (EX), employment value proposition, worker performance and productivity.
Drivers
- Organizations are responding to the ongoing talent crunch and increased worker burnout/fatigue by trying to better understand employee perceptions, enable managers, drive digital workplace adoption and improve EX. Continuous listening remains a critical element to maintaining a connection to a distributed and less-connected workforce.
- Many HR clients use an annual survey as a feedback baseline but have also implemented some form of pulse measurement to increase the frequency of feedback and reduce lag between feedback, analysis and action.
- Organizations now want to go beyond merely gathering engagement-related data and expand use of VoE to communicate care, listen to a broader set of employee concerns, prioritize investments and quickly take action where necessary. They are also using VoE to measure the effectiveness of EX initiatives and to drive further iteration.
- Many organizations now perceive the more continuous approach to employee listening embodied by VoE to be more crucial than ever before.
- Some providers are responding to customer demand by blending VoE with other HR processes such as performance, recognition, learning and leadership actions. Others are exploring the intersection of VoE and EX insight management. Regardless, these combinations are attempting to build an ongoing sense and respond capability that crosses traditional application boundaries.
Obstacles
- No VoE solution fully supports all types of VoE listening (direct-survey-based, focus-group-based, indirect) and analytical methods, so integrating multiple providers will be a common outcome. It may be difficult for internal stakeholders to come to an agreement on which types of listening to use and how best to consider the outputs from each type.
- Organizations often struggle to take timely action in response to VoE results, commonly due to limited delivery of manager insights and difficulties identifying solutions to the challenges raised by VoE.
- Clients with long-standing, internally built surveys may find it hard to transition to VoE platforms that require adherence to the vendor’s methodologies.
- Providers acquiring VoE technology will often need two to three years to fully integrate it into their existing solution and organization. Clients implementing during that time frame will likely face integration challenges and disparate analytical tools.
User Recommendations
- Adjust VoE strategy to support faster decision timelines, including choice of metrics and measurement intervals.
- Determine what types of VoE listening are desired and how much weight will be given to each type.
- Define the degree to which managers will participate in VoE, and also assess enterprise readiness to tightly link VoE to other talent or work processes. Results of these two tasks will help drive tool selection.
- Select the right data sources, collection/measurement methods and technologies. Assess how well the provider applies techniques such as natural language processing and event-triggered listening.
- Scrutinize provider integration roadmaps if all or part of VoE functionality are the result of an acquisition in the past 18 months.
- Implement selected technologies on a pilot basis, then iterate based on early feedback from employees and managers.
- Make VoE initiatives actionable by equipping stakeholders to respond quickly to anonymized, aggregated insights coming from VoE data.
Sample Vendors
Culture Amp; Effectory; Microsoft (Viva); Perceptyx; Qualtrics; Quantum Workplace; UKG; Workday; WTW
Gartner Recommended Reading
EXTech Orchestrators and Overlays
Analysis By: Ron Hanscome, Harsh Kundulli
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Adolescent
Definition:
Employee experience technology (EXTech) orchestrators and overlays streamline, unify and orchestrate the primarily HR-related digital aspects of EX, generally across a fragmented applications landscape. Their scope spans worker interactions with HR, managers, teams and communities. They typically include low-code/no-code tools that enable trained end users to develop, track and iterate employee “journeys” to address critical “moments that matter” throughout the employment life cycle.
Why This Is Important
Most organizations realize that employee engagement and retention are primarily driven from an optimized EX. Unfortunately, EX is often hampered by the disparate user experiences employees encounter as they navigate their enterprise’s fragmented solution portfolio, even when a human capital management (HCM) suite is deployed. Presenting a more streamlined EX while effectively supporting ongoing hybrid and remote work has driven continued strong interest in EXTech orchestrators and overlays in 2024.
Business Impact
Worker motivation and engagement are key in work environments that demand ever-increasing levels of innovation, creativity and collaboration across teams. These solutions can help achieve EX outcomes such as employee productivity, motivation and engagement, thus aiding business performance and outcomes while supporting a pivot to a more agile culture. They can also help to improve the overall employment value proposition over time by better matching EX with the organization’s values, culture and objectives.
Drivers
Continued significant interest in EXTech orchestrators and overlays represents the strong majority of Gartner client inquiries on the overall EXTech topic, and come from three main client types:
- Organizations with a mix of on-premises core HR and payroll solutions, augmented by cloud talent management tools, often minimally integrated. These clients want to deliver a modern, consistent, improved UX overlay to give them time to swap out their on-premises components as time and resources permit.
- Those in the first seven years of their HCM suite journey who have realized that the suite UX won’t completely address their business requirements, leaving them with an “HCM suite plus” portfolio that still suffers from integration and disparate UX issues.
- Mature HCM suite users (more than eight years) who have come to grips with the limitations of their chosen suite, and have completed their initial augmentations. These are evaluating existing EXTech orchestrators and overlays versus using various IT tools (such as application composition technologies and multiexperience development platforms) to internally build their own solutions.
The following needs will also influence selection over the next two to three years, regardless of client type:
- Attracting and retaining staff for regions and industries grappling with uneven talent availability due to continued economic and geopolitical disruption
- Supporting a more agile organization and increasingly fluid work environments, including the splitting of jobs or roles into groupings of tasks requiring similar skill sets
- Improving EX for predominantly desk workers within the context of remote or hybrid environments by rendering HR processes and tasks within a new work hub, thus increasing the connection of employees to others
- Orchestrating employee journeys that may support subprocesses (including both work and life events) that cross application boundaries and owners, and require awareness and tracking of process steps
Obstacles
- There is still no comprehensive EX “platform” that meets the needs of all worker types and work patterns in the major industries across all employee size segments and geographies. Despite robust development (and marketing) efforts by many providers, one is not likely to emerge in the next four years (if ever), so enterprises will have to deploy multiple EX solutions to meet their requirements.
- EX usually has multiple stakeholders, with HR, corporate communications, digital workplace leaders and operations all wanting to drive (or at least influence) solution design and deployment. This can cause difficulties in gaining consensus on the issues and outcomes.
- The market has become increasingly crowded, with digital workplace, HCM suite, HR service management, frontline communications, modern intranet and specialist vendors all positioning their offerings as “employee experience platforms.” This has resulted in continued market confusion as to which solution is best fit for a given use case.
User Recommendations
- Use Gartner’s EX-Ready model to gain stakeholder agreement on EX priorities and to build a three-year investment roadmap.
- Assess each solution’s philosophy and design approach to determine its cultural and contextual fit, using Gartner’s digital workplace framework to identify its relationship to existing “work hubs.” Also evaluate your incumbent HCM suite and HR service management solution (if deployed), as they continue their EXTech investments.
- Conduct agile pilots. As EXTech solutions are emerging, features vary and relative impact differs across worker types and industries. Focus on employee value-add and time to benefit.
- Use leading design practices such as personas and employee journey mapping to ensure that the delivered solution actually improves interaction quality.
- Examine these tools for both shorter-term hybrid work environment needs and longer-term requirements to remove EX “dissatisfiers” and cultivate a deeper relationship between organization and employee.
Sample Vendors
Akumina; Applaud; Leena AI; LumApps; Microsoft; Nintex (Skuid); Oracle; SAP; ServiceNow; Workday
Gartner Recommended Reading
Next-Gen WFM
Analysis By: Sam Grinter, Ron Hanscome, Kelsie Marian, Ranadip Chandra, Josie Xing
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Workforce management (WFM) is a set of functions designed to help manage hourly paid workers. Core WFM functions include time and attendance, scheduling, absence and task management. An emerging and transformative capability is AI-enabled skills management in WFM. Next-gen WFM is the result of the following trends impacting the market: skills management, automation of the manager experience, employee experience, generative AI, new platforms and flexible workforce.
Why This Is Important
WFM is often considered as only a system of administration. However, WFM contains untapped value in supporting the business outcomes of worker effectiveness, the employee value proposition and cost optimization. Furthermore, leaders find it challenging that WFM has no clear business owner, leading to WFM applications being overlooked and underinvested. As such, WFM, and in particular next-gen WFM, presents a compelling opportunity to deliver business transformation.
Business Impact
Next-gen WFM can augment and transform hourly/frontline worker business processes. The benefits of next-gen WFM include more effective resource scheduling, improved employee experience, reduced manager time spend on administrative tasks, reduced training cost, and easier management of employees and contingent workers.
Drivers
- WFM is included as part of a wider digitalization/digital workplace/employee experience initiative.
- The ransomware attack and subsequent outage of the Kronos Private Cloud (KPC) product in December 2021, which affected 2,000 enterprise clients, is leading to a refresh of this product and similar-era products.
- WFM investment is included as part of wider human capital management or payroll transformation.
- WFM is adapted to be fit for purpose in the context of skills shortages for frontline workers.
Obstacles
- A lack of clear ownership often stalls investment, with ownership often somewhere between HR, IT, operations and even finance.
- Consideration of WFM as “merely” a system of administration rather than a system for transformation is a continued obstacle for further growth and realization of next-gen capabilities.
- The global market for WFM is fragmented, making it challenging to consolidate WFM applications with the wider HR application ecosystem. This can cause tension when HR technology leaders attempt to consolidate HR applications.
User Recommendations
- Assign a senior stakeholder for WFM applications to prioritize and oversee investment.
- Unlock new business value by evaluating the current use of qualification and certification as a capability of WFM and expanding to include other skills attributes relevant to each role.
- Work with operations, finance, procurement and HR leaders to ensure your organization’s WFM requirements reflect an updated and holistic perspective that incorporates the needs of workers, managers, administrative staff and executives.
- Plan to migrate any on-premises and older cloud WFM applications to the latest generation of cloud solutions within the next one to two years to gain access to the latest capabilities and, where applicable, leverage migration discounts.
- Identify the potential of emerging WFM capabilities, such as AI-enabled skills management in WFM. Develop a business case for a pilot deployment to quantify the ROI and to justify wider rollout of the initiative.
Sample Vendors
ADP; ATOSS Software; Dayforce; Deputy; Jitjatjo; Legion Technologies; Promark; Quinyx; UKG; WorkForce Software
Gartner Recommended Reading
Continuous Employee Performance Management
Analysis By: Laura Gardiner
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Continuous employee performance management (PM) tools enable managers and employees to track progress toward goals, and capture ongoing informal and evaluative performance feedback. Increasingly, they include managing one-to-one preparation and follow-ups; pulse engagement surveys; diversity, equity and inclusion (DEI)-related filters; and ties to rewards and recognition. Recent innovations in applying generative AI (GenAI) to PM are in early adoption, but may resolve longstanding challenges.
Why This Is Important
If done well, continuous PM often leads to improved engagement, employee productivity and manager effectiveness. When employees are all working toward goals aligned with corporate strategy, better business results typically follow. In fast-paced business environments, PM technology that enables and documents regular feedback and check-ins helps ensure that workers focus on the right things, while giving the organization visibility into work progress.
Business Impact
Adapting performance feedback processes to match the pace of business is a vital step for leaders of HR transformation initiatives. Continuous employee PM supports the cycles of expectation setting, feedback and evaluation. Leveraging technology to give and receive feedback improves adoption and signals the importance of feedback in the organizational culture. Establishing this culture of feedback improves employee performance, while supporting growth and development.
Drivers
- Many organizations are subject to policies and regulations that are satisfied by compliance with and documentation of a formal performance evaluation process.
- Growing talent pressures from macroeconomic conditions affect the demand for PM processes and technology that create a foundation on which to differentiate rewards for top performers.
- The vision of continuous PM as a driver of skills growth and internal mobility readiness for increased retention remains elusive for many organizations still working to embed collaborative and constructive feedback into the fabric of the company culture.
- Innovations in applying GenAI to continuous PM technology can increase employee and manager perceived satisfaction with performance activities by reducing the work burden.
- As continuous PM practices mature, organizations seek technology that understands the role of continuous PM in a strategic talent management function.
Obstacles
- As consumer appetite for AI reaches talent management processes, the market is in transition, and the vendor landscape is changing.
- Continuous PM programs often need to be supported by multiple technologies that tie feedback, performance evaluation, merit compensation planning, employee development, coaching and succession planning together as part of a broader talent management function.
- However, there are limited technology options for large and technologically mature companies looking to provide a consumer-grade experience that handles their internal complexities.
- Properly implementing and executing continuous PM requires significant resources and strong change management practices to address manager and employee resistance and weave PM into ongoing ways of working.
- Feedback may be treated differently across organizational business units, causing a healthy skepticism among employees about the relationship between feedback, contributions and compensation.
User Recommendations
- Deploy solutions that support the right cadence and activities for the organization and culture.
- Evaluate new processes and tools for relevance and practicality, from both the employee and management viewpoint.
- Track current market disruptions, and use business capability modeling to develop a foundational, long-term plan.
- Evaluate the potential risks and benefits of GenAI in PM by considering quality, impact on relationship and regulatory requirements.
- Invest in robust change management practices to ensure the adoption and impact of selected technologies that support employee PM.
Sample Vendors
15Five; Betterworks Systems; Culture Amp; Lattice; Profit.co; Quantum Workplace
Gartner Recommended Reading
Employee Well-Being Solutions
Analysis By: CV Viverito
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Employee well-being solutions refer to the set of technologies and services extending beyond physical wellness and into mental/emotional, financial and community support. Components of employee well-being include mobile apps, wearable devices, dashboards to track status, on-demand motivational and instructional content, organized events, and rewards. Additional components include communities and social networking capabilities and gamification services (such as leaderboards and challenges).
Why This Is Important
Fatigue, stress, worry and anxiety are common symptoms of the modern workplace. In response, 92% of total rewards leaders report steady or increasing well-being budgets. However, only one in three report their organization is currently doing a good job of assessing their well-being programs’ efficacy. Employers still need to address the issues of low employee participation and reactive well-being approaches.
Business Impact
Employers have historically deployed wellness programs focused on reducing employer healthcare costs. However, the broader value of employee well-being programs is toward reinventing employee value proposition (EVP) to deliver a more human deal that focuses on the whole person, their life experience and, ultimately, the emotional response this human deal creates. This eventually increases engagement and loyalty among employees.
Drivers
Employee well-being solutions can boost employee experience and deliver impact. These solutions can consist of three main sets of capabilities: foundational, direct functional and indirect functional.
- Foundational capabilities include a unified user experience (UX) layer for employees and other users; analytics for employees, managers and HR to show participation and impact of the various programs; and integration components to connect the various functional capabilities.
- Direct functional capabilities include functionalities to help employees support the various aspects of well-being: physical, mental, financial, community/social and digital. These functionalities are used solely for the purposes of well-being support and include technology (e.g., mobile applications and wearable applications), content (e.g., learning resources) or interaction with experts (e.g., counseling or clinical support).
- Indirect functional capabilities include functionalities not used solely for well-being and that support effectiveness of related programs. Examples include:
- Voice of the employee (to measure employee sentiment about well-being programs)
- Employee recognition (to provide recognition relevant to well-being achievements)
- Learning experience (to support learning activities toward well-being)
Obstacles
- The business value of employee well-being initiatives is difficult to quantify. This is particularly true as participation rates are generally still low (typically, under 50%) despite more time and effort in communicating well-being offerings. Many organizations also find it hard to make the broader connection between participation rates and actual outcomes, such as mental health. Although midmarket organizations increasingly make well-being investments, larger employers typically have the budget for such initiatives without a rock-solid business case.
- Organizations with a reactive mindset around well-being. This affects the ways organizations are measuring the impact of well-being programs, connecting data beyond absence and retention rates. This data is often unstructured and requires deeper analysis to uncover impact.
- Limited insights with regard to the use of well-being solutions. Organizations provide applications and services to their employees as a benefit, but fail to regularly ask for employee feedback with regard to their use and perceived value.
- Fragmentation of solutions. The employee well-being solutions landscape has been very fragmented, which makes it difficult to streamline user experience and analytics. More recently, we have seen a stream of M&A between well-being providers and healthcare providers, and the incorporation of financial well-being in many payroll solutions.
User Recommendations
- Pilot where possible to justify further investment. Programs can start as a grassroot effort to reduce stress, to become more physically or mentally active, or to create a greater sense of team spirit. Well-being coaches and employee recognition initiatives can play a key role in encouraging participation and building communities.
- Design metrics to capture the full employee adoption journey and align well-being measurement to key moments in the well-being program administration life cycle, such as the launch of a new program or the delivery of communications in a campaign.
- Enroll senior leadership as champions for well-being. Employee well-being becomes more strategic and transformational when connected to formal programs and HR processes.
- Plan how employee well-being technology will connect with your wider HR technology ecosystem. Employee well-being can be delivered via point solutions, employee experience technologies and human capital management suites. Buyers should first review the capabilities offered by existing providers, then consider additional point solutions if needed. If deploying multiple point solutions, consider utilizing providers that act as data, workflow and analytics aggregators.
Sample Vendors
ADP; Alight; Benevity; BetterUp; Cabana; Personify Health; TELUS Health; Thrive Global; Unmind; WebMD Health Services
Gartner Recommended Reading
Machine Learning in HR
Analysis By: Sam Grinter, Helen Poitevin, Eser Rizaoglu
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Early mainstream
Definition:
Machine learning is an AI discipline that solves business problems by utilizing statistical models to extract knowledge and patterns from data. Machine learning techniques, when applied to HR, translate most frequently into data-driven recommendations and predictive insights in domains such as recruiting, learning, employee engagement, compensation, benefits, HR service management and career development.
Why This Is Important
Machine learning helps HR leaders go beyond descriptive analytics and basic reporting to detect patterns and improve decision making. Insights change how strategic investment decisions are made, helping HR leaders select high-impact talent program investments. They provide data-driven decision support around who to hire, which learning materials to consume, what compensation to propose or what actions to take to improve engagement.
Business Impact
Data-driven insights ensure that HR and organizational leaders make the right investments in talent for the future. Flight risk analysis identifies drivers influencing employee churn. Strategic action can be taken to decrease the cost of attrition and increase engagement. Through personalization and prescriptive advice to employees and managers, HR can have a greater impact on employee experience, organizational culture, reskilling and upskilling efforts, and overall organizational health.
Drivers
- Embedded and vendor-provided capabilities such as risk analysis, recommendation engines or matching algorithms within a broad set of HR applications drive widespread adoption. This includes employee flight risk analysis, sentiment analysis, candidate-ranking algorithms, learning recommendations, augmented and segmentation analysis across many talent metrics, and a personalized homepage with an automated display of key insights or workflows to fit user behavior patterns.
- Homegrown or consulting service-provider-developed algorithms support one-off talent analytics projects or custom applications. These cover the same scope as above, but may also aim to connect talent data with business operations data to uncover areas of improvement with clear business impact.
- Hype is driven by a desire of HR teams to go beyond descriptive analytics to more predictive and prescriptive insights. Since machine learning is one set of AI techniques, interest is also driven by innovation teams seeking AI and increasingly generative AI use cases in the HR domain.
Obstacles
- Lack of awareness among HR teams that this technology is supported by some of the vendor solutions they already have in place.
- A number of HR technology providers have struggled to introduce machine learning into their solutions. Organizations leveraging aging HR solutions may not have access as easily to this set of capabilities.
- Lack of trust in the models delivered by HR technology providers; many are proprietary and not openly shared with customers.
- Difficulties in accessing relevant data to support meaningful predictions or models. Data can often be siloed in individual applications.
- Lack of discipline in model management including drift, infrequent updates, and lack of adaptation to new data or data management practices.
- Lack of resources in HR teams to develop, build and deploy models, to support the use of machine learning in HR.
User Recommendations
- Evaluate your existing application portfolio for the use of machine learning techniques and check vendor roadmaps.
- Hire or nurture staff that can understand machine learning and advanced statistics in order to articulate the benefits and limitations of the associated techniques.
- Evaluate solutions based on the relevance and accuracy of the output of the models; the ability to modify or build models; the data lineage and the nature of the data being used. Also evaluate on the basis of the ability to leverage analytical output in other analytics workflows, or display results in various parts of the application; the results as presented to end users.
- Ensure alignment with digital ethics principles because machine learning in HR involves personal data about workers.
- Explore use cases where data from HR systems, and from other business or operational systems, are combined to answer strategic and business-critical talent-related questions.
Sample Vendors
ADP; Deloitte; One Model; Oracle; Panalyt; Qlearsite; SAP; UKG; Visier; Workday
Gartner Recommended Reading
Workforce Planning
Analysis By: Harsh Kundulli, Helen Poitevin
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Early mainstream
Definition:
Workforce planning enables HR professionals to plan and monitor the evolution of their organization by aligning talent supply and demand to various business scenarios, such as transformation, growth, rationalization or divestiture. Functions can include organization visualization and modeling, support of restructuring, headcount management, headcount budgeting and forecasting, and strategic workforce planning.
Why This Is Important
The need to respond to market shocks and uncertainty makes it important for HR, finance and business leaders to be well-equipped to support agile and continuous workforce planning activities. There is increased HR leader interest and maturity in implementing workforce planning processes. Technology solutions supporting workforce planning continue to improve their ability to connect tactical and strategic scenario-based workforce planning activities.
Business Impact
Workforce planning and modeling brings business, HR and finance leaders together. It provides them with a shared view of the current workforce, and of the workforce-related changes that must occur to meet strategic and operating objectives. It supports both short- and long-term strategic business goals, whether these are related to managing economic uncertainty, driving talent agility, location strategy, AI productivity impacts, growth or changes through merger and acquisition, or divestiture activities.
Drivers
- Interest in workforce planning tends to be cyclical, increasing in times of uncertainty and decreasing in times of stability. Economic uncertainty, geopolitical shifts, demographic changes and AI investments have created an environment for rising interest in workforce planning.
- Workforce planning practices are getting established for both operational workforce planning (headcount management, budgets and forecasts) and strategic workforce planning (scenarios and strategic investment decision support). Increased process maturity and improved governance typically lead to more technology investments.
- Organizational modeling and transformation require timely communication, employment contract changes and system updates. This is still a heavy administrative burden for HR teams in large multinational organizations. Hence, organization modeling technology solutions that can help assess scenarios and automate the execution of these tasks are being increasingly adopted.
- Skills data is increasingly becoming critical to managing talent, and AI-enabled skills management and labor market insights are now enabling skills-based workforce planning. This is of particular interest when doing labor market scans to determine skills availability.
- Workforce optimization, including capacity utilization optimization, automated work distribution and specific resource planning, remains industry-specific. Interest in this form of workforce planning has increased due to the introduction of automation and hybrid work arrangements.
- Interest in extended planning and analysis (xP&A) is increasing due to its ability to provide better transparency into business performance and enable more holistic planning. xP&A connects financial planning and analysis with other planning disciplines across the enterprise, such as supply chain, operations, IT, sales and workforce planning. Adequate functionality in xP&A tools to meet HR’s operational workforce planning drives tighter alignment with financial planning and analysis, and leads to increased tool adoption.
Obstacles
- Leaders across business, finance and HR have disparate perspectives on what workforce planning is and how it should be done.
- Organizations exhibit varying degrees of maturity in employee data governance, which in turn affects access to employee data for headcount reporting and other kinds of workforce planning.
- Lack of access to quality data on the contingent workforce makes total workforce planning challenging.
- HR is not always in a leadership position for workforce planning. It may be managed within a finance organization. This often limits the application of strategic workforce planning.
- Strategic planning efforts rarely incorporate downstream workforce planning implications into their timelines.
- Detailed personnel cost planning can be challenging in multinationals because of the variability of payment and wage types across geographies. In addition, payroll system data can be difficult to access due to aging systems.
- No one workforce planning technology solution can support all forms of workforce planning.
User Recommendations
- Collaborate: Engage in conversations with business leaders and executives to prioritize the workforce questions you need to answer. This will help you decide which type(s) of workforce planning and associated technologies you need.
- Start small: Prioritize certain workforce segments for workforce planning rather than starting with the whole workforce. For example, choose hard-to-resource workforce segments.
- Explore new technology: Explore using AI-based techniques to confront the challenges in workforce planning, such as obtaining reliable skills and talent profile data, and the ability to include more data sources.
- Create a portfolio: Invest in a portfolio of technologies to support the most critical workforce planning activities in order to increase workforce planning maturity.
- Align: Connect workforce planning to financial planning and analysis through xP&A.
Sample Vendors
Albert; Anaplan; Ingentis; Nakisa; Oracle; Orgvue; SAP; Vemo; Visier; Workday
Gartner Recommended Reading
Recognition and Reward Systems
Analysis By: Rania Stewart
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Employee recognition and reward systems allow organizations to show appreciation to individuals “in the moment” for accomplishments and behaviors desired by the organization, and provide additional input to the year-end review process. Rewards can be monetary- or nonmonetary-based.
Why This Is Important
Given the challenges in the macroenvironment, many organizations need to revitalize company culture and organizational morale to improve talent attraction, engagement and retention. Recognition and rewards technology provides a continuous means for grassroots value contribution appreciation in the flow of day-to-day work achievements. It also serves to bring a record of those in-the-course-of-the-year accomplishments as an additional input to end-of-year performance reviews, thereby limiting recency bias (where last 90 days of achievement get disproportionately more attention).
Business Impact
Recognition and reward technology can improve an employee’s morale, motivation and sense of belonging. It can help to drive and sustain initiatives such as job candidate referrals, company well-being initiatives, health and safety, and environmental, social and corporate governance (ESG) programs. Recognition and rewards technology also helps bridge the gap from a purely past-focused assessment culture to a more agile, ongoing supportive learning culture.
Drivers
- Making in-the-flow-of-work recognition easier than ever via built-in “widget-like” integrations with collaboration tools, like Microsoft Teams and Slack.
- Consolidating to a single, more efficient off-the-shelf rewards and recognition technology to meet the needs of multiple, homegrown recognition tools throughout an organization.
- Leveling up to a more capability-rich point solution to meet organizational needs, when lightweight features masquerading as products no longer fit the bill.
- Improving talent retention and attraction for in-demand skills and talented individuals.
- Driving an elevated customer experience by using recognition and reward to positively influence employee engagement.
- Providing motivation outside of merit and bonus plans, which typically occur only once or twice a year.
- Empowering individuals to recognize others for “going the extra mile” or exceptional work on a one-on-one or a one-to-many basis.
- Allowing leaders to track progress on initiatives such as ESG and other efforts outside of revenue targets.
Obstacles
- Unclear or wavering management sponsorship and support, with not enough tangible business value for HR leaders to prioritize recognition and reward programs ahead of other initiatives.
- Reduced scope and/or lack of budget for monetary awards; both for the technology and per employee/per year spend allocation.
- Lack of awareness that this category offers much more than a work anniversary gifting tool.
- Lack of change management and internal commitment to continually evolve the recognition and reward program through thoughtful program and campaign management (minimum partial dedicated resource).
- Perception of recognition and reward as being a tactical compensation and benefits project.
- Fear of individuals gaming the process/system and uncertainty of recognition and reward process governance.
- Buyer confusion because the market is crowded with overlapping offerings from HR, talent, employee experience, and recognition and reward solution providers.
User Recommendations
- Elevate the impact of recognition with a reward component. Use inbuilt reporting and analytics to gain insight into where and why there are hot and cold spots of usage.
- Invest time in solution design and internal marketing to push awareness and ongoing system usage and engagement. Make it easy to give and “see” recognition and encourage leadership to regularly promote the program. Enable in-the-flow-of-work accessibility ease (such as embedded in Teams).
- Consider a mix of monetary rewards and nonmonetary rewards, including paid-time-off days, charitable donations and volunteer service time.
- Use recognition to encourage and maintain cultural cohesion and employee engagement to unite employees across work styles with the mission, principles and culture of the company. Examine the possibility of integrating recognition systems with performance management systems to enhance the 360-degree view of the employee.
Sample Vendors
Achievers Solutions; Awardco; BI WORLDWIDE; Giift; Kudos; O.C. Tanner; Semos Cloud; Vantage Circle; Workhuman; WorkTango
Gartner Recommended Reading
Climbing the Slope
AI in Talent Acquisition
Analysis By: Jackie Watrous
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
AI capabilities found in recruiting solutions deliver efficiency and data analysis to drive better staffing outcomes. For example, AI can automate repetitive manual tasks, personalize the candidate experience and analyze data to prioritize top qualified candidates. AI solutions in recruiting continue to emerge, but AI-enabled sourcing, screening and scheduling solutions are currently the highest in demand.
Why This Is Important
Recruiting leaders continue to face talent shortages, hiring volatility, increased candidate expectations and pressure to drive efficiency. They must consider AI features to stay competitive. Capabilities like AI-enabled sourcing and screening can deliver high-impact results that reduce time to fill and improve quality of hire. As offerings mature and vendors address risk mitigation, we see adoption and utilization increase. Teams are moving from AI curiosity to active, operational use.
Business Impact
AI solutions can improve the productivity of recruiters and hiring teams, and positively impact candidate engagement. They help elevate recruiting team capabiltiies throughout the recruitment life cycle. Investments in AI can improve key deliverables, such as candidate experience, diversity and inclusion, quality of hire (candidate prioritization, skills identification), cost per hire, time to hire and process efficiency.
Drivers
- Generative AI (GenAI) hype: AI capabilities have been present in recruiting solutions for some time. However, the hype around GenAI has accelerated the entire AI conversation. We are now seeing human capital management and talent acquisition suite vendors embedding AI features into their core product, alongside point solutions that bring additional depth of functionality.
- Demand for AI-enabled sourcing: The demand for AI-enabled candidate sourcing is increasing as organizations seek to curate leads for open positions within their existing pools or through externally sourced public data. This has the potential to rightsize increased spend in the sourcing space to find the required talent.
- Transparency and efficiency in candidate screening: AI in solutions like candidate matching/prioritization and candidate assessments have matured over the last few years, with vendors placing a greater emphasis on responsible and transparent AI. Features now include details that show the recruiter why a candidate was given a particular ranking/score. Many organizations are looking for ways to help their teams screen more efficiently and effectively, particularly where there is a high volume of candidates per role, which introduces the risk of overlooking candidates with the right skills match.
- Candidate experience and recruiter efficiency: Many organizations are looking to eliminate manual processes, with an added goal of improving and streamlining the candidate experience. AI has delivered significant improvements in activities such as scheduling and data collection. New features continue to emerge, such as interview intelligence, which offers the ability to generate interview summaries and gather defensible interview feedback.
Obstacles
- Complex vendor selection: The market is flooded with vendors introducing new AI capabilities. Some will have done the appropriate level of due diligence to focus on responsible AI, but others will not. A thorough review is required to ensure new vendor partners have taken the necessary steps to mitigate bias.
- Risk of replacing rather than assisting human decision making: During implementation, there is a risk that too much AI automation is brought into the process. Teams must set expectations regarding the use of AI features to ensure the AI assists in human decision making, but does not replace it. Clearly documenting recruiter roles and where AI will support is critical.
- Ethics and compliance requirements: AI remains under review with legal, compliance and data privacy teams. In certain countries, regulations are starting to address how AI should be managed to ensure fair outcomes.
User Recommendations
- Focus on prioritized use cases when considering AI solutions. Use cases may include improving candidate engagement, elevating recruiter capabilities, implementing automation or reducing cost.
- Consider full automation through tools like virtual assistants for high-volume recruitment, such as in retail, to reduce candidate drop-off and time to fill.
- Start with roles that have clear job descriptions and qualifications, and a sizable candidate base for candidate matching/prioritization. Partner with vendors to understand how they monitor for bias. Vendors should confirm the frequency of these analytics and provide samples as part of the implementation.
- Engage with governing bodies within your organization when considering new vendors and preparing for implementation. While some capabilities have low risk (e.g., interview scheduling), others will require more thought to mitigate risk (e.g., prioritization of qualified candidates). Vendors must deliver a product that offers explainable AI, through the user interface (e.g., highlighting skills matches within a profile), and through analytics that demonstrate fairness in selection outcomes.
Sample Vendors
Beamery; Eightfold AI; HiredScore; hireEZ; HireVue; Paradox; Pillar; Phenom; SeekOut; Textkernel
Gartner Recommended Reading
Digital HR Document Management
Analysis By: Ron Hanscome
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Digital HR document management tools enable enterprises to store, access and manage HR documents, while complying with multijurisdictional regulatory requirements for security and retention. Common functions include multilevel security, document tagging to enable search, notification and approval, digital signature support and robust auditing/traceability. Typically, these solutions integrate with administrative HR systems, but they may also link to other HR and identity management solutions.
Why This Is Important
Enterprises struggle with how to best manage HR documents needed for regulatory and corporate policy compliance. Storing paper files in HR offices or warehouses has existed for decades, but this approach is costly and lacks the security and quick access/search capabilities needed for legal discovery or compliance audit requests. Also, paper records cannot be analyzed for missing data or expirations, nor can they be easily purged. Automation and digitalization are needed to address these issues.
Business Impact
Digitalizing HR documents can result in productivity savings due to:
- Time saved searching for information.
- Reduced physical document storage costs.
- Delivery of secured access to data for HR staff process support.
- Mitigation of risk of using dated or incorrect legal forms.
- Avoidance of regulatory fines and potential legal costs.
Enterprises with a complex HR technology portfolio can benefit from using this solution to combine transactional and unstructured data with documents to form a unified HR hub.
Drivers
- While many firms have partly digitized basic HR documents, several factors have added to this function’s complexity. These include:
- Increasing globalization, which raises the number of workers operating in multiple locations. Volume of documents, storage, security and retention requirements vary by country.
- The sheer volume and rate of increase of regional and country-specific regulatory requirements, including GDPR, digital format mandates, and those driven by distributed work environments.
- The impact of mergers, acquisitions and divestitures, which generate even more documentation.
- The need for easy employee, manager and HR administrative access. This became particularly acute with the ongoing use of fully remote and hybrid workers, as these roles are typically unable to access physical documents “at the office.”
- Difficulty in determining how best to grant and manage the right level of access to the appropriate users across HR and operational functions.
- Although client interest in documentation requirements for COVID-19 has dried up, many HR and operations functions are applying these principles to broader health and wellness tracking, including health attestations and doctor’s notes for medical reimbursement.
- Many organizations desire solutions that enable a holistic approach to managing HR documents in their distributed environment.
- Several HR document management vendors have added IHRSM functionality. Conversely, many IHRSM solutions and HCM suites have added HR document management, as have several content services platforms (CSPs). These providers continue to enhance their offerings to meet a wider range of use cases. The result is a steady market progression, with adoption slated to continue increasing (particularly among midmarket enterprises) over the next three years.
Obstacles
- Picking the right solution from this complex landscape is a significant challenge, as market entrants come from:
- Traditional records management providers that have developed software combined with services to help clients convert paper records to digital.
- CSPs that have enhanced their solution to comply with HR’s more stringent security and confidentiality needs.
- HR software and service providers that have either built or acquired and integrated a point solution.
- Point solutions that may cover multiple countries and deliver granular security models, including the ability to specify where digital documents are physically stored. This is particularly relevant to clients with operations in the EU due to the enforcement of GDPR.
- Justifying the investment if the enterprise hasn’t experienced litigation or penalties due to prior noncompliance.
- Ensuring sufficient change management to increase adoption, especially in lagging businesses.
User Recommendations
- Determine needs based on business growth strategies and whether they include new locations in countries with differing regulations. Consider the persistence of remote and hybrid work, as this will increase demand for digitalization. Collaborate with legal department staff to ensure alignment with the organization’s regulatory compliance philosophy.
- Develop a strategy around HR document governance, which may include addressing any existing paper document backlogs.
- Determine if the organization is ready to meld it into a broader IHRSM initiative, as this will reduce the vendor pool to those meeting both requirements.
- Evaluate the current CSP strategy and solution along with other alternatives.
- Scrutinize the provider’s ability to handle complex requirements like customer-configurable workflows and notifications, quick document tagging and search, and compliance with multijurisdictional records retention policies and regulations.
- Include processes for ongoing conversion of paper to digital, as some amount of paper documentation will be a fact of life for many organizations for the foreseeable future.
Sample Vendors
The Access Group; aconso; ADP; D2Xchange; DynaFile; Hyland; Neocase Software; OpenText; ServiceNow; UKG
Gartner Recommended Reading
Integrated HR Service Management
Analysis By: Ranadip Chandra, Eser Rizaoglu
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Integrated HR service management tools are a holistic platform for organizations to manage HR shared services operations and transactional activity. Core functionality includes HR case management (ticketing or routing), knowledge base, content delivery via channels such as portal and virtual assistant, SLA monitoring and single sign-on into transactional applications. Additional functionality may include digital document management, business process management tools and transition management.
Why This Is Important
Many HR organizations move through physical, virtual or distributed shared services models, especially if they have more than 2,500 employees in multiple geographies. Integrated HR service management (IHRSM) solutions give robust control and standardization to the processes required to provide and manage HR services. Personalized workflows for work or life transitions have become an important part of the employee experience narrative, especially for employees working in a hybrid workplace.
Business Impact
Improved HR administration can drive HR service delivery efficiently and improve the overall perception of HR. The effective deployment of integrated HR service delivery tools will significantly reduce HR shared services costs. At mature levels, IHRSMs include early detection and correct handling of employee relation cases with the correct actions and documents. These include investigation questionnaires and court-ready templates, which improve the process for employees while saving legal fees and remaining compliant for the organization.
Drivers
Demand for IHRSM tools is driven by a desire to streamline HR administration, increased compliance and risk complexity, which has accelerated in recent years due to a desire to improve employee service experience. Additional drivers include:
- Expanding the scope of manager-led configurable workflows (often branded as “journeys”) for assisting employee life cycle events such as parental leave, academic sabbatical and work events such as onboarding, role change or relocation.
- Automating the resolution of repetitive employee queries around common policies and company updates. Resolving the employee questions, before they are logged in as tickets, helps in reducing the manual workload of the HR shared services resources. Additionally, some advanced tools help automatically create categories grouping similar cases highlighting common HR technology challenges.
- Managing sensitivities relating to HR issues and data, which requires specialized functionality above that of IT or CRM service management applications. For example, specialized complexity and legislative requirements for union-governed cases, health and safety cases, long-term disability cases, or general data protection regulation (GDPR) compliance are often too complex for incumbent IT ticketing systems.
- Providing comprehensive employee service experience throughout the enterprise. Most IHRSM vendors now offer natively built conversational platforms, in addition to common access options such as portal, mobile device and online chat.
- Emerging GenAI developments aim to generate comprehensive case summaries for HR experts and specific brief responses to complex queries from employees analyzing multiple policy documents.
Obstacles
- Solutions in the market vary in the robustness of case management capabilities as well as the depth of HR domain expertise.
- Vendors’ focus on workflows has often come at the expense of improving other core functionalities such as employee relations or document management. As a result, many organizations need a hybrid portfolio of IHRSM solutions to satisfy different use cases.
- Many IHRSM solutions lack support for specialized cases that require judgment-based decisions, such as employee relations, grievances and disciplinary actions, as well as survey questions, form templates and domain expertise.
- IHRSM solutions add a secondary layer of cost and integration maintenance that adds complexity to the HCM technology portfolio.
- The low-code/no-code (LCNC) platforms to configure workflows remain at various levels of maturity and many of the solutions in this category offer limited flexibility for custom workflows.
User Recommendations
- Evaluate IHRSM solutions based on their ability to support different functional components covered under HR service management. Generally, the solutions tend to be stronger in the module of their origin (e.g., case management, document management) and weaker in other extended use cases.
- Avoid selection bias by balancing the evaluation of overly hyped employee experience features with less visible but critical capabilities such as employee relations.
- Assess the level of complexity in configuring the IHRSM solution with the HR core. It is preferable to pick a solution that offers out-of-the-box integration with the present HCM suite. If any additional tool, such as an HR virtual assistant, is needed for handling employee queries on top of the IHRSM solution, then assess the integration readiness between the two.
- Investigate emerging capabilities such as alumni portal, employee communications and campaign management and critically evaluate organization-specific needs.
Sample Vendors
Avature; BMC; Dovetail Software; Ivanti; Leena AI; Neocase Software; ServiceNow; UKG; WTW
Gartner Recommended Reading
Talent Analytics
Analysis By: Laura Gardiner
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Talent analytics includes the processes and tools HR and business leaders use to track HR’s performance and program investments, and to analyze talent segments. In conjunction with business performance, talent analytics improves performance measurement and contextual decision support throughout an organization, thereby improving overall workforce effectiveness and ongoing productivity.
Why This Is Important
Enterprises equipped with accessible talent data and insights will meet the fast-paced demands of business and a rapidly evolving workplace for strategic decision making. All leaders need to understand which talent metrics to focus on, and have the skills, data, analytics tools and governance necessary to support them. Talent analytics maturity is increasing across enterprises. The most advanced are going beyond HR data to incorporate external data for more complete analyses of HR impact and opportunities.
Business Impact
All organizations need, at a minimum, visibility into headcount, employee demographics and HR process completion rates. Most organizations leverage talent analytics insights to improve diversity and employee experience. Advanced talent analytics teams combine talent and skills-related data with other business data to explore the impacts of talent decisions on business outcomes. New applications of AI can enable faster delivery of insight without increasing analytics team headcount.
Drivers
- Adoption of talent analytics technology solutions continues to grow. Solutions include prepackaged talent analytics offerings from specialist providers or the talent analytics modules offered by their cloud human capital management (HCM) suite provider. In addition, many have built their own data lakes or data warehouses and reporting solutions on generalist BI platforms.
- Self-service access to trend analysis, standard dashboards, KPIs and predictive insights are standard parts of talent analytics solutions. The aim is to increase the HR team and business leader’s use of talent insights in decision making.
- The volume of data available to organizations continues to rise, and business leaders are expected to rely on objective, data-driven insights from talent analytics to navigate the uncertainty of a rapidly evolving talent landscape.
- A growing number of solutions offer augmented analytics capabilities to pinpoint talent segments requiring greater focus for improvement efforts.
- Growth of midmarket adoption in organizations with fewer than 2,500 employees is progressing as market offerings extend to meet their requirements regarding fast time to value and cost-effectiveness. Adoption will accelerate as business leaders recognize how access to more sophisticated talent analytics, especially those with user-friendly conversational interfaces, can improve their ability to make more strategic and better-informed workforce-related decisions at the organization, team and individual employee level.
- Highly mature talent analytics practices incorporate data from systems beyond those managing HR processes. This includes behavioral data, operational data from business processes and external data sources. Advanced talent analytics functions engage in strategic talent analytics projects aimed at using data to drive strategic investment decisions and employee experience design initiatives. GenAI is making this level of strategic talent analytics accessible to more and more organizations.
Obstacles
- Organizations have difficulty accessing data from HR and non-HR systems.
- Talent analytics teams encounter challenges in setting up and maintaining sufficient data governance.
- Frequent changes in organizational structures introduce complexities in time series analysis.
- Metrics can appear stable at the highest level but show great variability across meaningful segments. These meaningful segments can be difficult to detect and act upon without technology-assisted insights.
- Some talent analytics teams get stuck rebuilding and adjusting dashboards for headcount and HR operations metrics. They struggle to go beyond this work to take on analytics projects and to deliver more meaningful insights.
- Organizations struggle to structure talent analytics teams to meet ever-growing demands from business users with limited staff.
User Recommendations
- Align talent analytics investments to HR strategy, HCM technology strategy and enterprise analytics strategies. When selecting technology solutions, consider the size of your talent analytics team and any budgetary constraints.
- Invest in data governance for the most critical data points that support baseline analytics, such as headcount, worker, job or functional categories, location, and department.
- Invest in technologies that automate the design and delivery of standard dashboards, metrics and reports, along with importing data from non-HR functions like finance. When possible, make sure these technologies contain augmented analytics features to drive adoption across HR roles and business management. Aim to free up talent analytics resources to focus on more strategic talent analytics projects.
Sample Vendors
Crunchr; One Model; Orgvue; Praisidio; SplashBI; Vemo; Visier; ZeroedIn
Gartner Recommended Reading
Entering the Plateau
Employee Onboarding
Analysis By: Rania Stewart
Benefit Rating: High
Market Penetration: More than 50% of target audience
Maturity: Mature mainstream
Definition:
Employee onboarding begins when a job applicant accepts a contract or employment offer and ends when that worker is productive at work, increasingly extending well past the first 30 days. Onboarding solutions include: forms management to support regulatory compliance, task management to ensure activities are completed efficiently, asset provisioning that includes software access, badges and uniforms, and sociocultural assimilation tools to improve engagement, productivity and retention.
Why This Is Important
Organizations are upgrading their onboarding processes as part of the “first impression” new-hire experience, bridging from candidate to employee. The more operational side of automating onboarding at scale (tasks, forms, training) is being complemented by additional investment in building sociocultural components of onboarding, such as caring, belonging and trust-building, most in demand since the COVID-19 pandemic. This is an attempt to balance investment in finding the right hire and getting them started with the best, “stickiest” employee experience, as a means of not losing that investment prematurely.
Business Impact
Onboarding products are largely adopted by organizations for compliance (compliance new-hire forms) and transactional on-ramping (what’s needed for the first paycheck). Increasingly, they are also in demand to improve scalable operational efficiency — in the form of a more holistic workflow that ties in related tasks undertaken by L&D, procurement, security, facilities, finance and IT, as well as softer, experiential capabilities nurturing team and organizational belonging. Onboarding technology helps support retention and increase speed to productivity/sustainable engagement.
Drivers
- A need to support a greater variety of employee transitions through the talent life cycle: Increasingly, onboarding software is being purpose-built or tuned for additional employee transitions, including promotions and transfers (cross-boarding), acquisitions (massboarding), terminations (offboarding), and “boomerang” rehires (reboarding).
- Demand to support sociocultural objectives such as organizational and team belonging: Employee experience has become a key focus for using onboarding technologies to support new hires, transfers, contract workers, reorganizations, and on-site to virtual moves and vice versa.
- An increase in regrettable turnover for hard-to-fill roles: The offboarding process, previously focused on compliance, has also become an experience-driven process. Organizations are looking to manage process delivery as well as encourage positive feelings (e.g., closure) for potential alumni to return or recommend others to work at the organization in the future.
Obstacles
- Integrated provisioning: The embedded links and key contact features for delivering different asset types (e.g., uniforms, laptops, software access) often fall short of expectations. This disappointment is largely due to the reliance on integration with other in-house systems like procurement, learning and identity management.
- Compliance forms processing: Some challenging use cases, such as the identity verification required for forms such as the I-9 in the United States, may not be addressed by all onboarding solutions. Also, while many onboarding solutions offer a forms builder with some sample templates, they do not necessarily maintain the most up-to-date forms for each country/province/state/city in which an organization hires. This is particularly impactful to more global organizations.
- Fragmented ownership: Onboarding requires setup tasks to be completed by various internal departments such as legal, IT and L&D. Defining clear ownership is often a challenge, given the heavy dependency on one another, as well as the myriad of HR technologies that offer onboarding modules.
- Limited AI application: With onboarding’s expanding definition and parameters, leaders find it hard to accomplish the aspired leap in value proposition (particularly, improving experience and talent retention). It may easily take two to three complementary solutions, layered over time, to mature into a world-class onboarding experience, as providers are slow to apply AI technology to automate various transition processes.
User Recommendations
- Assess your onboarding needs and your process maturity carefully before committing to a technology or vendor. Ask prospective vendors to be explicit about what areas of onboarding they actually manage versus simply capture as a topical placeholder in a workflow process (leaving the automation burden to your organization). Common expectation mismatches include geography-specific compliance forms maintenance and integrated asset provisioning.
- Move beyond baseline automation of administrative forms and evaluate onboarding solutions that address learning, cultural orientation and social collaboration. Seek vendors that assist in scaling team and organizational belonging.
- Make onboarding a part of broader digital transformation initiatives, including an end-to-end approach that integrates activities beyond HR’s borders, to improve enterprise efficiency. Onboarding plays a pivotal and disproportionate role in enduring productivity, engagement and all types of worker transitions (new hires, transfers, promotions, developmental/experiential programs, exits, etc.).
Sample Vendors
Appical; Deel; Enboarder; HeyTeam; HiBob; Leena AI; Rival; ServiceNow; Talentech (Talmundo); WorkBright
Gartner Recommended Reading
Appendixes
See the previous Hype Cycle: Hype Cycle for HR Technology, 2023
Hype Cycle Phases, Benefit Ratings and Maturity Levels
Hype Cycle Phases
Phase | Definition |
Innovation Trigger | A breakthrough, public demonstration, product launch or other event generates significant media and industry interest. |
Peak of Inflated Expectations | During this phase of overenthusiasm and unrealistic projections, a flurry of well-publicized activity by technology leaders results in some successes, but more failures, as the innovation is pushed to its limits. The only enterprises making money are conference organizers and content publishers. |
Trough of Disillusionment | Because the innovation does not live up to its overinflated expectations, it rapidly becomes unfashionable. Media interest wanes, except for a few cautionary tales. |
Slope of Enlightenment | Focused experimentation and solid hard work by an increasingly diverse range of organizations lead to a true understanding of the innovation’s applicability, risks and benefits. Commercial off-the-shelf methodologies and tools ease the development process. |
Plateau of Productivity | The real-world benefits of the innovation are demonstrated and accepted. Tools and methodologies are increasingly stable as they enter their second and third generations. Growing numbers of organizations feel comfortable with the reduced level of risk; the rapid growth phase of adoption begins. Approximately 20% of the technology’s target audience has adopted or is adopting the technology as it enters this phase. |
Years to Mainstream Adoption | The time required for the innovation to reach the Plateau of Productivity. |
Source: Gartner (July 2024)
Benefit Ratings
Benefit Rating | Definition |
Transformational | Enables new ways of doing business across industries that will result in major shifts in industry dynamics |
High | Enables new ways of performing horizontal or vertical processes that will result in significantly increased revenue or cost savings for an enterprise |
Moderate | Provides incremental improvements to established processes that will result in increased revenue or cost savings for an enterprise |
Low | Slightly improves processes (for example, improved user experience) that will be difficult to translate into increased revenue or cost savings |
Source: Gartner (July 2024)
Maturity Levels
Maturity Levels | Status | Products/Vendors |
Embryonic | In labs | None |
Emerging | Commercialization by vendors Pilots and deployments by industry leaders | First generation High price Much customization |
Adolescent | Maturing technology capabilities and process understanding Uptake beyond early adopters | Second generation Less customization |
Early mainstream | Proven technology Vendors, technology and adoption rapidly evolving | Third generation More out-of-box methodologies |
Mature mainstream | Robust technology Not much evolution in vendors or technology | Several dominant vendors |
Legacy | Not appropriate for new developments Cost of migration constrains replacement | Maintenance revenue focus |
Obsolete | Rarely used | Used/resale market only |
Source: Gartner (July 2024)