Hype Cycle for HR Technology, 2025
17 June 2025 - ID G00829927 - 138 min read
By Ranadip Chandra
CHROs face the critical task of delivering value and optimizing investments while seizing opportunities to achieve competitive advantage with cutting-edge technology. Use this Hype Cycle to gain insights into HR technology innovations and their potential impact, maturity levels and associated risks.
Strategic Planning Assumptions
By 2028, at least one-third of business decisions will be made semiautonomously or autonomously with the help of AI agents, up from less than 1% today.
By 2030, half of enterprises will face irreversible skills shortages in at least two critical job roles due to generative AI (GenAI) accuracy decline, skills erosion and uncompetitive pay.
Analysis
What You Need to Know
Over the past year, chief human resource officers (CHROs) have navigated the uncertainties of the macroeconomic climate by focusing on strengthening organizational culture, building a future-ready workforce and finding economically viable ways to boost employee productivity. They have achieved this while remaining vigilant in spotting innovations and planning generative AI (GenAI) pilots.
In a volatile and uncertain world, organizations expect CHROs to guide complex decision making and deliver value. CHROs must balance strategic exploration of potentially transformative innovations with investment in proven innovations (e.g., cloud HCM). By harmonizing the two, CHROs and their teams can fully harness advanced technology for both scalable HR deployments and digital HR transformation, creating immediate ROI and positioning HR for future success.
Key storylines in this Hype Cycle include:
- Targeted employee experience: EXTech applications offer smarter, personalized omnichannel experiences for users such as frontline and retirement-aged workers, covering interactions with HR, managers, teams and communities.
- AI advancements: A wide range of applications leverage machine learning (ML) and composite AI based on core HR technology. Although these innovations are at different maturity stages, many are advancing rapidly and delivering tangible ROI.
- GenAI maturation: GenAI in HR has moved past the Peak of Inflated Expectations, but it is still two to five years away from delivering consistent business value. HR teams are reviewing GenAI use cases alongside traditional AI and accessing many capabilities through their HR technology providers. Many of these providers have addressed GenAI risks through advanced governance features, thus increasing adoption.
- Hype versus value balance: Less-hyped technologies, such as next-gen workforce management, workforce planning and voice of the employee, are steadily maturing along the Hype Cycle. They consistently deliver value to HR, are nearing mainstream productivity and, therefore, deserve continued investment.
The Hype Cycle
This Hype Cycle features a large number of innovations that have many years of development and adaptation behind them, and that are likely to reach the Plateau of Productivity within five years. While a minority of innovations face impediments to their progress, most are steadily advancing along the curve, propelled by high-impact drivers such as AI.
New Hype
Agentic AI is one of the most hyped technologies added to this year’s Hype Cycle. Agentic AI systems go beyond generating text or analyzing data based on user prompts. They can also plan and execute autonomous or semiautonomous actions, dynamically adapting their approach based on context and ongoing analysis to achieve a specific goal.
In its early stages, agentic AI will streamline self-service tasks and workflows. As adoption increases, its transformative impact will expand, driving greater effectiveness and innovation in HR processes.
This Hype Cycle also introduces two additional innovations at the Innovation Trigger phase:
- EVP-EXTech for retirement-aged workers reflects the increasing participation of retirement-aged workers in the workforce and the unique needs of those workers. Its maturity level is embryonic.
- Mentoring solutions is now a separate innovation, distinct from coaching technology. It has a relatively higher maturity level, surpassing its two newcomer peers.
Peak Hype
Labor market intelligence (LMI) and internal talent marketplaces (ITMs) are at the peak this year. Organizations continue to prioritize skills in their talent management decisions, with many adopting a skills-based approach. LMI solutions enhance this approach by providing valuable insights into external skills availability.
The ITM market has grown rapidly, encompassing HCM suite providers, talent acquisition vendors, learning platforms and specialized point solutions. ITMs have matured in their application of AI to detect, infer and map relationships among skills. They can also use AI techniques to automatically match talent with work opportunities.
Fast Movers
Both ML and AI in HR have accelerated their progress, driven by customer demands and increased adoption. AI is becoming increasingly pervasive, with HR technology providers investing in AI applications within their products. The surge of interest in GenAI and agentic AI has spurred investment in AI across HR processes.
As investments in broader AI capabilities and solutions grow, so does ML use. ML, in conjunction with feedback loops from end users, is crucial for enhancing the accuracy of GenAI and AI agent solutions. It reduces errors by identifying and eliminating hallucinations.
Lingering Technologies
Challenges in managing data governance and securing necessary hardware, software and infrastructure investments have held back organizations from advancing people analytics and immersive learning (AR/VR).
The Priority Matrix
The Priority Matrix shows the degree of benefit potentially attainable from innovations relative to their progression along the Hype Cycle. However, impact is not the only factor to consider when investing. Applicability, budget, time to implement and receive payback, and business value are also important.
Innovations that deserve particular attention include:
- Integrated HR service management (IHRSM): Gartner expects that organizations will effectively deploy IHRSM within two years, significantly reducing HR shared services costs. IHRSM is also one of the foundational use cases for experimenting with HR-specific AI agents.
- HR virtual assistants (HRVAs): HRVAs are two to five years away from mainstream adoption. Early adoption of HRVAs will create significant competitive advantage for HR organizations. Techniques like prompt engineering via retrieval-augmented generation (RAG) have led to new methods of developing GenAI-native HRVAs that are more effective at supporting employee assistant use cases.
- Agentic AI and ITMs: Although they’re five to 10 years away from mainstream adoption, agentic AI and ITMs have transformational value. These innovations will affect employee experience and HR processes, but will require long-term investment to deliver value.
Priority Matrix for HR Technology, 2025
| 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 (June 2025)
Off the Hype Cycle
The following innovations were removed from the Hype Cycle this year:
- Blockchain in HCM was removed because blockchain adoption in HR technology has lagged expectations. It has failed to deliver use cases with perceived business value. Blockchain in HCM had the potential to streamline time-consuming and redundant processes, such as background verification and payment approval processing. However, issues with interoperability, governance and protocol standards have rendered blockchain in HCM obsolete.
- Employee onboarding aligns more closely with recruiting and talent management. It features in other Hype Cycles this year, including Hype Cycle for Talent Acquisition (Recruiting) Technologies, 2025, and Hype Cycle for Talent Management Technology, 2025.
- Responsible AI serves as an essential guardrail in AI initiatives within HR. However, the organizational practices for accountable and ethical AI development align more with the central AI governance program of the organization. Responsible AI features in other Hype Cycles this year, including Hype Cycle for Artificial Intelligence, 2025.
- Workstyle analytics aligns more closely with digital employee experience (DEX) tools. This innovation features in other Hype Cycles this year, including Hype Cycle for Digital Workplace Applications, 2025.
The following innovations were renamed this year:
- Coaching/mentoring applications was renamed to digital coaching applications.
- Talent analytics was renamed to people analytics.
- VR and AR in corporate learning was renamed to immersive learning (VR/AR).
- Workforce nudgetech was renamed to simply nudgetech.
On the Rise
EVP-EXTech for Retirement-Aged Workers
Analysis By: Sam Grinter
Benefit Rating: Moderate
Market Penetration: Less than 1% of target audience
Maturity: Embryonic
Definition:
Participation in the workforce is increasing among certain groups of retirement-aged workers. HR leaders must deploy technology to attract and retain this growing cohort of highly skilled and experienced workers, who have unique needs, motivations and expectations for work.
Why This Is Important
Increasing life expectancy, rising inflation, a shortage of skilled workers, and new-quality-of-life aspirations are expected to convince some employees who are past retirement age to stay in the workforce longer (see Early Retirement Increasingly Concentrated Amongst the Wealthy, IFS). While it is perceived that the youngest generation in the workforce has unique needs, expectations and motivations, this is certainly also true for their older colleagues. HR leaders must cultivate a unique employee value proposition (EVP) in order to attract and retain older workers, and leverage technology for the delivery of this strategy.
Business Impact
Employers that are successful in attracting and retaining retirement-aged workers will have a competitive advantage through the increased expertise, depth of knowledge and diversity of their workforce. Employee experience technology (EXTech) specifically configured and deployed to cater to the requirements of older workers will become the key tool in delivering a successful EVP at scale for this emerging cohort of workers.
Drivers
- Persistently low unemployment rates are highlighting the importance of highly experienced and skilled older workers for both doing the work themselves and as managers, supervisors and mentors.
- A period of high inflation has reduced the value of pensions, which means employees may need to work longer to ensure they have the savings necessary to support themselves and their families in later life. Many retirement-aged workers are also choosing to stay engaged in the workforce longer, because they find meaning and enjoyment in the work, especially if they are able to shift to reduced hours or a flexible schedule.
- Retirement-aged workers have unique needs. They are more likely to look for flexible working options, including shifting to contingent work rather than permanent employment. They have specific concerns and priorities related to their life stage, such as medical support for menopause or financial planning for retirement, that differ significantly from younger workers. The rate of disability also increases in retirement-aged workers, but often individuals experiencing these changes experience them so gradually they may not notice or request accommodation.
Obstacles
- A contextualized version of EXTech that delivers an EVP for retirement-aged workers is only just emerging. There are few vendors specifically targeting this market, so end users must make do with the tools at their disposal until such a time as a dedicated market emerges.
- Working for longer in later life isn’t an option for everyone. Declining health may stop someone who would otherwise financially benefit from working. As such, the trend of retirement-aged workers is not universal for all and for all types of work.
- There are practical considerations for employers when hiring retirement-aged workers. They may require flexibility in terms of working hours, have higher salary expectations, and may incur higher medical/benefits contributions from the employer. These factors will reduce the total demand for retirement-aged workers.
User Recommendations
- Evaluate the current age demographic of the workforce and the extent to which retirement-aged workers can positively impact the organization’s hiring and retention strategy.
- Conduct market research and focus groups to understand the specific needs, motivations and expectations of retirement-aged workers both within your workforce and in your broader labor market. Identify barriers that prevent retirement-aged workers from wanting to work for your organization as they age.
- Develop an EVP specifically tailored to the requirements and motivations of retirement-aged workers, including but not limited to differentiated job boards, pension contributions, health insurance, flexible working, provision for training and upskilling, and leadership/mentoring opportunities. This may also be expanded to workers nearing retirement age if there is a high attrition rate of workers taking early retirement.
- Configure existing EXTech or purchase and implement new EXTech to deliver on the EVP.
Sample Vendors
Akumina; Applaud; DaysToHappy; Flip; LumApps; Perkbox; YOOBIC
Gartner Recommended Reading
Nudgetech
Analysis By: Rania Stewart
Benefit Rating: High
Market Penetration: Less than 1% of target audience
Maturity: Embryonic
Definition:
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 suggestions typically surfaced in the flow of work. 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 gaining traction in people development, personal productivity and employee experience applications. As GenAI becomes embedded in HR tools, it is further expanding the reach, precision and adaptability of nudging — especially in use cases where desired behaviors are not automatic and require judgment, interpretation or agency of choice.
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 (e.g., nudging toward a more innovative, security-minded, or growth-oriented culture). Increasingly, application of nudgetech can be correlated to improved outcomes of retention and engagement.
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 GenAI-enabled assistants can particularly be improved by nudgetech in those applied use cases where the bar needs to be raised from assistant to personal AI 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.
- In a fragmented HR tech landscape, nudgetech can deliver cross-platform behavioral consistency by surfacing timely, context-aware prompts that reinforce desired outcomes across systems.
Obstacles
- Lack of definition: Nudgetech is not yet sufficiently far along to have a commonly accepted definition.
- Difficulty distinguishing nudges from notifications: 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.
- Uncertainty about AI integration: Many implementations do not incorporate AI-driven feedback loops that enable the system to learn which nudges work better for different 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.
- Insufficient choice for users: 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. There’s also the risk of perceived behavioral manipulation when the choice is diminished.
- Lack of trust and relevance: Nudges that fail to consider cultural sensitivity, preferences, appropriateness and timeliness undermine trust and autonomy, which are essential for effective behavioral change.
- Measurement challenges: Many organizations lack frameworks to assess nudge effectiveness beyond completion rates, limiting their ability to scale or refine interventions meaningfully.
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.
- 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
Microsoft; Perceptyx; Workday
Gartner Recommended Reading
Agentic AI in HR
Analysis By: Anand Chouksey, Harsh Kundulli, Stephanie Clement, Eser Rizaoglu
Benefit Rating: Transformational
Market Penetration: 1% to 5% of target audience
Maturity: Emerging
Definition:
Agentic AI is an approach to building AI solutions based on the use of one or multiple software entities that are classified, completely or at least partially, as AI agents. AI agents are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments. Agentic AI in HR will automate and optimize HR processes, improving employee and candidate experiences and driving organizational outcomes.
Why This Is Important
Agentic AI in HR, though in its very early stages, promises to transform work processes, boost efficiency, enhance decision making, improve experience and reduce human error. As it evolves, agentic AI will apply to orchestration of HR workflows in the form of AI agents, initially applied in the areas of talent acquisition, workforce management, compliance and payroll. As adoption grows, its transformative impact will expand, driving greater effectiveness and innovation in HR processes.
Business Impact
Agentic AI in HR will streamline self-service tasks and workflows, making them more efficient for employees, managers and HR administrators. This will increase satisfaction and free up time for core work tasks. By simplifying HR tasks that require orchestrating information across environments, it will lead to cost savings by allowing HR teams to focus on more value-added work, enhancing overall productivity and driving business value.
Drivers
- Executive leadership influence: Aspirational investments in agentic AI will be primarily driven by executive expectations for AI outcomes. The 2025 Gartner CEO and Senior Business Executive Survey reveals that 77% of CEOs foresee AI significantly impacting their industries within the next three years, with 62% believing it will define the next business era. This top-down pressure highlights the strategic necessity of adopting AI to shape future business landscapes. (See 2025 CEO Survey — The Year of Dynamic Capacity to learn more.)
- Transformative potential of agentic AI: Agentic AI holds the promise of revolutionizing HR processes and service delivery by increasing efficiency and automating routine tasks, leading to cost savings and enhancing the employee experience. While real-world results are still emerging, the potential for significant improvements in work processes and decision making continues to drive interest and investment in AI technologies.
- Vendor marketing and hype: Vendors are promoting agentic AI capabilities in the form of AI agents, generating significant hype around agentic AI’s potential in HR. This marketing surge raises awareness but can also inflate expectations, causing organizations to anticipate immediate benefits that may not match the current maturity of AI solutions.
Obstacles
- Maturity: Agentic AI is still developing, with AI agents often lacking accuracy and reliability. Without proper control mechanisms, concerns about human centricity and transparency arise.
- Data: Agentic AI’s effectiveness in HR will heavily rely on accurate and complete data. Inconsistent or incomplete data will hinder performance and decision making.
- Integration: Integrating agentic AI into HR systems is challenging due to fragmented HR technology. Without integrating multiple data sources across HR workflows, AI’s capabilities remain siloed, limiting impact and reducing ROI.
- Regulation: Ensuring compliance with privacy, data security, employment laws and AI regulations requires continuous oversight and adaptation to evolving legal standards.
- Trust/adoption: Concerns about human centricity and transparency will impact trust in agentic AI, hindering adoption. Gaps between expected benefits and actual outcomes dampen motivation, while resistance to changing workflows poses additional challenges.
- Skill gaps: Implementing agentic AI will require new skills like AI literacy both within HR and IT deparments.
User Recommendations
- Leverage emerging agentic AI: Explore evolving AI solutions by understanding workflows and data needs to maximize value. Collaborate with IT and vendors to align AI developments with organizational goals.
- Optimize automation: Identify areas where agentic AI adds value and determine processes requiring human intervention to ensure automation complements human skills.
- Drive business outcomes: Identify productivity zones enhanced by agentic AI and expand focus to address broader needs using an AI portfolio for enterprisewide benefits.
- Ensure interoperability: Work with IT and HR tech vendors to connect applications and data across systems, enabling agentic AI to deliver solutions across workflows, not just in isolated tasks.
- Develop a multiyear strategy: Create a long-term agentic AI strategy focused on AI-driven interactions. Implement user-centric features to boost satisfaction and perceived value.
- Enhance user experience: Orchestrate multiple agentic AI solutions with a unified interface for improved user experience, facilitating secure information access and efficient task completion.
- Foster a culture of innovation: Encourage innovation by developing change management plans for roles affected by AI. Introduce human behavioral experts to support the human-machine partnership.
Gartner Recommended Reading
Composable HR Application Frameworks
Analysis By: Sam Grinter
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, HR technology 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.
- Cloud HCM suites cannot respond quickly to new challenges by turning on/off the functionality provided by third-party vendors as connections between applications are often delivered via custom integrations. This is a 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 office, and for organizations and employees impacted by geopolitical conflict.
- Emerging integration platform vendors that leverage AI to build integrations are improving the integration capabilities of cloud HCM suites. This trend nudges the market for cloud HCM suites closer to CHAF.
- Investment in HR system integration to support the deployment of AI agents to serve as conversation user experience will act as a catalyst in bringing current HR technology ecosystems closer toward CHAF.
- 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 with the initial intention at deployment of serving 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 procuring and integrating multiple third-party vendors.
- 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 it, rather than a CHAF, due to the comparable difference in market maturity.
- There are not many off-the-shelf CHAF products on the market today. Therefore, to deploy a CHAF over the short term, an organization would have to self-build it.
- Lack of clarity about CHAF definition in the market may lead clients to misidentify CHAF with an augmented cloud HCM suite, or even an on-premises HR information system loosely integrated with a cloud talent management suite and a portal.
User Recommendations
- Evaluate the composability of the application portfolio by rating how easily HR and technology teams 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. Include questions asking for the provision and type of integration technology and partnerships with integration platform vendors.
- Introduce application composition platforms into the 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 CHAF by prioritizing a set of employee experiences and leveraging general-purpose application and data composition solutions. Start this work internally while vendor offerings mature.
Sample Vendors
ClayHR; Prismatic; StackOne
Gartner Recommended Reading
Mentoring Solutions
Analysis By: Chantal Steen, Laura Gardiner
Benefit Rating: Moderate
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Definition:
Mentoring technology solutions support mentoring programs and range from matching mentors and mentees to providing insights and direction on the outcomes of mentoring engagements. These solutions can support scalable expansion of mentoring programs beyond the high-touch use cases that require extensive HR intervention to facilitate matches and structure.
Why This Is Important
Mentoring applications improve mentoring program effectiveness by supporting the scaling of various types of mentoring (e.g., peer, 1:1 and group mentoring), thereby enhancing the realization of mentoring benefits like employee engagement, inclusion and skill growth. Automated mentor matching replaces an otherwise laborious application, selection and pairing process. It also supports the ongoing management and tracking of programs, helping to articulate the business value of mentoring programs.
Business Impact
Organizations are leveraging mentoring technology to provide greater accessibility to existing mentoring programs. The optimization of mentoring programs through technology provides a key way to develop workforce capabilities and build bench strengths. HR oversight is key to facilitate feedback loops to ensure that mentor-mentee relationships are working well, and that the mentoring program is fulfilling its purpose.
Drivers
- Automated matching: Mentoring programs are common across talent development however, organizations struggle and invest a lot of time in matching mentors and mentees. Mentoring solutions can automate this process helping organizations save time and resources.
- Personalized development opportunities: Employees increasingly expect more personalized, targeted development. Mentoring solutions are able to identify matches at scale beyond top talent without too much effort and potentially optimize costs.
- Improved reporting: Platform providers have started to optimize their reporting capabilities, which will make it easier for organizations to track some key components of their mentoring program’s success.
- Variety of mentoring types: Organizations have different purposes for mentoring; it could be upskilling, career development, engagement or linked to inclusion and belonging initiatives. Some platforms provide that level of differentiation in their mentoring solutions.
- Facilitated mentoring for higher impact: A key element of a successful mentor program is to equip participants with the right tools that enable them to have a successful mentor-mentee relationship. Some platforms provide support elements, such as conversation guides, which helps save time during initial stages.
Obstacles
- While most vendors support matching mentors and mentees, beyond this expectations for necessary capabilities are not uniform. This leads to variability in the market.
- Relatively few solutions provide robust program tracking and none allow complete automated management of a mentoring program. Therefore, technology can support an HR team’s ability to scale mentoring to larger employee groups but it cannot eliminate the need for resources allocated to program management.
- While reporting and analytics capabilities are improving, many tools do not collect data that enables HR to make decisions to continue, suspend or expand mentoring programs.
- While human capital management (HCM) suites may include mentoring capabilities, they typically support mentor matching only. In the absence of readily available data demonstrating the business value of more robust mentoring programs at scale, many HR leaders cannot justify the additional cost of a point solution that will further complicate the HR technology portfolio.
User Recommendations
- Assess whether mentoring technology, which only provides matching capabilities, will deliver enough value to warrant the cost. If not, build the case for investment on either a limited scope program or additional resources to manually manage the program.
- Establish whether individual vendor solutions can be quickly scaled to meet evolving business needs, given that mentoring programs can quickly expand, especially in large organizations.
- Identify outcome and impact measures for your mentoring program. Evaluate mentoring solution vendors on their ability to gather and share the data needed to demonstrate program impact.
Sample Vendors
Chronus; Eightfold AI; Gloat; MentorcliQ; Phenom; PushFar; Qooper; SAP; Together; Workday
Gartner Recommended Reading
Immersive Learning (VR/AR)
Analysis By: Travis Wickesberg
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
Virtual reality (VR) and augmented reality (AR) are different, yet related, technologies that support immersive learning. VR provides a computer-generated 3D environment (supporting both computer graphics and 360-degree video) that surrounds a user and responds to the learner’s actions in a natural way, either through 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
Most VR and AR use cases have shown promising results that exhibit 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:
- 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.
- Some vendors are starting to offer content and hardware packages that make it easier for organizations to test and learn.
- The emergence of low-code/no-code GenAI tools is making it easier and faster to develop personalized content and create more engaging interactions.
- VR is well-aligned to support complex scenarios in the military, healthcare (surgeries), aviation (flight simulations) and various safety training environments.
- Organizations are starting to adopt 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 have been slow due to a combination of technical maturity and pressure on budgets.
- Only a few vendors have invested in simple learning management system (LMS) integrations, but complete integration is still a challenge and often requires administration of multiple platforms.
User Recommendations
- Evaluate immersive learning as an emerging, effective and often less risky option to replace face-to-face training in selective circumstances where such training is resource-intensive, but not providing it presents increased risk.
- Leverage full-service vendors that have prebuilt, out-of-the-box, high-quality content and HMD packages.
- Run 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
Bodyswaps; Immerse; Mursion; PIXO; PTC; Roundtable Learning; Saritasa; Strivr; Talespin by Cornerstone; VR Vision
Gartner Recommended Reading
At the Peak
AI-Enabled Skills Management
Analysis By: Stephanie Clement, Travis Wickesberg
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
AI-enabled skills management is used to automate skills inference for people, content, work tasks, career paths and jobs. It applies natural language processing (NLP), knowledge graphs and other AI techniques to build a dynamic representation of skills data.
Why This Is Important
Dynamic skills data, driven by AI-enabled skills management, transforms how organizations manage their workforce and support talent processes. High-quality automated skills detection and assessment facilitate 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:
- Productivity and capacity utilization by prioritizing and distributing work assignments.
- Hire quality and internal mobility by matching candidates to roles.
- Strategy execution when used to support continuous strategic workforce planning activities.
- The alignment of reskilling and upskilling initiatives to personalize learning and career path recommendations, and inform their performance and total rewards processes.
Drivers
- Skills-based talent management: HR leaders are increasingly interested in applying skills data across talent processes. Skills data can be used to automatically tag and recommend learning content, and to find and connect with experts across teams more easily. Moreover, skills data helps to dynamically propose career development options, match talent to job opportunities in talent acquisition systems and internal talent marketplaces, and align compensation with employee skills and contributions.
- Pace of change: Planning and responding effectively to rapid changes in technology and other disruptions drive the need for greater visibility into skills. Skill footprints are changing in many professions, with AI and automation causing further uncertainty about the type of skills and roles that will be needed in the future.
- Tight labor markets: Organizations can benefit from tapping into AI-enabled and skills-based labor market insights to support recruiting and workforce planning efforts.
- Technology improvements: Graph techniques and technologies have improved in terms of availability and maturity. Increased capabilities in NLP techniques help to automatically detect and infer skills data in unstructured text, in multiple languages, within not only HR but also operational systems. In addition, more vendors are now employing a variety of AI-enabled skills management in their platforms.
Obstacles
- Job architecture or attachment to existing, less-detailed competency frameworks is lacking.
- Insufficient access to data about what work is done hinders better codification of skills. Data from HR systems is often low in detail. Internal data is often difficult to access and is inconsistent.
- The standards and languages to describe the same skill vary across contexts.
- Too many skills approaches from too many providers are available and there are difficulties in sharing data and models across systems.
- Variance in vendor use of skills metadata impacts the accuracy of AI inference.
- Dated enterprise job architecture impacts the accuracy of AI recommendations for learning and work.
- Trust in skills inference is low, with a desire to more tightly control the validation and assessment of skills.
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
Cornerstone (SkyHive); Draup; Eightfold AI; Gloat; Lightcast; Phenom; Reejig; retrain.ai; TechWolf; Visier
Gartner Recommended Reading
Digital Coaching Applications
Analysis By: Chantal Steen
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Emerging
Definition:
Digital coaching applications can complement and maximize the effectiveness of corporate coaching programs. They support coaches, learners and HR. In addition to connecting users for live coaching sessions, innovations in generative AI (GenAI) have introduced AI coaches that mimic elements of the leadership coaching experience, without a human coach. While not a replacement for human coaches, organizations are increasingly offering fit-for-purpose AI coaches.
Why This Is Important
Digital coaching applications enable optimal program matchup, flexibility when schedules are busy, execution of sessions, auxiliary resources and reporting/analytics. AI-enabled coaching brings new opportunities to meet the growing coaching demand across the broader workforce.
Business Impact
Coaching, especially at the leadership level, is an integral part of personalizing development. Due to the fast evolution of technology, these programs are expected to become more accessible for the entire workforce. While human coaches are still more versatile, AI coaches can bring opportunities when identified for a clear need at scale. Therefore, as adoption of AI coaches increases, the ability of coaching programs to change behavior will also increase.
Drivers
- Coaching programs are common at the leadership level. While AI coaches will not replace executive-level coaches, the broader workforce is seeking coaching advice and is open to working with AI coaches.
- Evolving workforce expectations see coaching as part of the career journey at all levels and potentially being more impactful in early careers.
- Digital coaching applications can provide access to a diverse network of coaches.
- AI coaches can augment human coaches to increase their capacity, or they can expand coaching programs to previously excluded audiences.
- Where virtual coaches tend to struggle with regional or language coverage, the evolution of AI capabilities makes some platforms more versatile in this regard.
- While organizations will leverage technology to manage coaching programs, they might require a pool of external coaches to supplement and observe the quality of the overall program.
- HR needs better metrics that display the connection between the time and cost invested, and the impact of these coaching arrangements and the overall program, without compromising confidentiality of coaching sessions. Many platforms optimize their reporting capabilities to support this need.
Obstacles
- Most vendors are offering AI coaches with a very specific scope, which does not always cover all the needs of the enterprise.
- For some coaching applications, the quality of coaching services and effectiveness of the corresponding vetting process of external coaches by the vendor still lack transparency.
- Reporting and analytics are improving across the market. However, many tools still do not enable HR to make decisions to continue, suspend or expand coaching programs.
User Recommendations
- Evaluate the scalability of vendor solutions by understanding enterprise needs for coaching. Given that talent needs are evolving fast, ascertain how flexible the vendor’s solution would be without harming the scalability of the program.
- Assess the content and advice offered by the vendor to drive quick program adoption, particularly for programs related to a specific topic (e.g., inclusive leadership and group coaching). This includes evaluation of AI-enabled recommendations and insights.
- Ensure that vendors have a multistage vetting process for hiring coaches that is aligned with your own quality bar. Get insights into their quality control process (including user ratings), to determine coaching quality, and make program adjustment and retention decisions.
Sample Vendors
BetterUp; CoachHub; EZRA; Landit; Perceptyx; Torch Leadership Labs; Valence; Wisq
Gartner Recommended Reading
Global Employer of Record Solutions
Analysis By: Nicole Paripurana
Benefit Rating: Moderate
Market Penetration: 5% to 20% 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, remain compliant and not hire people to perform core HR activities. Recently, the pressure to reduce costs, shortage of talent around specific skills and broader adoption of remote work have enabled organizations to expand their talent reach beyond existing or planned operations and associated legal entities.
Business Impact
With global EoR solutions, 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 globally 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
- 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, with several having implemented such a model to some extent. In recent years, organizations have continued the practice of borderless hiring, especially to acquire talent with niche skills (e.g., software developers in technology).
- Agile business expansion strategies: Many enterprises review or adjust their strategy at least twice every year. 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. Accommodations offered by EoR solutions can also aid the decision to withdraw from or downsize presence in specific markets, which otherwise can incur significant costs due to existing legal and administrative setup when an entity is owned.
- Expansion in use cases: Existing global organizations are challenged by the payroll or mobility aspects of a global workforce and the various compliance requirements. Several EoR vendors have expanded their offerings for services that touch subject matter areas such as payroll solutions, benefit partnerships, global mobility and guidance on transition to a client-owned entity. Transitions can be from the EoR supplier or a professional employer organization (PEO) vendor.
- Vendor solution collaborations: Large-scale EoR vendors are gaining more presence and partnerships with mature HCM vendors, combining areas that reduce integration risk and facilitate one another’s technology and strategies.
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 commonly underestimated during planning. Tightly connect business planning and workforce planning to spot cases where a global EoR needs to be considered early.
- Consider the gradual transition of global EoR usage to establishing your own legal entity. Most often, utilizing the services of a global EoR provider is a temporary step, and considerably costly, 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). Clearly indicate 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 provider offerings. 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; Helios Global Payments Solutions; Mercans; Neeyamo; Omnipresent Group; Oyster HR; Papaya Global; Velocity Global
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 experiential development opportunities, thereby democratizing access to development and mobility. They provide personalized recommendations aligned with workers’ unique skills and experiences. Opportunities include gigs, 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
As organizations adopt skills-based talent management and infuse skills into all of their talent decisions, ITMs provide a path to skills transformation while also meeting worker demands for increased mobility and development opportunities. 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
Internal talent marketplaces help:
- Improve internal mobility by providing workers with curated recommendations for new skills and positions.
- Understand workforces through a new lens focused on the skills needed, rather than on 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. The team, project and product leaders of organizations 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 dynamic organizational models and project- or gig-based work.
- Talent hoarding. Managers may discourage team members from seeking outside opportunities, as they only see their team talent engaging in work for other teams and not their own.
- Lack of psychological safety. Workers may not be confident enough to bid on projects or gigs for fear that they will not be selected.
- Limited readiness for skills-based talent management due to outdated job architecture that does not align skills to roles.
Data challenges include:
- Limited data sources regarding knowledge, skills and worker experiences. HR data alone does not yield rich insights.
- Lack of metadata or variance in its use that can lead to poor fit matches and recommendations.
- Lack of organization- or industry-specific granular skills for better matching.
User Recommendations
- Pilot ITMs within business units that use adaptive or agile organization models.
- Assess organizational maturity and implementation readiness by investigating foundational cultural values and behaviors that support talent mobility and development. Identify existing development programming to align the ITM with organizational strengths and prioritize these areas in implementation.
- Make an inventory of the 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 vendors that integrate with other skills technologies to avoid skills living in a silo.
- Market the ITM as it gets adopted in your organization as an essential, growth-focused part of your differentiated employer brand.
Sample Vendors
365Talents; Cornerstone; Eightfold AI; Fuel50; Gloat; Oracle; ProFinda; SAP; Workday
Gartner Recommended Reading
Labor Market Intelligence
Analysis By: Emi Chiba
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 intelligence is essential to making strategic, data-informed workforce planning decisions at scale. LMI provides 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 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
- Skills-based talent management: Organizations continue to place skills at the center of their talent management decisions. LMI solutions strengthen a skills-based approach by providing valuable insight into external skills availability.
- A need for diverse, external 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. Because of strategic workplace planning solutions’ high entry barrier, many organizations supplement their existing operational workforce planning solutions with LMI platforms.
- Competitive labor market: 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: LMI alone will not solve workforce planning questions, although it can provide a more complete picture of skills and labor availability. LMI solutions should be considered part of a larger orchestration of people, process and technology coming together to support a recurring, monthly/quarterly/semiannual discipline.
- Lack of relevant data: Not all jobs or industries may be represented in publicly available data. Therefore, some can be difficult to track.
- Limited support for APIs for diverse data sources: Some LMI vendors lack APIs to transfer and combine internal data with external insights.
- Limited language support: Language support outside of English may be limited, or the terminology 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; Lightcast; LinkedIn; People Data Labs; TalentNeuron; Wilson (Claro Analytics)
Gartner Recommended Reading
AI in HR
Analysis By: Jackie Watrous, Stephanie Clement, Eser Rizaoglu
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Adolescent
Definition:
Artificial intelligence (AI) uses advanced techniques like machine learning, deep learning and prescriptive analytics to identify patterns, predict outcomes, make and execute decisions, and generate content. AI augments human decision making, and automating routine and nonroutine tasks. Within the HR domain, we see AI used to provide predictive insights, execute tasks, create and personalize content, and offer a conversational user interface.
Why This Is Important
AI in HR is embedded in or acquired as an add-on to a broad range of HR applications. A new generation of solutions are AI-native; they are architected from the data and AI models first, before specific workflows and user experiences are designed. Existing solutions have also introduced AI capabilities into their products. This rapid delivery has transformed user expectations when interacting with HR tech, which, without AI, will increasingly fail to achieve growing end-user expectations.
Business Impact
AI enhances the HR function by automating recruitment processes, personalizing employee onboarding and improving decision making through data-driven insights. It boosts employee engagement and retention by monitoring sentiment and suggesting initiatives, while optimizing learning and development with tailored recommendations. Lastly, AI streamlines administrative tasks and utilizes predictive analytics to proactively address workforce challenges, making HR more efficient and strategic.
Drivers
- CHROs are leveraging AI to meet pressures to enhance organizational impact, improve employee experience and boost operational efficiency while meeting demands for flexibility, personalized support and equity.
- The surge in interest in generative AI (GenAI) and AI agents has accelerated investment in AI across HR processes, focusing on conversational interfaces, summarized insights and workflow automation.
- AI is increasingly pervasive, with HR tech providers investing in AI applications within their products for over five years. These embedded capabilities are more accessible than third-party solutions but may lack depth.
- Individual models, like algorithms predicting employee queries in conversational interfaces, may seem minor, but their combined effect is significant. This transformation changes how HR handles data, delivers services and supports businesses with advanced insights.
- Continued vendor investments in embedding AI capabilities within HR platforms is lowering adoption barriers, making it easier for organizations to access and experiment with AI.
Obstacles
- Overhyped vendor claims inflate AI expectations, leading to rushed decisions and marginal returns.
- The evolving regulatory landscape complicates investments, especially in recruitment, requiring transparency and fairness.
- Bias is inevitable. AI explainability is vital for bias mitigation, especially in decisions impacting livelihoods or peer positions.
- Organizational readiness, AI literacy and change management support are often lacking for effective AI adoption, management, tracking and monitoring.
- AI alters task execution, shifting roles and required skills.
- AI use cases are frequently siloed in separate HR systems, limiting integration and hindering cross-functional workflows. Ideally, skills and career recommendations should be shared within learning systems to enhance career development suggestions.
User Recommendations
- Focus on AI use cases with high business value and feasibility. AI solutions are not one-size-fits-all.
- Develop your HR teams’ AI agility to assess AI use cases and vendor offerings. Consider AI advocates and product owners for oversight.
- Collaborate with IT to create an HR AI strategy, evaluating features, data, security, integration and maintenance needs.
- Prioritize foundational data readiness by improving data quality and consistency across core HR systems.
- Scrutinize vendor documentation on ethical usage, enforcement, vulnerabilities and weaknesses.
- Establish robust data and content guardrails, especially for GenAI in content creation or conversational tools like virtual assistants.
- Conduct fairness and accuracy tests during and after AI implementation, with governance to monitor usage and impact.
- Be ready to modify processes based on AI inclusion, identifying changes and planning training accordingly.
- Ensure proper change management to build trust, educate users and promote adoption.
Gartner Recommended Reading
Sliding into the Trough
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 employee experience technology (EXTech) is an approach that delivers distinctive 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. Yet, employee experience technology initiatives largely focus on desk-based workers. Tools that offer a targeted experience for frontline workers and help boost their productivity enable operations and HR leaders to reduce high attrition rates while enhancing overall efficiency.
Business Impact
- Frontline jobs face extreme stress and burnout. Improving daily business application experiences could reduce stress and improve retention. Frontline worker EXTech allows for proactive monitoring of frontline worker fatigue or burnout, adjusting approaches as needed.
- Frontline worker EXTech could consolidate over 10 different daily applications, replacing inefficient homegrown portals, making it an opportunity to explore a superapp platform approach in the future.
Drivers
- Scheduling flexibility: Frontline workers desire flexibility and control 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, accrue additional paid time off (PTO) hours and use them when needed.
- Benefits and recognition delivery: 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.
- Intranet packaged solutions (IPS): 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. Some of these could evolve into superapps, allowing the creation and publication of custom miniapps tailored to specific worker use cases.
- Learning for frontline workers: Frontline workers in retail, healthcare and hospitality are increasingly utilizing dedicated learning solutions to access job-specific learning modules. This trend is further amplified by the introduction of generative AI (GenAI). In healthcare, for instance, GenAI aids physicians by generating timely medical notes from patient conversations and integrating them into clinical staff task applications. This reduces the administrative burden and streamlines patient hand-offs, thereby mitigating potential safety risks. Additionally, some organizations have recently begun utilizing augmented reality (AR) guidance systems to more efficiently and consistently assemble kits for consumer use.
Obstacles
- The frontline worker experience initiative often lacks ownership at executive levels. Some initial projects 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.
- 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.
- Many industry-specific applications are crucial for the frontline worker in the short term, but usage decreases over time due to lack of improvements.
- Many frontline worker applications highlight notifications for open shifts or immediate tasks that require attention. However, they often fail to identify avenues for long-term career growth, which are proactively offered to desk-based employees. This lack of support does not contribute to reducing attrition rates.
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.
- Establish a criterion for minimal clicks or time spent to complete a transaction when evaluating vendors, such as: “The process should not exceed two minutes or require more than five clicks/form parameters for moderately complex use cases.”
- 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.
- Plan for the future, as frontline worker teams may transform into collaborative human-machine teams, with AI robots managing many repeatable tasks. Chief HR officers are investing in a roadmap to reskill frontline workers, encouraging them to creatively leverage hardware innovations. Prioritizing applications that support this initiative will be crucial.
Sample Vendors
Beekeeper; Blink; Flip; FYLD; Site Diary; SparkPlug; WorkJam; Workstream; Wyzetalk; YOOBIC
Gartner Recommended Reading
Generative AI in HR
Analysis By: Eser Rizaoglu, Hiten Sheth, Jackie Watrous, David Bobo
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 continues to receive a lot of hype, with high expectations for driving greater efficiency and effectiveness of the HR function. HR leaders review GenAI use cases along with traditional AI, and access many capabilities through their HR technology providers. Whether GenAI is used for text, image, video or sound generation, applications are being adopted across most HR processes and could be set to transform how work is done in HR over the longer term.
Business Impact
GenAI in HR is primarily focused 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 that deliver conversational AI. A few HR subfunctions, such as learning and development (L&D), extend GenAI 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, especially in HR service delivery, recruitment and L&D.
- Agentic AI is a top driver of a GenAI value proposition this year due to automation benefits and combining GenAI with other techniques.
- HR technology vendors are embedding GenAI use cases in their products and will continue to do so. This allows HR functions to easily consume GenAI capabilities.
- Many IT functions have rolled out GenAI capabilities for their enterprise or are exploring enterprisewide GenAI solutions (e.g., Microsoft Copilot or ChatGPT). This extends HR’s access to GenAI capabilities beyond what their HR technology vendors are rolling out.
- GenAI deployment through virtual assistants has become a key focal point for both HR leaders and HR tech vendors, because they simplify HR processes and information for employees, managers and new HR team members. GenAI makes information more accessible and decreases the amount of time needed for nonspecialists to complete HR tasks, thereby improving user experience for HR service delivery.
Obstacles
- GenAI in HR is still a maturing solution, so best-practice approaches still don’t exist. This includes barriers to implementation, solution accuracy, employee resistance and regulatory uncertainties. Until further clarity is achieved, broad-based utilization within HR will be limited, resulting in lower near-term ROI from investments.
- Currently, GenAI utilization is siloed, with use cases being task- and platform-specific. Without greater HR data standardization and orchestration of multiple GenAI tools, fragmented implementation will result in greater complexity of the HR technology stack, so the true potential of GenAI will remain limited.
- Typically, the perceived expectations of GenAI use cases and value drivers differ from actual reality, which leads to deflated motivations.
- GenAI can produce inaccuracies and hallucinations, so it requires customization, governance and human supervision.
User Recommendations
- Develop an AI HR strategy aligned with the enterprisewide AI strategy and HR functional strategy, outlining HR’s vision, governance approach, and risk and ethics considerations.
- Introduce an HR AI product lead to navigate HR’s approach to GenAI utilization.
- Develop a stance on how GenAI can positively impact your HR function. Determine initial use cases that improve existing processes that can benefit from enhanced text generation, while meeting HR’s risk appetite by having a human-in-the-loop approach.
- Plan for GenAI’s impact on HR staff and co-create augmented roles while upskilling or reskilling your HR staff to effectively use GenAI.
- Prioritize vendors who promote responsible deployment of models by publishing and enforcing usage 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 that 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 integration and extensibility capabilities for HR technology solutions. PaaS allows customers to integrate with other products and gain customer-specific functionality, leveraging the same toolset as their HR technology vendor.
Why This Is Important
PaaS provides customers with bidirectional integration from HR technology to third-party and in-house-built systems. It also allows customers to gain more custom-made workflows and extended functionality that behaves and looks as if it were developed by their vendor.
Business Impact
PaaS in HR technology helps end customers get a more complete solution that integrates with other applications and/or delivers extended functionality. Customers gain the benefit of a more complete solution with simplified integration and implementation, and less risk and ongoing management overhead than in the on-premises world.
Drivers
- PaaS can be more economical than buying and integrating a third-party point solution, especially in cost-constrained times.
- AI and GenAI (and likely agentic AI) PaaS services are becoming available to customers.
- PaaS provides support for organization-specific process journeys.
- Implementation partners are increasingly interested in and offering more PaaS tools.
- HCM suite customers are increasingly using PaaS.
- Marketplaces that offer PaaS extensions are beginning to offer more solutions.
Obstacles
- Regulations surrounding AI, such as the EU AI Act, along with a lack of organizations’ readiness to leverage AI in their HCM processes.
- Limited end-customer understanding and internal resources to manage PaaS capabilities.
- Additional licensing implications if customers leverage AI and/or GenAI services in their usage of PaaS capabilities.
- Limited availability of skilled PaaS implementation and/or consulting resources.
- Cost of using and managing extensibility options, since most PaaS offerings are sold as ongoing subscriptions in addition to the base cost of the HR technology offering.
- PaaS does not offer unlimited freedom to customers. Most vendors have established guardrails and limitations that curtail some complex customer requirements.
User Recommendations
- Check whether your vendor’s PaaS adheres to any organizational data processing and security requirements, especially when leveraging AI services that consume, process and return actions or data.
- Use PaaS to support processes that are not possible from configuring your SaaS application and/or when a third-party point solution is not ideal.
- Plan for ongoing regression testing and evolution. Budget for ongoing training and certification of internal resources.
- Determine whether using PaaS will be temporary (one to three years) or ongoing (more than three years) by comparing your needs with the vendor’s roadmap. Annually review each use case to determine whether it should be maintained, evolved or retired.
- When using an implementation partner, ensure sufficient proficiency with any resources used. This is because most vendors have separate certifications for their PaaS offerings.
Sample Vendors
Cegid; Cornerstone; Darwinbox; Dayforce; Oracle; SAP; ServiceNow; UKG; Workday
Gartner Recommended Reading
Digitally Enabled Diversity, Equity and Inclusion
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 bias mitigation — across people, processes and daily work.
Why This Is Important
In today’s high-uncertainty environment around DEI, particularly in the U.S., most CHROs are assessing and adjusting DEI strategies. Yet, employees remain concerned about the impact on DEI and psychological safety. HR leaders must balance mitigating risk with finding opportunities for sustained impact. Digitally enabled DEI — technologies positioned as integral to DEI program design — boosts the scalability and efficacy of DEI, drives data-informed insights and embeds technology capabilities.
Business Impact
Human-centric work — combining connection, collaboration and empathy — significantly improves employee performance and intent to stay. Organizations with both diversity and belonging see a significant workforcewide boost in engagement and in performance, compared to organizations with diversity alone. Digitally enabled DEI can be seen as the amplifier — or scaling mechanism — to deliver human-centric-work design that advances these talent outcomes.
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 job levels, functions and regions
- 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 (“glocalization”)
- Reduce bias across talent sourcing and selection activities through the use of AI
- Broaden the funnel of potential qualified candidates through diversified outreach
- Tailor candidate experience toward a more universal design that creates fair opportunities for employment across a broad candidate pool
- Proactively address (or at least detect and fix) pay inequity
- Comply with the growing body of pay transparency legislation in the U.S., the EU and elsewhere
- Develop inclusive leaders across all levels of the organization
- Reinforce universal design of the digital workplace, ensuring accessibility to all employees, including employees with disabilities and neurodiverse individuals
- Enhance emotional proximity for those employees who have limited 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 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. Many organizations consider their DEI technology strategy to be lower in maturity compared to other aspects of their DEI strategy.
- 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. Effective organizations increasingly blend local variations in their DEI programs to fit local objectives and culture (for example, 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
Flexible Earned Wage Access
Analysis By: Ron Hanscome
Benefit Rating: Moderate
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
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
Ongoing disruptions and economic uncertainties, due to tariffs, inflation, war and extreme weather events, continue to affect employees globally. Thus, many hourly paid workers have little financial reserve and cope with unforeseen expenses by resorting to various expensive, short-term borrowing options. FEWA continues to serve 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. 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 market adoption is 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 low- and mid-level salaried staff dealing with unplanned expenditures.
- Customer adoption is manifesting in several EMEA countries, with the U.K. leading the way. FEWA may be especially attractive in Europe, with its popular monthly pay cycles. This increases the employee appetite for more flexibility in accessing their earned wages.
- Adoption is also increasing 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 including a certain number of weekly or monthly FEWA transactions. Others reduce cost to both employer and employees by taking a percentage of the paycard transaction fees charged to merchants.
- FEWA is providing 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 the 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. Adoption is primarily in the U.S. and U.K. markets, along with some uptake in ANZ. The timeline for a robust, competitive market in other countries is several years away. This limits applicability if an employer has hourly workers in multiple countries and wants to make this capability available to all.
- Legal, compliance and processing requirements for FEWA vary substantially by country, and even by state or jurisdiction in complex countries such as the U.S.
- Functional maturity of FEWA varies, particularly where it has been recently deployed as part of an HCM suite.
- FEWA administration 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; at worst, it 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 FEWA’s 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 Finance
Gartner Recommended Reading
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 (either integrated with other HCM applications or natively provided) that work with multimodal human prompts (usually text and/or voice prompts) through a conversational user interface. HRVAs assist employees and HR staff in completing HR tasks or requests. This multimodal human-machine interaction takes place via smartphone, tablet, computer or other specific device.
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 and reducing the time it takes to get support. Generative AI (GenAI) has boosted the performance of HRVAs and opened doors for new GenAI-native solutions. This has intensified competition, compelling vendors to develop unique capabilities and focus more clearly on specific use cases.
Business Impact
Virtual assistants (VAs) are becoming an important layer for HR functions — particularly gaining maturity in recruiting, HR service management, benefits enrollment, onboarding and HR functional insights (e.g., talent analytics insights). HRVAs 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
- GenAI and rising interest in AI agents are major drivers for hype and interest in these solutions. Many vendors have incorporated GenAI in HRVA platforms.
- Leveraging contextual data through techniques like prompt engineering via retrieval-augmented generation (RAG) has led to new methods for developing GenAI-native HRVAs. Generally, these new solutions are considered more effective for supporting employee-assistant use cases in intelligent document processing — such as extracting information, summarizing documents and utilizing insight engines for conversational search and information retrieval.
- 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 have built comprehensive HRVAs. Many of these VAs can also be deployed as a wrapper or as the underlying model in an orchestration framework, thus opening up possibilities to coexist with other VAs.
- HR tasks can be time-consuming and confusing for employees, managers and new HR team members. HR continues to build their own virtual assistants to achieve anticipated productivity gains and reduce the demand on HR for delivering self-service options. 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 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 and orchestration of automation tools to execute user requests.
- 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 with systems often 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.
- 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.
User Recommendations
- Decide which VA approach is suitable for your organization — a centralized approach of deploying an enterprisewide conversational AI or an HCM-contextualized VA approach. A centralized, platform-based approach provides consistency in chatbot operations and conversational management. HCM-contextualized VAs will offer a deeper understanding of HR processes.
- Determine the HRVA use cases (for example, shift reminder, learning content suggestion) that will result in maximum benefit to employees.
- 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.
- Currently, prompt engineering via RAG does not offer differentiation. Instead, focus on the specific guardrails platforms can provide for GenAI, particularly in response validation and vendor-specific large language models.
Sample Vendors
Acuvate; Amelia; Espressive; Leena AI; ServiceNow; Simpplr (Socrates.ai); The Bot Platform
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 greater 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 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 have higher levels of adoption of learning and development opportunities often witness increased engagement, which then translates to less voluntary attrition and greater productivity.
Drivers
- Employees are expecting better learning experiences with more personalized and social learning functionalities.
- 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 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. A growing number of LMS vendors are building in LXP functionality, as customers don’t want to have to buy an LMS from one vendor and an LXP from another. This changing landscape is causing customers to reconsider, adding competitive pressures to specialist solutions.
- 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, conduct proper change management communications and make these 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 human capital management, 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
Digital Adoption Platforms
Analysis By: Melissa Hilbert, Stephen Emmott
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Adolescent
Definition:
A digital adoption platform (DAP) provides in- and cross-application guidance for employee- and customer-facing applications. It drives adoption, proficiency and engagement. It supports digital transformation by streamlining and accelerating how employees or customers learn and engage with technologies. DAP analytics provide actionable insights to improve experience and adoption/utilization, boosting the ROI of applications.
Why This Is Important
DAPs improve user productivity and efficiency by reducing digital friction and increasing user engagement with applications. 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 adoption and retention and spur growth. Use cases include onboarding, technology adoption and use, change management, and process efficiency.
Business Impact
DAPs provide high ROI for organizations looking to improve application adoption for employees and customers, including:
- Reducing employee and customer onboarding and training costs
- Speeding new-hire time to productivity and customer time-to-value
- Eliminating change-management-related training
- Reducing support tickets
- Improving user engagement, proficiency and efficiency
- Enabling continuous improvement via usage analytics and insights
- Improving customer satisfaction (customer satisfaction ratings, Net Promoter Score, etc.)
Drivers
- DAPs are relevant for any organization in any vertical and for the entire tech stack.
- Relevant for “external” use cases for vendors who sell software solutions to enable end users where user adoption and usage are critical to customer value realization, renewals and expansion.
- 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 drive actionable insights to improve the user experience and maximize ROI from application investments.
- AI enhancements and automation speed the completion of workflow in and across applications.
- DAPs offer their own AI assistants or integration with third-party AI assistants to improve workflow execution.
Obstacles
- Application cost must be tied to ROI, and some vendors utilize a per application model (including varying pricing for application complexity) and per user pricing model, which significantly increase costs when deploying at the functional or enterprise level.
- 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.
- Governance and DAP roles for guidance, content creation and maintenance are required, further increasing costs, especially as DAP scales to function or enterprise. Organizations that do not develop a partnership between stakeholders — including a dedicated DAP team, enterprise application leaders, product and customer success — struggle to scale.
- AI assistants cross into DAP features but come with the need for developer skills.
User Recommendations
Seek DAPs to help employees and customers adopt technology by removing digital friction in performing tasks that are difficult or infrequent with high impact and where business processes change frequently and new feature adoption is important. If your customers’ end users have low adoption correlated to renewal, seek a DAP.
To evaluate DAPs, organizations should:
- Create a rollout plan by functional area to incorporate DAPs by prioritizing high-impact applications such as CRM, ERP and human capital management, digital workplace or client-facing applications across the entire tech stack or product portfolio.
- For employees, evaluate all applications in an employee’s work hub by prioritizing impactful applications.
- For the external use case, identify top customer use cases by aligning to features and workflows in your product.
- Design a governance plan by including DAP roles or reallocating learning and development or subject matter expert roles to support content and a rollout across the organization.
Sample Vendors
Apty; Knowmore; Lemon Learning; myMeta; Pendo; SAP (WalkMe); tts; Userlane; Whatfix
Gartner Recommended Reading
Employee Productivity Monitoring
Analysis By: Brent Cassell, 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, timespend, 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
Clients remain interested in employee productivity monitoring as the return to office (RTO) debate persists and more companies have adopted increasingly aggressive strategies. 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 executives 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 because they measure value creation as well as efficiency. Further, they enable leaders to better understand the context in which that data was collected.
- 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.
- In some cases, interest in employee productivity monitoring is driven by a desire to ensure employee compliance and 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 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, with specific controls implemented to limit access to insights generated from the data and 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 help employees understand how this will make them more productive.
- 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; HubStaff; Insightful; Prodoscore; ProHance; Sapience Analytics; Teramind; Time Doctor; WorkMeter
Gartner Recommended Reading
Hyperautomation in HR
Analysis By: Ranadip Chandra
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Adolescent
Definition:
Hyperautomation in HR involves a convergence of technologies used in coordination, including robotic process automation (RPA), intelligent document processing (IDP), business process automation (BPA), process mining, low-code application platforms (LCAP), integration platforms as a service (iPaaS) and test automation. 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 automate workflows and improve efficiency and reliability, particularly across transactions and workflows subject to manual data entry errors or delays, and also traverse multiple systems such as payroll, workforce management, recruitment and service operations. When used strategically, hyperautomation can accelerate organizational performance and reduce operational costs.
Business Impact
Hyperautomation in HR positively impacts service delivery effectiveness by reducing error rate and optimizing resource allocation by increasing overall staff availability. Positive effects on business operations include increased efficiency, scalability and reliability. HR technology teams are increasingly leveraging business metrics enhanced by hyperautomation, including straight-through processing (STP), increased transaction rates and reduced risks, to elevate executive-level visibility.
Drivers
- Hyperautomation in HR interests high-transaction domains and has rapidly changed from being optional to vital due to the relentless demand toward digital business models.
- Hyperautomation in HR can boost organizational efficiency and effectiveness, which enables organizations to operate in an environment with high volatility, uncertainty, complexity and ambiguity (VUCA).
- Human capital management technology megavendors (such as Oracle, SAP and Workday) have invested in an end-to-end process automation platform that comprises a growing set of hyperautomation-enabling technologies. 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 Salesforce and ServiceNow) are focusing on improving HR workflow hyperautomation capabilities within their platforms to address employee and manager demands.
- RPA in payroll is driving some payroll processing alerts, along with data migration utilizing RPA to speed up data validation and implementation.
- Hyperautomation is increasingly becoming the foundational approach for building HR virtual assistants (HRVAs) and AI agents, with a strong focus on their development. Consequently, there is a greater emphasis on utilizing hyperautomation in the background.
Obstacles
- HR has a fragmented HR technology stack with unstandardized processes and data. Fragmented solutions prevent scalable hyperautomation across the HR function, leading to siloed automation tools used at task level and resulting in reduced HR productivity potential.
- There is no single vendor or technology capable of independently enabling a hyperautomation initiative. The highly fragmented and overlapping technology markets have led to complex architectures, overspending and a lack of enterprise orchestration.
- HR teams’ limited expertise with combined integration, business process management, RPA and other tools will be one of the biggest barriers to effective 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
- Architect and holistically map multiple, concurrent HR technology initiatives, rather than stand-alone administrative task automation, to maximize hyperautomation success.
- 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.
- Provide automation tool guidance (for example, RPA vs. iPaaS vs. LCAP) to engage citizen developers. Well-governed citizen development has become more popular as a method to alleviate automation pipeline bottlenecks and empower business innovation.
- Engage experts from other parts of the organization, and build multidisciplinary fusion teams, to maximize use of best-of-breed tools (such as BPM, PaaS and AI integration). HR’s adoption of enterprise architecture principles and cross-functional partnerships will enhance value beyond short-term cost-cutting initiatives.
Sample Vendors
Automation Anywhere; Celonis; IBM; Microsoft; Pega; SS&C Blue Prism; UiPath
Gartner Recommended Reading
Unified Multicountry Payroll
Analysis By: David Bobo
Benefit Rating: Moderate
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
Unified multicountry payroll (MCP) is an approach to deploying an integrated solution by an organization 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 MCP 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
A unified MCP strategy removes process efficiency bottlenecks and gives an opportunity to add uniformity to the service metrics. A unified data reporting layer ensures visibility into payroll costs, such as overtime allocation and compliance breach settlements, helping with cost allocation and workforce planning. A unified MCP 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 partnerships with large MCP 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 MCP solutions have launched an updated centralized compliance library to help customers set up operations in a new country or keep pace with changes in the country-specific regulations for existing locations.
- Easier vendor maintenance through consolidation results in 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 centers of excellence to consolidate payroll operations.
- MCP BPO vendors are replatforming their native payroll technology to improve integration, reporting and analytics, and to reduce dependency on legacy payroll engines. They have also gained improvements in integration/API capability and help desk support.
- Over the last two to three years, there has been an attempted expansion of traditional EOR and global contractor providers into MCP, and vice versa, to provide a total workforce payments platform.
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 MCP 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.
- Organizations operating in a decentralized, localized fashion often see reduced benefits from a unified MCP strategy due to the variance in business processes and leadership reluctance to force a consolidated approach.
User Recommendations
- Develop a payroll transformation strategy that is suitable for your organization. Prioritize execution based on your geographic footprint of providers, volume of workers and existing/planned HR application investments.
- Evaluate vendors to expand localization. If your organization has plans to expand its geographical footprint in the next five years, think ahead and evaluate suitable providers that can support this journey.
- 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; CloudPay; Deel; EY; Mercans, Neeyamo; Papaya Global; Ramco Systems; SD Worx; Strada
Gartner Recommended Reading
Voice of the Employee
Analysis By: Ron Hanscome
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 ongoing volatility, uncertainty, complexity and ambiguity (VUCA), many still use an annual survey as the primary means of gathering 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 potentially 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), employee value proposition (EVP), worker performance and productivity
Drivers
- Organizations are responding to the ongoing talent crunch and increased worker burnout and 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 workers, even with the uneven adoption of hybrid work environments and RTO mandates.
- Many HR clients use an annual survey as a feedback baseline but have augmented it with some form of pulse measurement to increase the frequency of feedback and reduce lag between feedback, analysis and action.
- Organizations are also interested in how providers are applying GenAI and HR virtual assistants (HRVAs) to more efficiently and accurately summarize employee sentiment themes and deliver actionable recommendations.
- HR and C-suite leaders 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 of the employee experience.
- 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, focus group, 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 longstanding, internally built surveys may find it hard to transition to VoE solutions that require adherence to the vendor’s structured 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 timeframe will likely face integration challenges, user interface disparities and disparate analytical/AI 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 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 and measurement methods, and technology. Assess how well the provider applies techniques such as natural language processing, GenAI, HRVAs and event-triggered listening.
- Scrutinize provider integration roadmaps if all or part of VoE functionality results from 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; 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 employee experience (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 2025.
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 improve EX outcomes such as employee productivity, motivation and engagement, thus aiding business 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 five 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 and improved UX overlay to give them time to swap out their on-premises components as time and resources permit.
- Those early in their HCM suite journey who have realized that its 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 who understand the limitations of their suite, and have completed their initial augmentations. These are evaluating existing EXTech orchestrators and overlays versus using various IT tools to internally build their own solutions.
- Leading-edge organizations (less than 5% of the market) that are taking a holistic approach to EX, looking to build journeys that cut across traditional enterprise process silos (e.g., HR, finance, operations) via agile fusion teams.
- Early adopters who are exploring initial vendor AI agent offerings as an orchestrator to deliver employee experiences in a more conversational form.
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.
- 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 predominantly for desk workers in remote or hybrid environments by rendering HR processes and tasks within a new work hub, thus increasing the connection of employees to others.
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 is 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 four work modes framework to prioritize EX needs. See The 4 Work Modes Framework to Enhance the Digital Employee Experience.
- 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
Applaud; Firstup; LumApps; Microsoft; Nintex; Oracle; SAP; ServiceNow; Unily; Workday
Gartner Recommended Reading
Continuous Employee Performance Management
Analysis By: Laura Gardiner
Benefit Rating: Moderate
Market Penetration: More than 50% of target audience
Maturity: Early mainstream
Definition:
Continuous employee performance management (PM) systems 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, and 9-box features. Interest in applying generative AI (GenAI) to PM continues to grow but risk concerns hinder adoption.
Why This Is Important
The goal of continuous PM is to improve 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 reduce managers’ perceived work burden of performance activities.
- As continuous PM practices mature, organizations seek technology that understands the role of continuous PM in a strategic talent management function.
Obstacles
- 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.
- As AI adoption continues to grow and reach talent management processes, the market is in transition and the vendor landscape is changing. Also, many employees remain skeptical of AI-driven performance feedback.
- 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.
- Limited technology options exist for large and technologically mature companies looking to provide a consumer-grade experience that handles their internal complexities.
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; Culture Amp; Lattice; Oracle; Profit.co; Quantum Workplace; SAP; Workday
Gartner Recommended Reading
Next-Gen WFM
Analysis By: Josie Xing, Ron Hanscome, Kelsie Marian, Sam Grinter
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 scheduling optimization and skills management in WFM. Next-gen WFM is the result of the following trends impacting the market: skills management, process automation, employee experience, generative AI and AI agents, 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, 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 in. 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, real-time data, reduced compliance risks, improved employee experience, reduced manager time spent 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.
- Increasingly stringent labor regulations are driving organizations to adopt advanced WFM solutions to ensure compliance and security.
- 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.
- The rapid evolution of technology, such as AI, is pushing organizations to upgrade their WFM systems to leverage these technology advancements for better efficiency and decision making.
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 to further growth and realization of next-gen capabilities. This is especially true for organizations in industries like aviation and retail, which require the system to address industry-specific requirements.
- 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.
- Employees and managers may resist adopting new WFM systems due to comfort with existing processes or fear of increased monitoring.
User Recommendations
- Assign a senior stakeholder to prioritize and oversee WFM investment.
- Unlock new business value by evaluating the current use of qualification and certification as a capability of WFM and expanding it 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.
- Enhance mobile accessibility for WFM to drive end-user adoption, and provide real-time access for end users.
- Identify the potential of emerging capabilities, such as AI-enabled scheduling optimization and skills management in WFM. Develop a business case for a pilot deployment to quantify the ROI and justify the wider rollout.
Sample Vendors
ADP (WorkForce Software); ATOSS Software; Dayforce; Infor; Jitjatjo; Legion WFM; ProMark; Quinyx; UKG
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 covering physical, mental/emotional, career, financial and community wellness and 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, well-being solutions are projected to be one of the top three HR tech investments for 2025. However, only 30% of HR tech leaders report their team has been successful in driving employee well-being outcomes. To drive targeted solutions, employers still need to address the issues of low employee participation, reactive approaches and missing root cause analysis of poor well-being.
Business Impact
Employers have historically deployed wellness programs focused on reducing employer healthcare costs. However, the focus of these programs should be broader: reinventing the employee value proposition to deliver a more human deal that focuses on the whole person, their life experience and, ultimately, the emotional response that this human deal creates. Doing this eventually increases employee engagement and loyalty.
Drivers
Strategic employee well-being solutions can increase retention and boost employee engagement, performance and productivity. These solutions can consist of three main sets of capabilities:
- Foundational capabilities include a unified user experience 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 five 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
- Difficulty in quantifying the business value: Especially where participation rates are low despite more time spent communicating offerings, quantifying business value is a challenge. Organizations struggle to draw the link between participation rates and outcomes (e.g., improved mental health). Because employee well-being solutions are a top projected HR tech purchase for 2025, CHROs will need to clearly demonstrate impact on business and talent outcomes.
- Reactive mindset around well-being: This affects how organizations measure the impact of well-being programs and connect data beyond absence and retention rates.
- Limited insights into use of solutions: Organizations often fail to regularly gather employee feedback to assess their use and perceived value of well-being solutions.
- Fragmentation of solutions: A recent trend of mergers and acquisitions among well-being and healthcare providers and an incorporation of financial well-being in payroll solutions hampers streamlining of user experience and analytics .
User Recommendations
- Pilot where possible to justify further investment. Programs can start as a grassroots effort to boost physical or mental health or to build a sense of community. Well-being coaches and employee recognition initiatives help to encourage participation.
- Design metrics to capture the full employee adoption journey and align to key moments in the program life cycle (e.g., launch, communications campaign).
- Plan how employee well-being technology connects with the wider HR technology ecosystem.
- Evaluate the capabilities of current providers before adding new solutions and choose those that integrate data, workflows and analytics when using multiple solutions. Employee well-being can be delivered via point solutions, employee experience tech and human capital management suites.
Sample Vendors
ADP; Benevity; BetterUp; FinFit; Personify Health; TELUS Health; Thrive Global; Unmind; WebMD Health Services; Wellable
Gartner Recommended Reading
Workforce Planning
Analysis By: Harsh Kundulli
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Early mainstream
Definition:
Workforce planning enables CHROs 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
It is important for HR, finance and business leaders to be well-equipped to support agile and continuous workforce planning activities because of economic and geopolitical shocks and continuing skills shortages. There is increased HR leader interest and maturity in implementing workforce planning processes. Technology solutions supporting workforce planning continue to improve HR’s ability to connect tactical and strategic scenario-based workforce planning activities.
Business Impact
Workforce planning brings together business, HR and finance leaders, offering a shared view of the current workforce and necessary changes to meet strategic and operational goals. It supports short- and long-term objectives, addressing economic uncertainty, talent agility, location strategy, AI productivity, growth, mergers, acquisitions or divestitures.
Drivers
- Interest in workforce planning tends to be cyclical, increasing in times of uncertainty and decreasing in times of stability. Economic uncertainty, regulatory and geopolitical shifts, demographic changes, and AI investments have created an environment for rising interest in workforce planning.
- Workforce planning practices are being 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 during 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 rising due to its ability to provide business transparency and holistic planning capabilities. xP&A integrates financial planning with other enterprise functions like supply chain, operations, IT, sales and workforce planning. Effective xP&A tools for HR’s workforce planning improve alignment with financial planning, boosting 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, which limits the application of strategic workforce planning.
- Multinationals face challenges in detailed personnel cost planning due to varied payment structures, wage types and leave policies across regions, compounded by the difficulty of accessing payroll data from outdated systems.
- No one workforce planning technology solution can support all forms of workforce planning.
- There could be challenges integrating workforce planning solutions with existing HR, payroll, finance or other business systems.
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 types 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, scalability, 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
Machine Learning in HR
Analysis By: Sam Grinter, Stephanie Clement
Benefit Rating: High
Market Penetration: 5% to 20% of target audience
Maturity: Early mainstream
Definition:
Machine learning (ML) is an AI discipline that solves business problems by utilizing statistical models to extract knowledge and patterns from data. ML 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
ML is essential in improving the accuracy of other AI-based solutions, such as generative AI (GenAI) and AI agents. As organizations increasingly deploy GenAI and AI agents in HR, the use of ML will grow. Additionally, ML enables HR leaders to move beyond descriptive analytics to pattern detection and improved decision making. ML aids in strategic investment decisions, guiding HR leaders in selecting impactful talent programs and providing data-driven support for hiring, learning, compensation and engagement strategies.
Business Impact
- Improving the accuracy of GenAI and AI agents is vital in ensuring these technologies deliver business value for HR.
- 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
- Hype is driven by a desire of HR teams to go beyond descriptive analytics to more predictive and prescriptive insights. Since ML is one set of AI techniques, interest is also driven by innovation teams seeking AI, and increasingly, GenAI use cases in the HR domain.
- As investments into broader AI capabilities and solutions increase, so too does the use of ML. The key reason is that ML is critical for increasing the accuracy of GenAI and AI agent solutions by reducing errors, such as identifying and removing hallucinations in conjunction with feedback loops from end users. For example, a video interviewing application creates a summary of an interview using GenAI, and the interviewer is then asked to rate the quality of the summary and provide commentary to support the rating. ML is used to improve accuracy of GenAI based on this feedback.
- Embedded and vendor-provided capabilities such as risk analysis, recommendation engines or matching algorithms within a broad set of HR applications is driving increasing adoption of ML in HR. Examples include: employee flight risk analysis, sentiment analysis, candidate-ranking algorithms, learning recommendations, and augmented and segmentation analysis across many talent metrics.
- 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.
Obstacles
- Limited 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 ML into their solutions. Organizations leveraging aging HR solutions will find that their systems do not incorporate ML.
- 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 fragmented across multiple 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 or prioritization within IT to develop, build and deploy models, to support the use of ML in HR.
User Recommendations
- Evaluate your existing application portfolio for availability of ML techniques and check vendor roadmaps.
- Hire or nurture staff that can understand ML 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 presence of clear data lineage, and the nature of the data being used.
- Evaluate solutions on the basis of the ability to leverage analytical output in other analytic workflows, the ability to display results in other modules of the application and the ability for the results to be presented to end users.
- Ensure alignment with digital ethics principles, because ML 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
Deloitte; One Model; Oracle; Panalyt; Qlearsite; SAP; SmartRecruiters; UKG; Visier; Workday
Gartner Recommended Reading
Climbing the Slope
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 enable organizations to express appreciation to individuals “in the moment” for achievements and behaviors that align with organizational goals. These systems also contribute to the year-end review process. Rewards can be monetary- or nonmonetary-based.
Why This Is Important
In today’s challenging macroenvironment, revitalizing company culture and morale is essential for improving talent attraction, performance and retention. Recognition and rewards technology offers a continuous way to appreciate employee contributions through service awards, peer-to-peer recognition and manager discretionary awards in the flow of daily work. It also provides a record of achievements throughout the year, serving as valuable input for year-end performance reviews and reducing recency bias.
Business Impact
Recognition and reward technology enhances employee morale, motivation and sense of belonging. It supports initiatives like value-reinforcement campaigns, technology adoption, job candidate referrals, company well-being initiatives and ESG programs. This technology transitions organizations from a past-focused assessment culture to a more agile, ongoing growth-based learning culture.
Drivers
- Simplifying in-the-flow-of-work recognition through integrations with collaboration tools like Microsoft Teams and Slack.
- Consolidating to a single, efficient rewards and recognition technology to replace multiple, homegrown tools.
- Upgrading to a feature-rich point solution when lightweight alternatives are insufficient.
- Improving talent retention, engagement and attraction and understanding of recognized in-demand skills.
- Indirectly enhancing customer experience by boosting employee engagement through internal and external (e.g., customer) recognition.
- Providing motivation beyond merit and bonus plans, which typically occur only once or twice a year.
- Empowering individuals to recognize exceptional work on a one-on-one or a one-to-many basis.
- Enabling leaders to track progress on initiatives such as ESG and other efforts outside of revenue targets.
Obstacles
- Unclear or wavering management sponsorship coupled with insufficient perceived business value realization for recognition and reward programs ahead of other initiatives.
- Limited budget for monetary awards; both for the technology and per employee/per year spend allocation.
- Misunderstanding of the category as merely a gifting tool or employee “perk.”
- Lack of ongoing change management and commitment to evolve the recognition and reward program through thoughtful campaign management (minimum partial dedicated resource).
- Perception of recognition and reward as a tactical compensation and benefits project as opposed to a key, ongoing component of a world-class total compensation strategy.
- Concerns about system misuse and governance.
- Market confusion due to overlapping offerings from various providers.
User Recommendations
- Elevate the impact of recognition with a reward component. Use recognition to foster cultural cohesion and engagement.
- Use inbuilt reporting and analytics to gain insight into where and why there are hot and cold spots of usage.
- Invest in solution design and internal marketing to boost awareness and engagement.
- Make it easy to give and “see” recognition and encourage leadership to regularly promote the program.
- Ensure ease of access within digital work tools (e.g., embedded in Microsoft Teams).
- Consider technology that supports a mix of monetary rewards and nonmonetary rewards, including paid-time-off days, charitable donations and volunteer service time.
- Consider the impact potential of the allocated per employee/per year budget for rewards and how well that aligns with your strategy (e.g., milestone award “perk” vs. total compensation package strategic lever).
- Explore integrating recognition systems with performance management and voice of employee systems for a comprehensive employee view.
Sample Vendors
Achievers Solutions; Awardco; BI Worldwide; Bonusly; Guusto; Kudos; O.C. Tanner; RewardsGateway; Workhuman; Workstars
Gartner Recommended Reading
AI in Talent Acquisition
Analysis By: Jackie Watrous
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
AI in talent acquisition refers to the application of AI technologies — such as machine learning, natural language processing, and generative AI (GenAI) — to automate, augment and optimize recruitment outcomes. AI in recruitment delivers insights, recommendations and data analysis to complete manual workflow tasks, personalize the candidate experience, support decision making and drive quality-staffing outcomes.
Why This Is Important
AI is transforming recruiting by automating tasks, enhancing decision making and enabling personalized experiences at scale, crucial for competing in a complex labor market. AI-driven sourcing and screening improve hiring speed and quality. Virtual assistants streamline scheduling and reduce manual tasks. As AI solutions mature and vendors prioritize ethical practices, organizations are advancing their AI recruitment strategies.
Business Impact
AI solutions can improve the productivity of recruiters and hiring teams, and positively impact candidate engagement. They help elevate recruiting team capabilities throughout the recruitment life cycle. Investments in AI can improve key deliverables, such as diversified candidate pools, candidate experience and quality of hire (e.g., through candidate prioritization and skills identification), cost per hire, time to hire and process efficiency.
Drivers
- AI evolution and impact on recruitment: The capabilities of AI are diverse and rapidly advancing. While AI has been integrated into recruitment solutions for some time, the emergence of GenAI has significantly accelerated progress. This momentum continues with the development of AI agents capable of being semi- or fully autonomous in completing tasks. Human capital management and talent acquisition platforms have incorporated AI features into their core offerings, complemented by specialized solutions that enhance functionality.
- Growing demand for AI-driven sourcing: Organizations increasingly seek AI-enabled candidate sourcing to efficiently curate leads for open positions, utilizing both existing talent pools and externally sourced public data. This approach has the potential to optimize spending in the sourcing domain, ensuring the identification of the necessary talent.
- Enhancing transparency and efficiency in screening: AI applications in candidate matching, prioritization and assessments have matured, with vendors emphasizing responsible and transparent AI practices. These solutions now provide recruiters with insights into why candidates receive specific rankings or scores. As organizations face high volumes of candidates per role, there is a heightened risk of overlooking those with the right skill matches, prompting a search for more efficient and effective screening methods.
- Improving candidate experience and recruiter efficiency: Organizations are keen to eliminate manual processes and enhance the candidate experience. AI has driven significant improvements in areas like scheduling and data collection. Emerging features, such as interview intelligence, offer capabilities for generating interview summaries and collecting defensible feedback, further streamlining recruitment processes.
Obstacles
- Complex vendor selection: The AI market is rapidly growing, with new vendors and existing ones enhancing their offerings. Some provide comprehensive capabilities, while others may not. For example, not all interview scheduling solutions offer full automation. Thorough evaluation is crucial to ensure all requirements are met.
- Process risks: Implementation risks include AI automating too much of the process. While suitable for basic tasks, AI influencing decision making is concerning. Teams must set clear expectations for AI use, ensuring it aids rather than replaces human decisions, keeping the recruiter in the loop. Documenting recruiter roles and AI-support areas is essential.
- Ethics and compliance requirements: AI faces scrutiny from legal, compliance and data privacy teams. Discussing with vendors how they ensure transparency and mitigate bias is vital. Regulations in some countries are emerging to guide AI management, promoting fair outcomes.
User Recommendations
- Prioritize impactful use cases like improving candidate engagement, enhancing recruiter capabilities, implementing automation or reducing costs. Define clear requirements and metrics to assess AI’s impact on efficiency, cost and quality of hire.
- Align AI capabilities with recruitment types; complex roles with low candidate volume benefit from AI-enabled sourcing and marketing, while early career roles require different AI solutions.
- Consider full automation with tools like virtual assistants for high-volume recruitment to minimize candidate drop-off and time to fill.
- Engage organizational governing bodies when selecting vendors and planning implementation. Some capabilities, like interview scheduling, pose low risk, while others, like candidate matching, need thorough review. Ensure vendors provide explainable AI via user interfaces and analytics that demonstrate fairness in selection outcomes.
Sample Vendors
Beamery; Eightfold AI; Fetcher; hireEZ; HireVue; Joveo; Paradox; 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 and traceability. These solutions typically 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:
- Increased 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 regulations, including General Data Protection Regulation (GDPR), digital format mandates and regulations 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 admin 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.” Recent return to office (RTO) mandates by some firms have not significantly reduced this driver in the overall market.
- Difficulty in determining how best to grant and manage the right level of access to the appropriate users across HR and operational functions.
- Documentation principles and requirements, initially driven by COVID-19 tracking needs, are now being applied to broader health and wellness tracking, including health attestations and doctor’s notes for medical reimbursement.
- Many firms desire solutions that enable a holistic approach to managing HR documents in their distributed environment, including more robust searching, tagging and summarization capabilities fueled by generative AI (GenAI).
- Several HR document management vendors have added integrated HR service management (IHRSM) functionality. Conversely, many IHRSM solutions and HCM suites have added HR document management, as have several content services platforms. These providers continue to enhance their offerings to meet more use cases.
The result is a continued slight decline in the use of point solutions and steady market progression, with adoption slated to continue increasing (particularly among midmarket enterprises) over the next three years.
Obstacles
Picking the right solution can be challenging, as market entrants come from:
- Traditional records management providers that have developed software combined with services to help clients convert paper records to digital.
- Enterprise document management systems that have enhanced their solution to comply with HR’s stringent security and confidentiality needs.
- HR software providers that have either built or acquired 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 EU operations due to GDPR.
Additional challenges include:
- 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 whether 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 enterprise document management 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, document summarization and compliance with multijurisdictional records retention policies and regulations.
- Paper digitization is a well-developed market. Consider outsourcing versus building this capability in-house.
Sample Vendors
Access; aconso; D2Xchange; DynaFile; Hyland; Iron Mountain; Neocase Software; OpenText; ServiceNow; UKG
Gartner Recommended Reading
Integrated HR Service Management
Analysis By: Ranadip Chandra, Nicole Paripurana
Benefit Rating: High
Market Penetration: More than 50% of target audience
Maturity: Early mainstream
Definition:
Integrated HR service management (IHRSM) tools are a holistic platform for organizations to manage HR shared services operations and employee experience. Core functionality includes HR case management (ticketing or routing), knowledge base, content delivery via channels such as portal and virtual assistant, service-level agreement (SLA) monitoring and single sign-on into HR administrative applications. Additional functionality may include digital document management and transition management.
Why This Is Important
Integrated HR service management (IHRSM) solutions give robust control and standardization to the processes required to manage HR services. Employees engage with HR to seek clarifications regarding work policies, organizational benefits and administrative processes through the IHRSM portal or help desk, making it a comprehensive platform for employee service experience. Personalized workflows for work or life transitions have also become an important part of the employee experience narrative.
Business Impact
Improved HR administration can drive HR service efficiency and improve the overall perception of HR. The effective deployment of IHRSM tools will significantly reduce HR shared services costs. Mature IHRSMs include early detection and correct handling of employee relations 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 maintaining compliance.
Drivers
Demand for IHRSM tools is driven by a desire for streamlined HR administration, increased compliance and improved risk mitigation, and an enhanced employee service experience. Additional drivers include:
- Expanding the scope of 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 and long-term disability cases are often too complex for incumbent generic 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.
- Developing generative AI capabilities generate comprehensive case summaries for HR experts and specific brief responses to complex queries from employees analyzing multiple policy documents. Additionally, HR technology vendors have chosen IHRSM as one of the foundational use cases for experimenting with HR-specific AI agents.
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.
- The low-code/no-code platforms within IHRSM systems are at varying levels of maturity. While some systems empower citizen users to configure workflows, many are constrained by limited flexibility for customization.
- While experimentation in AI agents holds promise over the long term, in the short to midterm, there may be confusion and challenges regarding feasibility and adoption.
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 and weaker in other extended use cases. (For example, IT service management origin solutions offer comprehensive case management and high automated resolution rate, but they offer relatively less mature employee relations support.)
- Avoid selection bias by balancing the evaluation of overly hyped employee experience features with less visible but essential capabilities such as employee relations.
- Assess the level of complexity in integrating the IHRSM solution with the core HR application. It is preferable to pick a solution that offers out-of-the-box integration with the present HCM suite.
- Investigate emerging capabilities, such as AI agents in HR service management, employee communications and campaign management and critically evaluate organization-specific needs.
Sample Vendors
BMC; Dovetail Software; Ivanti; Leena AI; Neocase; Salesforce; ServiceNow; UKG; WTW; Zendesk
Gartner Recommended Reading
People Analytics
Analysis By: Tim Pasto
Benefit Rating: High
Market Penetration: 20% to 50% of target audience
Maturity: Early mainstream
Definition:
People analytics is the analysis and interpretation of employee data that enables a data-informed approach to managing talent and making decisions about workforce planning, talent acquisition, development and retention. Leveraging access to data and utilizing advanced analytical methods, people analytics gives the organization a deeper understanding of their employees and identifies areas for improvement, leading to increased efficiency, productivity and overall business performance.
Why This Is Important
During times of increasingly rapid change, business leaders are under pressure to make informed and improved strategic decisions. To do so, they need to successfully translate the skyrocketing volume of data about work and employees at their disposal into objective, data-driven insights. Enterprises equipped with accessible people data and insights for strategic decision making will better meet the fast-paced demands of the business and a rapidly evolving workplace.
Business Impact
People analytics provides visibility into headcount, employee demographics and HR process completion rates. Many organizations leverage people analytics insights to improve key employee outcomes, such as employee experience, engagement and productivity. Advanced people analytics teams combine talent data with other business data to explore the impact of talent decisions on business outcomes. Leveraging AI can enable faster delivery of insight without increasing the people analytics team headcount.
Drivers
- Objective insights increasingly need to be delivered to business leaders at scale and must keep up with the constant shifts and changes in the workplace.
- Adoption of people analytics technology solutions continues to grow, with multiple ways for organizations to invest. Solutions range from packaged people analytics platforms from specialist providers to people analytics modules offered by cloud human capital management (HCM) suite providers. Some organizations may build their own solutions, leveraging data lakes or warehouses while utilizing a generalist business intelligence platform to generate insights.
- A greater understanding of common use cases for people analytics allows for the standardization of common dashboards, metrics, trend analysis and more advanced analysis, lowering the barrier to entry to the market.
- GenAI’s ability to automate insight generation and increase adoption and utilization among leaders is generating interest and hype.
- People analytics teams increasingly need to be able to incorporate multiple sources of data both internal and external to the organization. This increasingly includes complex quantitative and qualitative data, such as behavioral, workstyle, operations, customer service, labor market and financial data.
- Adoption of people analytics in midmarket and smaller organizations (with less than 2,500 employees) is growing, giving vendors the opportunity to extend and meet this need. This will continue to accelerate as the application of advanced AI models allows for greater efficiency with data modeling and visualization.
Obstacles
- Organizations have difficulty integrating data from both HR and non-HR systems.
- HR functions encounter challenges in setting up and maintaining sufficient data governance that enables them to have reliable data.
- Frequent changes in organizational structures introduce complexities when conducting 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.
- Poor data literacy skills of leaders in both HR and business leads to low rates of adoption of people analytics products. This leads some people analytics teams to constantly rebuild and adjust dashboards, compromising their ability to take on advanced analytics projects and deliver more meaningful insights.
- Organizations struggle to structure people analytics teams to meet the ever-growing demands from business users with limited staff.
User Recommendations
- Align people analytics investments to HR strategy, HCM technology strategy and enterprise analytics strategies. When selecting technology solutions, consider the size of your people analytics team, their skills, operating model and any budgetary constraints.
- Establish robust data governance for the most critical data points that support baseline data fields, such as headcount, worker, job or functional categories, location and department that support further analytics.
- Invest in technologies that automate the ingestion of multiple data sources — both from within and outside of HR — as well as the design and delivery of standard dashboards, metrics, reports and insight generation.
- Identify technologies that contain augmented analytics features that can drive adoption across HR roles and business management, and free up people analytics resources to focus on more strategic projects.
Sample Vendors
Crunchr; HRBench; One Model; Orgvue; Praisidio; SplashBI; Vemo; Visier; ZeroedIn
Gartner Recommended Reading
Appendixes
See the previous Hype Cycle: Hype Cycle for HR Technology, 2024
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 (June 2025)
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 (June 2025)
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 (June 2025)