Predicts 2026: AI’s Impact on the Future of Workforce

14 November 2025 - ID G00839338 - 24 min read
By Arun Chandrasekaran, Helen Poitevin,  and 4 more
AI will have a transformational impact on the future of work, indelibly altering how we perform work in the future. CIOs should act on these bold predictions to prepare their organizations and workforces and create a symbiotic relationship between humans and machines at the workplace.

Overview

Key Findings

  • How organizations hire people will change in the future as the half-life of workforce skills is rapidly shrinking, driven by the automation of routine work, the erosion of legacy competencies, and the emergence of AI-orchestrated workflows, where machines increasingly execute tasks under human oversight.
  • Autonomous AI agents are beginning to initiate, negotiate, and complete tasks independently, often without human input. This shift will redefine how users and systems interact, requiring CIOs to evolve beyond traditional UX/UI thinking toward a new design paradigm that is centered on agent experience.
  • CIOs are finding it hard to balance short-term AI ROI pressures with the long-term need for a coherent human-machine talent strategy, which could potentially result in wrongful workforce layoffs and an acute shortage of key skills.
  • Organizations are keen on creating digital avatars of their employees, leveraging innovations in multimodal AI. However, embodying the employee’s identity, decision-making style, and personal expertise falls into gray legal areas.
  • Representing AI agents as digital workers alongside employees in org charts subtly signals their equivalence and potential replacement, which is likely to negatively impact employee engagement and lower morale.

Recommendations

  • Future-proof your workforce by investing in adaptive learning systems that personalize learning programs, proactively recommend microlearning courses, and verify skills in real time.
  • Train UX and engineering teams to design systems optimized for human-agent collaboration, where workflows seamlessly integrate human judgment with AI-driven actions and eventually for machine-machine interactions.
  • CIOs must partner with HR to architect the new talent mix, redefining roles, skills, and workforce design to realize AI ambitions, with this “talent remix” guiding any workforce restructuring decisions.
  • CIOs should lead enterprisewide dialogue on the responsible use of employee data in AI enablement and digital avatars while proactively partnering with HR and general counsel to stress-test employment contracts for scenarios where AI may replicate elements of human skill.
  • Keep AI agents outside formal reporting structures when tracking agent identities in IAM tools or HR systems, or house them in a separate, clearly governed unit to prevent confusion over authority, responsibility, and performance ownership.

Strategic Planning Assumptions

  • By 2030, the half-life of technical skills will shorten to two to five years from eight to 12 years, resulting in adaptability and learning velocity being the primary metric for hiring.
  • By 2029, 60% of digital products will be architected primarily for AI agent consumption, with human-facing UX becoming a secondary consideration.
  • By 2029, 30% of employees who were terminated and replaced by AI will be rehired, often at a higher cost, due to ineffective workforce transition strategies.
  • By 2028, at least one large enterprise will expect the right to create and maintain AI avatars of all employees, replicating their knowledge and skills, forcing employment contracts to be inclusive of digital identities
  • By 2028, organizations that display AI agents in team structures will have 15% lower employee engagement compared to those that don’t.

Analysis

What You Need to Know

Whether AI will reshape current and future jobs has become the defining debate of our time. However, the narrative of an AI-driven jobs apocalypse is misplaced. Gartner’s latest analysis shows that while AI is profoundly reshaping work, it is not eliminating it. Thirty-two million jobs will require refactoring every year through 2031. We expect a significant amount of job redesign and task automation but not massive scale job reductions. Moreover, AI is rarely the primary cause of workforce reductions — geopolitical, market, and structural factors play far greater roles. While the IT software and services sector will experience the sharpest shifts, it accounts for less than 2% of the global labor force, meaning the intense disruption seen in tech cannot be generalized for the overall job market.
For CIOs, the message is clear: the AI era demands workforce reinvention and operating model agility, not fear of job disappearance.
CIOs must urgently navigate AI’s accelerating impact on the workforce: assess and respond to AI’s shortening the half-life of skills, and prepare for a workplace defined by human-machine collaboration, invest in workforce augmentation and reskilling strategies, enable new forms of digital identity and trust, and sustain employee engagement and morale amid the coming wave of workforce redesign and disruption.

Strategic Planning Assumptions

Strategic Planning Assumption: By 2030, the half-life of technical skills will shorten to two to five years from eight to 12 years, resulting in adaptability and learning velocity being the primary metric for hiring.
Analysis by: Arun Chandrasekaran and Tori Paulman
Key Findings:
  • AI is driving down the half-life,1 the time it takes for a skill to become less relevant due to technological evolution, of skills at a pace that is unprecedented. While even the half-life of foundational skills is decreasing, the half-life of tools, platforms related to technical skills is whittling down to as low as two to three years.
  • The Future of Jobs 2025 report by the World Economic Forum predicts large-scale skills disruption by 2030. Employers expect 39% of workers’ core skills to change by 2030, which is a sign of increasing skills instability in the future.
  • The half-life of skills is reducing due to a combination of factors: automation of routine tasks, fast-paced market innovation fueled by AI, devaluation of legacy skills and the potential rise of agentic AI, which is making AI as the execution engine with human oversight.
  • There is growing consensus that information retrieval, data analysis, routine communications (such as email) and entry-level technical skills will face faster automation due to the rise of AI, while roles involving complex problem solving, emotional intelligence, judgment and leadership skills will have a longer shelf life.
Market Implications:
  • Effective reskilling initiatives are critical because they allow companies to build a competitive advantage quickly by developing talent that is not readily available in the market and filling skills gaps that are key to achieving their strategic objectives.
  • Online learning vendors have updated offerings to include personalized learning through microlearning courses with adaptive AI tutors.
  • To get access to just-in-time and critical new skills, organizations may explore more blended resourcing models that involve a combination of contractors, gig workers, AI-augmented workers and more autonomous AI agents.
  • While AI is automating many technical and analytical tasks, it is simultaneously increasing the value of uniquely human skills such as emotional intelligence, leadership, complex problem solving, and collaboration. These skills, sometimes referred to as “soft skills,” are increasingly critical in future hiring decisions.
Recommendations:
  • CIOs should partner with CHROs to create a skills intelligence program that maps current skills and required skills for the future through internal collaborative efforts between HR, business and IT.
  • Invest in adaptive learning systems that personalize learning programs, proactively recommend microlearning courses, and verify skills in real time.
  • Invest in GenAI simulators that can realistically simulate real-life experiences by mimicking actual human behaviors.
  • Create safe zones for learning and experimentation by giving teams experiential programs to learn new skills and tech without fear of failure and reward learning in end-of-year appraisals.
Related Research:
Strategic Planning Assumption: By 2029, 60% of digital products will be architected primarily for AI agent consumption, with human-facing UX becoming a secondary consideration.
Analysis by: Brent Stewart
Key Findings:
  • Autonomous AI agents increasingly initiate, negotiate, and complete tasks with and without human triggers or inputs. This evolution demands a new perspective on graphical interfaces, system interfaces and user flows, known as agent experience (AX).
  • Intent-driven experiences are becoming standard practice. Systems must be designed to interpret, validate, and execute user-initiated, but agent-supplied, intents alongside traditional user inputs.
  • Hybrid experiences soon will dominate UX design. These experiences must facilitate human and AI agent collaboration/tasking while utilizing “live view” interfaces and extracting value from digital products. Interfaces, microservices, and workflows must balance human usability with machine-readable pathways.
  • The front-end and architecture implications of AX are profound. Organizations are beginning to rearchitect front ends, expand integrations beyond APIs to also include agent-to-agent protocols and MCP tooling/data, and implement rigorous trust models that govern how much confidence systems (and the humans behind them) place in an agent’s actions, decisions, and data exchanges.
Market Implications:
  • AX-ready products and platforms will have the competitive advantage: Vendors that rearchitect for agent-first interactions will dominate markets where speed, machine readability, and trust are essential. Organizations that do not pursue AX risk losing visibility as AI agents bypass their products and services in favor of more efficient, agent-friendly competitors.
  • Multiagent ecosystems will expand and lead to the introduction of new products and services: As agents transact and collaborate directly across systems, opportunities will open for startups and incumbents offering orchestration frameworks, integration layers, and governance services. Markets that fail to support cross-agent interoperability risk fragmentation.
  • Front-end development markets will be reshaped: Traditional human-centric UI demand will shrink as stripped-down, agent-optimized graphical interfaces take precedence. UX and front-end service providers must pivot toward building hybrid and agent-centered interaction models or risk obsolescence.
  • Trust, security, and compliance will be decisive differentiators: Buyers will prioritize vendors that provide zero-trust validation, auditability, and intent-verification guardrails for agent-driven actions. Markets will reward transparency and governance maturity over visual polish or legacy UX strengths.
  • Human-facing UX will decline as the primary value driver for many product types but will increase in value for products that offer experiences humans enjoy: In only a few years, digital products that focus on productivity and work will be judged primarily for their efficiency, reliability, and agent readiness first and secondarily for the quality of their UX design. For this category of products, hybrid interfaces will remain relevant mainly for oversight, exception handling, and branded experiences. However, UX design will increase in importance for product categories like social media, news, sports, certain types of retail shopping, gaming, etc., that humans enjoy doing and will not want to delegate to an AI agent.
Recommendations:
  • Design for AI agent consumption and multiagent systems by mapping agent personas and use cases alongside human personas to inform product design.
  • Use intent orchestration patterns (from schema-based approaches to more formal intent recognition frameworks) to interpret agent goals reliably. Pair these with API-first architectures to ensure agent-driven workflows remain predictable, auditable, and aligned to business-critical use cases.
  • Optimize for human-agent collaboration by training UX and engineering teams to anticipate hybrid interaction models that blend human and agent inputs, followed by stripped-down GUIs as agents become more autonomous.
  • Manage the operational impact of AX and refine governance strategies by piloting integration layers that use agent-to-agent protocols, MCP tooling and data, and traditional APIs.
Related Research:
Strategic Planning Assumption: By 2029, 30% of employees who were terminated and replaced by AI will be rehired — often at a higher cost — due to ineffective workforce transition strategies.
Analysis by: Shawn Murphy and Tori Paulman
Key Findings:
  • A cautionary tale: In early 2024, a fintech CEO partnered with an AI pioneer to build digital customer service agents, boasting that these agents had replaced 70% of their human customer service representatives. By mid-2025, service quality had faltered so badly that the organization began rehiring humans but was unable to hire enough people. It began drafting developers and marketers to take customer service calls. Most recently, the organization limited access to a human customer service rep as a “VIP” privilege rather than a standard offering.
  • Direct costs associated with rehiring employees can escalate to as high as $31,416. The associated costs include recruitment, training, and onboarding expenses.
  • Media headlines are claiming that AI is taking jobs and replacing staff. According to the 2025 Gartner AI in Finance Survey, 60% of CFOs expect they can reduce headcount due to AI. Yet, only 1% of headcount reductions are directly due to AI.
  • This is leading organizations into a dangerous belief that tech companies, which are selling AI, have figured out how to replace human workers with AI. Yet, notable tech companies are pursuing a talent remix, cutting from traditional business units such as customer service and HR, and investing in net-new AI revenue streams.
  • AI fluency and human readiness, coupled with the rise of agents, are bringing to the forefront the need for IT and HR to collaborate and co-lead the changes triggered by the AI-augmented workforce.
  • The 2025 Gartner AI Survey — CIO and Technology Leader View reveals that 59% of CIOs and technology leaders believe their organizations are struggling to keep pace with the rapid pace of change. Keeping that in mind, the accelerated advances of AI technologies stress the organization’s ability to implement change effectively, including following a repeatable change approach to reshape the organization’s workforce that’s fit for the future of work.
Market Implications:
  • The organization’s customer experience and reputational brand could be compromised without a layoff strategy that aligns with your organization’s AI talent patterns and AI ambitions. Furthermore, the organization’s employer brand could also be negatively impacted, making future talent acquisition more challenging and expensive.
  • In Gartner’s analysis of 2025 AI-driven layoffs, the main reason wasn’t solely the pursuit of automating human workloads and processes. The analysis revealed the layoffs were a way to reset the company’s go-to-market strategy by firing and then rehiring AI-related roles. To navigate the talent implications of the strategic shift, a symbiotic relationship between IT and HR leaders needs to mature. Their partnership is necessary to avoid needless layoffs, rehiring of previously terminated employees, and to mitigate the deleterious impacts these workforce transitions can have on the organization and the “surviving” workforce.
  • Executive leaders need to strengthen their leadership approach to guide the organization through the changes brought about by AI technologies. A repeatable change approach will give leaders a path to align workforce processes and practices with business needs. This will also mitigate against destroying already low trust levels in leaders and organizations with a poor change history. Incomplete layoff and change strategies will undermine momentum in the organization’s AI ambitions.
Recommendations:
  • Develop a layoff strategy that aligns with the organization’s AI implementation strategy. Use a combination of one or more of these layoff strategies: restraining hiring new talent, reducing existing headcount, and repositioning existing FTEs (see What’s Your AI Layoff Strategy).
  • Define the partnership between IT and HR where their focus is to design the new jobs and skills needed to support the organization’s AI ambitions. Additionally, focus on determining where to shift talent needed to support strategically important areas. This is what Gartner refers to as the “talent remix.” As a cost management strategy, evaluate offshoring solutions.
  • Design a change program that includes the right mix of change levers for your organization that support the layoff and AI talent strategy. At a minimum, your change program should include job redesign to support your organization’s AI implementation strategy, preparing managers to develop employees’ AI skills, rolling out an agile learning solution to support the talent remix, evaluating the proper organizational structure that aligns with the human-machine workforce, and updating your performance management system that supports your talent needs. The goal of your change program is to support the organization’s talent and layoff strategy and avoid the rehiring trap and associated costs.
Related Research:
Strategic Planning Assumption: By 2028, at least one large enterprise will expect the right to create and maintain AI avatars of all employees replicating their knowledge and skills, forcing employment contracts to be created for digital identities.
Analysis by: Afraz Jaffri and Tori Paulman
Key Findings:
  • An AI avatar of the employee represents a real human using computer-generated imagery (CGI), natural language processing (NLP), emotion AI, and synthetic voice to deliver a dynamic visual and auditory interface. These avatars offer organizations the ability to scale the potential impact of a single employee even when geographical or language barriers exist.
  • Previously, AI avatars primarily represented a digital being, not an identified human, and were used for internal use cases, such as executive communications and employee onboarding and customer-facing use cases such as customer support.
  • Employees are seeing increased adoption of tools aimed at capturing workplace interactions. This builds on the many digital pieces of content created during an employee’s years of service. The digital footprint of an employee includes their meeting transcripts, emails, chat interactions, presentation recordings, or any memorialization of an employee’s thoughts, speech, and written communication. These assets contain enough signal of an individual’s unique set of skills, style, and knowledge to form a substantial set of training data that could be used to partially or fully create their professional persona, even after they have left the company.
Market Implications:
  • Some professions have already seen the use of AI cause concerns over the impact on an individual’s original creative output, with books, movie scripts, artwork, and song lyrics all within the scope of AI replication. In 2023, the SAG-AFTRA strike in the U.S., in which one of the actors’ concerns was to seek protection against AI, demonstrated the potential to cause harm for years.
  • The digital replication of an employee’s identity, decision-making style, and personal expertise creates uncertainty on how new legal policies may unfold, intersect, and potentially conflict with privacy law, data protection and emerging rights related to biometric and personality data. Some U.S. states, for example, have introduced new laws to protect an individual’s right not to be preserved in AI form.
  • The market for AI avatar vendors already has several players, such as NVIDIA, Synthesia, Rephrase.ai, BeHumans, DaveAI, D-ID, Openstream.ai, PRSONAS, QuestIT, Soul Machines, and others. As technology matures, platforms that enable the building of customized software will make it easy, from a technical perspective, for organizations to build specialized digital workers with the capacity to act as AI agents to perform tasks and assume roles in more areas of the workforce.
  • As the market for building and producing AI avatars increases, the associated aspects of protection, enablement, guardrails and authentication will create new spaces for innovation and vendor offerings. Such guardrails may include limiting the behavior to only provide positive statements about the company, similar to guardrails currently placed on LLMs in customer-facing scenarios.
Recommendations:
  • Take advantage of AI avatars by minimizing security and compliance concerns associated with deepfake technology. Ensure digital identity verification and establish clear boundaries for use to protect both the organization and employees.
  • Choose scenarios where AI avatars can provide measurable returns on investment, such as reducing customer wait times and increasing customer satisfaction (CSAT). These metrics can be easily compared with a human-only workforce to evaluate effectiveness.
  • Engage in dialogue with representatives from all parts of the organization on how employee data is being used to deliver AI tools. Prepare for scenarios where parts of a valuable employee’s skills could be replicated through AI by stress-testing existing HR policies. For some highly valuable roles, understand how employee-produced content can be used as training data in accordance with existing regulations and standards.
  • Lower the technical barrier to offering AI avatars by combining generative AI with no-code interfaces. This approach simplifies the creation of image, voice and video components, making avatar development more accessible.
Related Research:
Strategic Planning Assumption: By 2028, organizations that display AI agents in team structures will have 15% lower employee engagement compared to those that don’t.
Analysis by: Helen Poitevin
Key Findings:
  • Placing AI agents alongside employees in org charts subtly signals their equivalence — and potential replaceability. Expectations about this vary around the world, per the 2025 Gartner Generative and Agentic AI in Enterprise Applications Survey. Respondents in APAC (57%) were more likely to agree that AI agents will replace many of their workers in the next two to four years than those in NA (28%) or EMEA (26%).
  • Tech vendors keep pitching AI as “digital workers” to win over executives making purchasing decisions, even as social media backlash reveals a disconnect. Those buying the tech see opportunity, while those using it see both threat and an incongruous mismatch between their value as an employee and what AI agents actually do.
  • Treating AI agents as people is a big stretch, especially when most perform narrow, transactional task sets. True humanlike acceptance of AI only emerges when it is designed to maintain deep, ongoing relationships with its users — a rarity in workplace deployments.
  • Per Gartner’s Global Labor Market Survey, between 24.9% and 33.2% of employees were highly engaged from 3Q22 to 2Q25. Many organizations have stable year-over-year employee engagement scores. However, these will likely be lower in organizations that broadcast a replacement message to their employees.
Market Implications:
  • Organizational leaders will experience higher employee engagement in their teams if they focus on treating AI agents as new technological capabilities that transform what employees and teams can accomplish, extending their ability to solve problems quickly.
  • Some AI agent deployments resemble role-bots, with an extensive scope across a particular domain. Role-bots support employees in a helpful and friendly way to get things done in a particular discipline, such as travel support or enterprise services, supporting topics like HR, purchasing, IT, and facilities. Interactions will likely remain transactional, despite the built-in friendliness and helpfulness. These role-bots should have a name, personality, and brand.
  • AI agent technology providers that focus on enabling AI agent governance, traceability, and performance insights will play a critical role in ensuring success with AI agents, whereas providers aiming to govern AI agents as if they were employees will fail. See Governance Challenges and Solutions for AI Agents.
  • HR technology providers that discourage employers from integrating AI agents into org charts will be more trusted by buyers. However, focus on the limited role an HR system may play in identity and access management will lead to more success. See IAM for LLM-Based AI Agents.
Recommendations:
  • Resist calling AI agents employees or digital workers. However, do give AI agents names, especially those that play a role-bot role.
  • Do not include AI agents in the org chart unless you isolate them in a separate unit away from the management reporting line. Only do this if their sole purpose is to assist in cost accounting, identity and access management, and enabling secure access to information and transactions. IAM systems are likely to manage this better.
  • When deploying GenAI assistants and LLM-based AI agents with a conversational user interface, be intentional about the type of human-AI relationship you want to promote.
  • Create an empathy map for key employee roles, clearly outlining where AI is likely to be used and the impact this has on identity, job satisfaction, and work-life balance. Follow up by examining engagement survey results related to job satisfaction and work-life balance following AI deployments.
Related Research:

A Look Back

In response to your requests, we are taking a look back at some key predictions from previous years. We have intentionally selected predictions from opposite ends of the scale — one where we were wholly or largely on target, as well as one we missed.
On Target: 2020 Prediction — By 2025, 47% of learning and development budgets will be wasted as AI eliminates 67% of on-the-job, task-based learning opportunities.
Previously Published in Predicts 2020: AI and the Future of Work.
Early-career professionals in many professions, such as customer service, software engineering, lawyers or investment analysts, have seen the number of on-the-job, task-based learning opportunities diminish substantially. Indeed, some early-career professionals in high AI-exposure jobs have struggled to enter the labor market at all.
Early-career professionals in complex jobs requiring high degrees of discernment, task selection, and collaboration face the biggest challenge with the introduction of AI. They have “experience starvation” as they lose opportunities to learn. More experienced workers in these professions use GenAI to do the work that was typically in the hands of junior professionals. Many are looking to GenAI simulators to help fill the skills gap and accelerate the ability of junior workers to develop the skills necessary to take on more complex work more quickly. See When GenAI Assistants Fail Protégés, GenAI Simulators Accelerate Experience and What’s Your AI Layoff Strategy?.
Missed: 2020 Prediction — 69% of what a manager currently does will be automated by 2024, requiring a complete overhaul of the role of the manager.
Previously Published in Predicts 2020: AI and the Future of Work.
The degree of automation of managers roles is substantially below the threshold put forward in 2020. In the 2024 Gartner Employee Perspectives on the Future of Work Survey, only 6% of workers stated that more than 70% of their role could be eliminated through the use of automation technologies. As explained in Organization Design Benchmarks: Manager Spans of Control, Management Layers and Responsibilities, a few management layers changed significantly between 2022 and 2024. However, the amount of work friction (anything that makes work hard for employees) from a higher number of management layers that employees experience has increased between those two years.
There is much talk about AI leading to layoffs in management. See 2025 CEO Survey: AI Opportunities to Delayer Middle Management. However, this doesn’t lead in reality to severe changes. For example, when Amazon announced a layoff of 14,000 employees in 2025, including many managers, “culture” was cited as the main driver, over cost or even AI. Indeed, much care must be taken when evaluating such headlines. See AI-Driven, Talent-Focused: Amazon’s Layoffs Announcement and the Future of Talent Strategies.

Evidence

1 2025 Gartner AI in Finance Survey. This survey was conducted to understand the level of AI planning, adoption and investment within the finance organization and the most frequent and most valuable AI use cases for finance. The survey also sought to identify the most significant and unexpected barriers to AI success and to capture lessons learned from early AI adopters. The survey was conducted from May through June 2025 among 183 respondents across North America, EMEA and Asia/Pacific. The questionnaire required the respondents to have certain job roles, such as CFO, financial planning and analysis leader, or transformation leader, and to have at least some involvement or responsibility in decision making about finance function technology. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
2025 Gartner Generative and Agentic AI in Enterprise Applications Survey. This study was conducted to understand the key challenges and opportunities when deploying generative AI (GenAI) tools, and where organizations should focus their AI investments. This research also aims to understand what stage organizations are at on their AI agent journey and their thoughts on AI agents. The research was conducted online from May through June 2025 among 360 respondents from organizations with at least 250 full-time employees across all industries (except IT software) in North America (n = 149), Europe (n = 140) and Asia/Pacific (n = 71). Soft quotas were established for country, company size, and respondent’s function type and job level to ensure a good representation across the sample. Organizations were required to have deployed or plan to deploy in less than one year at least one generative AI tool in at least one core enterprise application domain: digital workplace applications, customer relationship management applications, or enterprise resource planning applications. Respondents were team leaders or above, excluding C level, and involved in the rollout of generative AI tools; they were required to have certain responsibilities regarding these generative AI tools. Disclaimer: The results of this survey do not represent global findings or the market as a whole, but reflect the sentiments of the respondents and companies surveyed.
The 2024 Gartner Employee Perspectives on the Future of Work Survey was conducted to understand employee perspectives regarding various emerging technologies, and how it impacts their daily work. The research was conducted online from 20 August through 27 September 2024 and contains responses from 3,496 employees with representation from various geographies, industries and functions. Disclaimer: The results of this survey do not represent global findings or the market as a whole but reflect the sentiments of the respondents and companies surveyed.