EY: Can AI advance toward value if workforce tensions linger?
Source: EY, Can AI advance toward value if workforce tensions linger?, 29 January 2026
Executive summary
EY’s Work Reimagined research examines why widespread AI adoption is producing transformational outcomes for only a minority of organisations.
The study surveyed 15,000 employees and 1,500 employers across 29 countries. AI usage is already widespread: 88% of employees use AI at work to some degree and 37% use it daily. Yet only 28% of organisations are positioned to turn AI deployment into high value outcomes.
EY argues that access to AI is no longer the main constraint. The larger problem is the organisational foundation surrounding the technology. When AI is deployed on fragile talent foundations, including weak culture, insufficient learning and misaligned rewards, productivity benefits lag by more than 40% relative to organisations with what EY calls a Talent Advantage.
EY identifies two connected models.
At the employee level, AI Advantage comes from three factors working together: skill set, toolset and mindset.
At the organisational level, Talent Advantage comes from five interconnected capabilities: talent health and flow, AI adoption excellence, learning and capability development, culture and workplace transformation, and strategic total rewards.
Only 28% of organisations demonstrate strength across all five.
The research also shows why stronger AI adoption creates new management problems rather than simply solving existing ones. Employees receiving extensive AI training save significantly more time, but become more likely to leave. Employees bring their own AI tools into work when enterprise alternatives do not meet their needs, increasing innovation while creating governance risk. AI releases capacity, but organisations still need to decide how that capacity should be redeployed. Continuous organisational redesign may be necessary, but repeated restructuring can exhaust the workforce.
EY’s core argument is therefore that sustainable AI value depends on advanced technology and strong human foundations operating together.
Key stats and quotable claims
15,000 employees and 1,500 employers surveyed across 29 countries.
88% of employees use AI at work to some degree.
37% use AI daily.
Usage ranges from 80% of essential workers to 94% of knowledge workers.
Only 28% of organisations are positioned to turn AI deployment into high value outcomes.
Organisations with weak talent foundations see productivity benefits lag by more than 40%.
Only 5% of employees qualify as advanced AI users.
Employees receiving 81 or more hours of AI training per year save 14 hours per week.
Employees receiving fewer than four hours of AI training save just three hours per week.
Only 12% of surveyed employees received 81 or more hours of AI training in the previous 12 months.
Employees with more than 80 hours of AI training are 55% more likely to leave than the average.
Overall intent to quit has fallen to 29%, but reaches 45% among employees receiving the most AI learning.
Between 23% and 58% of employees bring their own AI tools to work, depending on sector.
64% of employees perceive a workload increase over the previous 12 months.
38% fear job loss without replacement.
Employees report an average of eight hours saved per week through AI.
In sectors including Wealth Management, Technology and Banking, reported savings reach 10 to 12 hours per week.
Talent Advantage organisations can unlock eight to 14 hours per week as employees move into more sophisticated AI use cases.
63% of Talent Advantage employers report significantly better culture than 12 months earlier, compared with 22% in the Talent Middle and 3% in the Talent Disadvantage group.
Approximately eight in 10 Talent Advantage employers have already significantly reorganised because of AI.
74% recognise that their organisations still need to evolve.
Overall summary
EY’s research suggests that enterprise AI has moved beyond an access problem.
Employees already have the technology and most are using it.
The important difference is how they use it and what surrounds that usage.
EY calls the first layer AI Advantage. It is built around three connected drivers.
Skill set determines whether employees know how to use AI effectively.
Toolset determines whether employees have appropriate tools for their role and clear boundaries around their use.
Mindset determines whether AI adoption is connected to organisational goals, incentives, leadership expectations and workplace culture.
Those three factors help explain why two employees with access to similar technology can generate very different outcomes.
But EY argues that AI Advantage is still not enough.
Organisations need a broader Talent Advantage built around five capabilities: talent health and flow, AI adoption excellence, learning and capability development, culture and workplace transformation, and strategic total rewards.
The 40% productivity gap is important because it makes the cost of weak organisational readiness measurable. AI can technically be deployed and actively used while the surrounding talent system prevents the organisation from realising the full gain.
The report also highlights several tensions that emerge as adoption improves.
More training produces more capability, but the people receiving the most training also become more attractive in the external labour market.
More AI produces more time savings, but released capacity has no automatic destination.
More employee experimentation can accelerate innovation, but personal AI tools introduce security and governance challenges.
More organisational redesign may be needed as AI changes work, but constant restructuring creates fatigue.
The AI operating model therefore has to manage technology adoption and workforce transition at the same time.
For WORK SELF, the strongest implication is that organisations need visibility into who is ready for the transition, which roles are changing, where capacity is being released, where employee risk is building and what intervention each person needs before AI deployment becomes an organisational performance problem.
Deep dive
Reaping value from AI investments requires mastering the tensions between talent and tech
Reaping value from AI investments requires mastering the tensions between talent and tech or risking an AI productivity drop of 40%.
AI usage at work is widespread (88%), yet only 28% of organizations have positioned employees to achieve transformative business impact from AI.
AI Advantage needs toolset, skill set and mindset investment while managing key tensions, including bringing your own AI behaviors and talent attrition.
Leaders build Talent Advantage by excelling in talent health, learning culture and strategic rewards that amplify AI adoption excellence.
CEOs across industries now see artificial intelligence (AI) as essential, not optional. Yet, according to a recent MIT study, despite billions invested in generative AI (GenAI), roughly 95% of organizations report no measurable return, while only about 5% of integrated AI pilots are extracting millions in value and demonstrating meaningful P&L impact. This, despite companies’ massive investments in AI – a third (35%) of senior leaders whose organization is investing in AI anticipate spending US$10 million or more next year, according to a recent EY study in the US.
The EY Work Reimagined study has tracked the evolution of work for years. This year, building on our past work on Talent Advantage, we zeroed in on AI in the workplace – specifically, why some organizations achieve transformational results while most see only modest gains.
We surveyed 15,000 employees and 1,500 employers across 29 countries and looked at a range of outcome measures including productivity, quality of work, decision making and various dimensions of the work experience. We looked at how employees are adopting AI and the benefits at work and then compared that with the benefits companies are seeing from a business perspective.
The findings of our Work Reimagined study reveal the scale of the challenge. Nearly nine out of 10 employees now use AI at work, yet only 28% of organizations are positioned to turn AI deployment into high-value outcomes.
Our research shows why: employees may be saving a few hours here and there but nothing that fundamentally changes how work gets done or how the business performs.
What separates the 28% from everyone else?
The organizations achieving transformational results have honed five strategic capabilities working in concert: having the right approach to recruiting and retaining talent; driving AI adoption at scale; building continuous learning into daily operations; reshaping culture and workplace norms; and aligning rewards with new behaviors and outcomes.
AI investment on its own isn’t enough.
When new technology lands on fragile talent foundations – weak culture, insufficient learning, misaligned rewards – productivity benefits lag by over 40%.
Chapter 1: How to drive AI Advantage – and why it’s not enough
Widespread adoption masks shallow use and fragile talent foundations
Employee usage of AI at work is now widespread, with 88% of survey respondents saying they use AI at work to some degree and 37% use it daily.
Usage spans from 80% of essential workers to 94% of knowledge workers, with near-universal adoption among leaders and managers.
Mind the maturity gap
However, this surface-level adoption masks a deeper problem.
While most workers use AI for basic tasks like searching for information (54%) or summarizing documents (38%), only 5% qualify as advanced users who blend multiple tools to unlock roughly a day and a half of additional productivity per week.
Advanced users extract far more value, using AI as a thought partner rather than a simple tool.
Skill sets, toolsets and mindsets drive the AI Advantage
What drives this gap between basic AI adoption and true AI adoption excellence is a model EY calls the AI Advantage.
EY analysed more than 15 variables in its 15,000 employee dataset to understand what fuels employees who see the most AI adoption value at work.
The measure examines median hours unlocked per employee per week and identifies three interconnected drivers: skill set, toolset and mindset.
Skill sets unlock productivity
AI training is key to success, accounting for nearly 50% of the AI Advantage score.
Employees with 81 or more hours of AI training per year save 14 hours per week, compared to just 3 hours for those with fewer than 4 hours of training.
Yet only 12% of surveyed employees received this level of training in the past 12 months, which helps explain why the global AI adoption value score sits at just 34 out of 100.
Manager proficiency also plays a crucial role. Employees who are confident their managers know how to use AI effectively see better outcomes.
However, the catch is that these highly skilled employees are also 55% more likely to leave.
Employers need to align the employee value proposition for employees with hot skills in the market.
It is critical to grant continued access to the latest technology as well as opportunities to translate the skills to career options.
Toolsets provide the right tools for the right role and clear guardrails
The right AI tools matter, and employees who report their AI tools are tailored to their specific role also demonstrate significantly higher adoption value.
However, in parallel, employees who are most passionate about AI potential are also taking matters into their own hands with 23% to 58% of employees bringing their own AI to work, and even paying for their own subscriptions.
Further, there is substantial pressure to perform, with 64% of employees perceiving a workload increase in the past 12 months and 38% fearing job loss without replacement.
This makes it even more critical to provide employees with the right toolset and to create the proper guardrails for personal tool usage.
Mindset becomes the new multiplier
Mindset drives adoption.
Having formal goals or incentives to adopt AI, along with involvement in organizational AI programs, correlates strongly with time saved and positive outcomes.
Leading organizations are increasingly formalizing business and individual goals to adopt and scale AI and agents.
They’re coupling upskilling programs with investments in culture, leadership and engagement to shift behaviors and drive impact.
When employees see AI adoption as part of their performance expectations and organizational culture, they engage more deeply.
Employers with Talent Advantage have 63% reporting that culture is significantly better than 12 months ago vs. 22% in the Talent Middle and 3% in the Disadvantage group.
Organizations that build an AI Advantage face new tensions
However, getting these drivers right is necessary but not sufficient.
Organizations that build an AI Advantage face new tensions that can offset productivity gains.
Bridging the gap between AI investment and tangible impact requires a balanced approach that considers both technological capability and human readiness.
The Talent Advantage – a framework of five strategic capabilities – can help to manage these tensions.
Chapter 2: Five capabilities that unlock high value AI outcomes
Less than a third of organizations master the Talent Advantage that makes transformation enduring.
Organizations with weak talent foundations see productivity benefits lag by over 40% relative to organizations with what EY has termed Talent Advantage – five interconnected strategic capabilities that create the foundation for sustainable transformation.
Only 28% of organizations in the survey demonstrate strength across all five – not as separate initiatives but as an integrated system where each capability reinforces the others.
1. Talent health and flow
Talent health and flow drive everything else, measuring overall conditions for success using employees’ likelihood to recommend their employer to friends and family.
Based on EY’s analysis of the variables that predict net promotion, the global Talent Health score stands at 65 out of 100, with culture accounting for 44%, rewards 32% and development 24%.
Talent Advantage employers achieve dramatically higher talent health scores.
While only 20% of employees at Talent Disadvantage organizations are likely to promote their company as a great place to work, 89% are likely to do so at Talent Advantage organizations.
This creates network effects – employee promoters become talent magnets who attract more high performers.
Talent flow remains foundational and even employees in sectors and at companies they promote will switch jobs.
The overall intent to quit has dropped to a four-year low of 29%, down from the “Great Resignation” peak of 43%.
However, this masks an important dynamic: job jumping remains high for those with the most AI learning, with 45% quit intent among this group who continuously weigh internal and external career and pay opportunities.
The paradox, which broadly surfaces with highly trained, high-value talent – building capability while increasing flight risk – becomes one of the critical tensions that organizations must navigate.
Employers can address this risk by ensuring these critical employees have continued access to tech, career opportunities, and are well rewarded.
2. AI adoption excellence
AI adoption excellence requires frequent, sophisticated use of AI with role-specific tools and strategic goals.
Organizations maximizing employee AI adoption value create opportunity to unlock eight to 14 hours per week of time savings as employees leverage higher complexity use cases.
Their advanced users don’t just use AI more, they use it differently, treating it as a colleague, coach and thought partner rather than a simple automation tool.
Top performers who adopt this collaborative mindset achieve gains more than twice as great as their peers.
However, organizations need to provide the right AI tools to get their employees to use the AI.
Even with tailored tools, given pressures to perform, employees are often taking matters into their own hands.
Between 23% and 58% of employees bring their own AI to work, with variation by sector.
This “shadow AI” represents untapped innovation potential but also governance and security challenges that organizations must address systematically.
Organizations have to be clear about the compliance issues created through employees using external AI tools while also maintaining sufficient flexibility to meet employee needs.
3. Learning and capability development
Learning and capability development stands as the strongest predictor of AI success.
Employees that receive the 81-plus hour threshold see transformational results.
However, higher levels of training create the learning paradox: employees with more than 80 hours are 55% more likely to quit compared to the average.
Advanced training makes skills more marketable and external markets may often reward AI expertise faster than internal promotion cycles can respond.
Talent Advantage organizations address this by pairing intensive learning with retention and rewards strategies and career development.
They create internal talent marketplaces, design progressive skill certifications tied to tenure and build learning cohorts that develop peer networks because social capital increases retention.
The proportion of employees receiving at least 80 hours of AI learning per year jumps from 15% at Talent Disadvantage organizations to 42% at Talent Advantage organizations.
4. Culture and workplace transformation
This kind of transformation provides the enabling environment for AI integration.
Leadership vision and cultural alignment are essential for successful AI adoption.
Culture scores have improved significantly, with 60% of employees now agreeing culture is significantly better than 12 months ago, up from 48% in 2021.
Employers have made progress in closing gaps in ways of working, enabling team connections, and helping employees feel more trust and support.
Talent Advantage employers dramatically outperform on culture metrics, with 63% reporting culture is significantly better than 12 months ago versus only 3% of employees at Talent Disadvantage organizations.
Culture accounts for 44% of the Talent Health score, driven by caring leaders, supportive employers, empowering managers, and team connection.
AI adoption itself can strengthen these cultural elements when organizations frame it as a collaborative learning experience rather than an individual technical skill.
5. Strategic total rewards
Strategic total rewards must be personalized and flexible, aligning with evolving employee needs and AI-driven roles.
Unlike in the past, where employees focused primarily on career opportunities inside their organizations, employees with AI skills can easily look outside for their next role.
These skilled workers become more focused on a work experience with great technology, flexibility and opportunities for growth that lead the market.
Talent Advantage organizations understand that rewards drive approximately 32% of talent health and they excel at making total rewards flexible.
Fully half of Talent Advantage employers strongly agree their rewards meet employees’ needs, compared to just 7% of Talent Disadvantage organizations.
Total rewards for the total person
The years-long trend toward more flexible working has become a baseline for many knowledge workers, with the data showing the baseline expanding.
Of the top rewards priorities, 31% of employees cite flexible schedule and work hours, with 26% citing a desire to work from anywhere.
Other top priorities reflect a desire of untethered employees broadly wanting to have rewards that reflect the realities of their daily personal and professional lives.
Pay remains important, but amid persistent concern about the cost of living, some employees are thinking differently about compensation.
The biggest set of knowledge workers, 37%, want more bonus and incentive options recognizing performance and contributions, with a third of employees choosing health and wellbeing benefits, paid time off and compensation that better reflects cost of living.
The broad rewards priorities of employers appear in line with these employee priorities, with noticeable differences.
Amid continued global healthcare cost pressures and expanded need, 39% of employers are planning health and wellness benefits investments in the next year, as programs need to reflect the needs of the whole person, including physical, mental and emotional health.
Nearly the same number of employers, 38%, are looking to improve performance based bonuses and incentives.
Notably, 28% of employers are looking to invest in skills building as part of total rewards enhancements, while only 23% of employers are planning to focus on modifying total compensation to reflect cost of living.
In a time of foundational talent flow it’s important not to consider employees a monolith, especially as the workforce is increasingly and necessarily multigenerational.
The youngest employees of Generation Z, for example, name paid time off and performance based incentives as their top two priorities, with health and wellbeing benefits and flexible schedules following closely.
Among Baby Boomers, 46% want total compensation to reflect cost of living, followed by 41% citing performance based incentives.
Chapter 3: Navigating the five critical tensions to reach the Talent Advantage
Leading organizations don’t avoid tensions; they actively manage them.
Every organization that pursues a Talent Advantage must pay attention to five important tensions that can derail progress.
The key is to lean into these challenges, providing the right support at the right time while learning as you go.
1. Address the learning retention dilemma
The 81-plus hours of AI training that drives adoption excellence also makes employees 55% more likely to leave.
Employees with fewer than 4 hours of AI learning have 21% quit intent, rising to 45% for those with 81 or more hours.
The motivations of highly trained employees shift too.
Those with more than 40 hours prioritize opportunities to work with the latest technology and enhanced flexibility over traditional compensation and career advancement.
Leaders face a dilemma: under-invest in learning and never build AI capability or invest heavily knowing it increases flight risk.
The answer lies in learning, rewarding and connecting to career paths as you go – pairing intensive training with evolved retention approaches that match how AI-skilled employees now think about their careers.
Build internal pathways that showcase opportunities for growth and technology access before employees look externally.
Further, create learning experiences that also build social capital and peer networks, recognizing that relationships anchor people as much as roles do.
Critically, calibrate the total rewards strategy to reflect what AI-skilled workers actually value, including access to cutting-edge technology, flexibility in how and where they work, and opportunities for continuous skill development, not just traditional pay increases.
2. Maximize AI enabled time gains
AI delivers tangible time savings, with employees reporting an average of eight hours saved per week.
However, many organizations struggle to translate these efficiency gains into meaningful business transformation.
Leading companies in sectors like Wealth Management, Technology, and Banking are saving even more time, 10 to 12 hours per week, but the critical question remains: how should this newly available capacity be strategically deployed?
JPMorgan Chase offers a compelling example.
The bank deployed LLM Suite – a generative AI tool – to 50,000 employees across its Asset & Wealth Management division.
The tool performs work equivalent to a research analyst, but rather than simply banking efficiency gains, JPMorgan is strategically redeploying analyst capacity toward higher-value work: complex interpretation, strategic advisory and deeper client relationships.
The key to avoiding the productivity trap is being deliberate about where saved time gets reinvested and recalibrating how ROI is measured.
EY argues that the value of AI has three key components: productivity, quality and efficiency.
Organizations fixated solely on hours saved miss critical dimensions like improved accuracy, reduced error rates, and enhanced decision-making quality.
Redesign roles alongside AI deployment
Redesign roles alongside AI deployment.
Determine what employees should stop doing, what high-value activities they should focus on, and how they can elevate their contributions.
Establish clear expectations about allocating time savings between strategic growth initiatives and creating space for innovation, learning and adaptation.
Consider piloting this approach in one function and measure both productivity and employee engagement metrics.
Make role redesign a standard component of AI implementation planning rather than an afterthought.
Additionally, create feedback mechanisms to continually refine how AI-enabled time savings translate into higher-value work, to more effectively gauge ROI and allow efficiency gains that drive meaningful transformation.
3. Deal directly with the anxiety innovation gap
While organizations struggle to find talent, 38% of employees fear job loss due to AI.
The same proportion, 38%, worry about overreliance on AI eroding human skills, expertise and learning.
This fear coexists with innovation demands.
Organizations need employees to experiment with AI and reimagine their work.
However, workforce anxiety creates hesitation, resistance and defensive behavior that protects current roles rather than transforming them.
Embrace emotions rather than ignore them.
Leaders must articulate a clear AI vision that addresses workforce concerns with a top-down commitment to managing the stresses of transformation – the increased workloads, the fears about job security, the anxiety about obsolescence.
Communicate not just what AI will do, but what humans will do that’s more valuable.
Identify employees whose roles have been elevated by AI and document their stories.
Specifically, what changed and why their work matters more now.
Give employees voice in how AI gets implemented in their areas, allowing those closest to the work to help shape its future.
Additionally, consider building programs that develop new skills before old ones become obsolete, making continuous adaptation the norm.
4. Resolve the shadow AI challenge
Between 23% and 58% of employees bring personal AI tools to work.
Stifling this kills innovation, but ignoring it creates security, governance and compliance nightmares.
Enterprise tools lag behind consumer AI experiences, and employees won’t wait for approval when they can solve problems immediately.
Survey your workforce to understand what personal AI tools they’re using and why and be sure to incentivize disclosure rather than punishing it.
Then, create “AI sandbox” programs that channel this energy into governed experimentation.
Employees need time and space to practice with the AI tools, figure out how they can use them and then apply it to their workflows.
Give employees explicit space to test personal tools under clear parameters.
Fast-track promising solutions into enterprise adoption to create a reverse innovation pipeline.
Turn shadow AI users into internal champions and tool scouts.
Establish clear governance boundaries while defining “innovation zones” for controlled risk taking.
5. Energize reorganization to combat fatigue
Roughly eight out of 10 Talent Advantage employers have already significantly reorganized due to AI, yet 74% recognize this still needs to evolve.
Leaders must continue changing to capture AI value, yet constant restructuring exhausts the workforce.
AI transforms work every quarter, but traditional reorganizations take 12 to 18 months.
To help address reorganization fatigue, create autonomy for parts of the organization to experiment with new structures in short cycles.
Assign clear roles and responsibilities while delegating decision-making authority appropriately.
Establish which parts of the organization will remain stable alongside which areas need to transform, giving employees certainty about what won’t change.
Build change capacity as an ongoing capability rather than treating each reorganization as a singular traumatic event.
Map your organization into stability zones and transformation zones, then communicate this clearly.
Enable the people executing these experiments to have real authority to make decisions about how work gets organized, not just permission to suggest changes that require endless approvals.
Shape the AI and workforce future with confidence
Only 28% of organizations have reached Talent Advantage, unlocking transformational results.
The remaining 72% have a choice: become fast followers who quickly build the five capabilities while navigating the tensions or fall further behind as the gap between leaders and laggards widens.
To achieve the right mix of five strategic capabilities and reach the Talent Advantage, organizations must orchestrate competing priorities: learning and retention, innovation and security, global and local talent needs, centralized vision and distributed execution.
This requires systematic excellence, not heroic individual efforts.
Talent Advantage matters because it predicts not only productivity but also whether organizations can achieve strategic goals beyond productivity, including enhanced performance, better decision-making, improved employee wellbeing and culture.
In a fast-changing business environment, where an adaptable and resilient workforce is essential, organizations with Talent Advantage have the opportunity to pursue ambitious growth while managing inevitable talent tensions.
Those without get stuck optimizing existing processes while competitors reimagine business models.
Sustainable advantage in the AI era depends on combining the ability to build strong human foundations and advanced technology.
There’s no question whether AI will transform an organization’s industry and workforce. It will.
The question is whether organizations will shape their transformation – or react to it.
The five capabilities provide what to build.
The five tensions reveal the challenges organizations will face.
The 28% who have achieved Talent Advantage prove it’s possible.
What the report means for workforce transformation
EY’s research reframes the enterprise AI readiness problem.
The issue is no longer simply whether employees have access to AI.
88% already use it.
The issue is whether the organisation surrounding those employees is capable of turning adoption into enterprise performance.
The more than 40% productivity gap between weak talent foundations and Talent Advantage organisations provides a measurable indication of what poor organisational readiness can cost.
The research also shows that readiness is multidimensional.
An employee can have strong AI skills and still leave because internal career pathways have not kept pace.
AI can save eight or 14 hours per week without generating equivalent enterprise value because nobody has decided where that capacity should go.
Employees can embrace AI enthusiastically while using unapproved external tools that introduce new governance risk.
Leaders can repeatedly redesign the organisation around new technology while exhausting the employees expected to execute the transition.
The workforce problem is therefore not solved by training alone.
Organisations need visibility into skills, role changes, readiness, retention risk, career pathways, workload, employee confidence and the future allocation of work.
Key takeaways
AI access is no longer the primary enterprise problem. 88% of employees already use AI at work.
Only 28% of organisations have built the broader Talent Advantage required to generate transformational results.
Organisations with weak talent foundations experience AI productivity benefits that lag by more than 40%.
Training matters enormously, but training without career and retention planning creates a second problem. Employees receiving the most AI learning are 55% more likely to leave.
AI time savings need an explicit destination. EY recommends determining what employees should stop doing and which higher value activities should replace that work.
Role redesign should be a standard part of AI implementation, not an afterthought.
Shadow AI demonstrates why employee experimentation and governance have to be managed together.
Continuous AI driven change requires organisations to distinguish between stability zones and transformation zones rather than repeatedly reorganising the whole enterprise.
Sustainable AI value depends on technology capability and human readiness operating as one system.
The operating question is no longer whether employees will use AI. It is whether the organisation can redesign work, careers, capability and accountability quickly enough to turn adoption into enterprise value.
