Artificial intelligence is spreading through workplaces faster than many employees can prepare for it. A new PwC global workforce survey released on September 29, 2026, shows that AI use among workers has increased by 10 percentage points over the past year, while access to learning and development resources has fallen by 8 points. The result is a widening skills divide in which a majority of employees risk being left behind while a smaller group of highly capable AI users moves ahead.
PwC surveyed nearly 50,000 workers across 48 countries and regions for its 2026 Global Workforce Hopes and Fears Survey. The findings offer a striking picture of a workforce adapting to faster technological change while receiving fewer opportunities to build the skills needed for that change. Just 51% of workers say they have access to the learning and development resources they need, down from 59% last year. Among the 56% of workers classified by PwC as the “engine room,” only about two in five report having the learning resources they need.
AI Adoption Is Rising While Workforce Preparation Is Falling
The most immediate finding is the growing gap between technology adoption and employee preparation. Nearly two thirds, or 64%, of workers say they have used AI at work during the past 12 months. That represents a 10 point increase from the previous year. Daily generative AI use has also climbed from 14% to 22%.
That acceleration matters because AI is no longer limited to specialist technology teams. Employees across business functions are increasingly encountering AI assisted workflows, automated processes, generative tools and systems that can change how routine tasks are completed.
Yet the training infrastructure has not kept pace. Only 51% of workers say they can access the learning and development resources required at work, compared with 59% in the previous survey. The decline creates a difficult situation for employees who are expected to adopt new technology while receiving less formal support to learn how to use it effectively.
PwC’s findings are consistent with its broader research showing that the skills demanded by employers are changing rapidly. Its 2026 AI Jobs Barometer found that skills required in highly AI exposed occupations are changing more than twice as quickly as those in less exposed occupations.
The 56% Workforce Group Facing the Biggest Skills Challenge
PwC divides the global workforce into four groups based on the scarcity of their skills and their progress with AI. The largest group, representing 56% of workers, is described as the “engine room.” These employees perform much of the day to day work inside organizations but are not far along the AI learning curve and generally do not possess skills that employers currently consider scarce.
This group faces a particularly difficult combination of circumstances. Only around two in five engine room workers say they have access to the learning and development resources they need. They are also less likely to report some of the positive outcomes associated with stronger AI adoption, including confidence in job security, confidence about developing new skills and willingness to seek career progression.
The distinction is important because falling behind does not necessarily mean an employee lacks ability or ambition. A worker may be willing to learn but lack time, training, access to appropriate software or support from a manager. When technology changes faster than workplace training, the resulting skills gap can become an organizational problem rather than simply an individual one.
A Smaller Group Is Pulling Further Ahead
At the opposite end of the workforce are PwC’s “front runners,” who represent 14% of respondents. These employees combine scarce skills with stronger AI capabilities. More than half use generative AI daily, and nearly 80% report access to learning and development resources.
The difference between these employees and the larger engine room group illustrates how access can compound existing advantages. Workers who already have valuable skills may receive more training because their employers see those skills as strategically important. They then become more capable of using AI, which can increase their productivity and confidence and potentially create further opportunities for advancement.
The risk for employers is that this cycle can become self reinforcing. If training is concentrated among workers who are already ahead, the organization may create a smaller group of highly capable employees while leaving a much larger section of its workforce with limited opportunities to catch up.
AI Users Are Reporting Greater Confidence at Work
The survey also finds a meaningful difference between frequent and infrequent AI users. Workers who use AI daily report greater confidence in their job security, stronger trust in management and greater confidence in their ability to learn new skills.
Daily AI users report a 68% level of confidence in their job security, compared with 57% among infrequent users. They are also 12 percentage points more likely to say they expect to ask for a promotion and 21 points more likely to express confidence in their ability to learn new skills.
These figures do not establish that AI use itself causes greater confidence. Workers who already have stronger skills, better access to resources or more supportive workplaces may also be more likely to use AI frequently. Still, the relationship highlights a practical concern for employers: employees who have opportunities to experiment with AI may be developing advantages that are difficult for less supported colleagues to replicate.
The Talent Loss Risk Is Not Limited to Workers Who Are Falling Behind
There is another side to the skills divide. Companies could also lose employees who are already ahead.
PwC reports that 29% of front runner employees with strong AI capabilities and scarce skills say they are very or extremely likely to change employers during the next year. That creates a double risk for organizations. Some employees may struggle to acquire the skills required for changing roles, while highly skilled employees may have enough market demand to move elsewhere.
For employers, the challenge therefore extends beyond offering generic online courses. Workers need visible pathways that connect training with real responsibilities, career progression and compensation. They also need opportunities to use newly acquired skills in their everyday jobs.
PwC’s earlier global workforce research found that employees who feel supported in developing new skills report substantially higher motivation. Its Global Workforce Hopes and Fears research has also highlighted a substantial difference in learning access between senior executives and non managers.
Why Training Access Matters More as AI Adoption Accelerates
AI adoption changes the nature of workplace learning. Traditional training programs often operate on a predictable cycle in which employees learn a skill and then apply it for several years. AI can shorten that cycle considerably.
A software developer may need to learn new AI assisted coding practices. A marketing employee may need to understand automated content analysis. An accountant may encounter AI supported forecasting and reconciliation. A customer service employee may work alongside conversational systems. Managers may need to learn how to evaluate AI generated information and redesign workflows around human judgment.
That means training cannot be treated as an occasional event. Employees need repeated opportunities to practice, ask questions, make mistakes and apply new capabilities to real work.
The human element also matters. When employees are told that AI will change their jobs but are not given meaningful preparation, uncertainty can become frustration. When they receive training and see how new tools can help them perform their work, the same technological change can feel more manageable.
What Employers Can Do About the AI Skills Gap
The survey points toward a workforce strategy that treats AI readiness as a broad organizational responsibility rather than a privilege reserved for specialists.
Give frontline employees meaningful access to training
Learning resources should reach the people performing everyday operational work, not only executives, technical teams and high potential employees. Training should be available in formats that fit real working conditions, including short practical sessions, guided projects and supervised experimentation.
Connect training with actual jobs
Employees are more likely to benefit when training addresses the tasks they perform every day. Instead of teaching AI as an abstract concept, organizations can show workers how to use it for research, analysis, documentation, communication, planning and other legitimate workplace activities.
Reward newly acquired skills
Training has greater credibility when employees can see what happens after they complete it. Clear promotion pathways, expanded responsibilities and appropriate recognition can demonstrate that developing new capabilities has tangible career value.
Protect time for learning
Employees cannot realistically reskill if every hour is already consumed by existing responsibilities. Companies introducing new technology should consider learning time part of the implementation process rather than an optional activity employees must complete after work.
Keep experienced employees involved
AI skills should not replace human expertise. Employees with years of industry knowledge can help identify where AI is useful, where human judgment remains essential and where automated outputs require careful review. Combining domain expertise with AI capability can be more valuable than treating the two as separate skill sets.
The Bigger Workforce Question Is Who Gets the Chance to Adapt
PwC’s findings present a workforce story that is more complicated than a simple argument that AI will create or destroy jobs. The immediate divide is between employees who have the opportunity to develop alongside the technology and those who are expected to adapt without sufficient resources.
The 10 point increase in workplace AI use shows that adoption is moving quickly. The 8 point decline in access to learning resources shows that workforce preparation is moving in the opposite direction. And the fact that 56% of workers fall into the engine room category makes the issue too large to treat as a niche talent problem.
For employees, the findings reinforce the value of building practical AI literacy alongside existing professional skills. For employers, they raise a more difficult question: whether investment in AI is being matched by investment in the people expected to use it.
The companies that address that question will need to look beyond purchasing AI systems. They will need to create conditions in which employees can learn, practice and apply new skills without being left behind by the speed of technological change. The survey does not suggest that every worker needs to become an AI specialist. It does show that access to learning is becoming increasingly important as AI becomes part of ordinary work.
As workplace adoption continues to accelerate, the defining workforce issue may therefore be less about whether employees can adapt to AI and more about whether organizations give enough people a fair opportunity to do so.
