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AI capability is spreading unevenly across the workforce, and access to learning is becoming one of the dividing lines. New global research suggests that employees who use AI frequently receive greater development support, confidence, and opportunity than many of the people who keep core operations running. L&D leaders can reduce that divide by building role-based AI learning that reaches employees across functions, locations, and levels.
AI adoption is often reported as a companywide percentage: how many employees have licenses, how many teams use a tool, or how many hours the technology saved. Those averages can conceal a much more important question. Which employees are gaining the skills, support, and visibility to benefit from AI, and which employees are being left to figure it out alone?
PwC’s 2026 Global Workforce Hopes and Fears survey makes that concern difficult to ignore. Published on September 29 and based on responses from nearly 50,000 workers in 48 countries, the report found a widening divide between employees who are advancing with AI and those who are receiving less access to tools and development. PwC reported that overall access to learning and development fell from 59% to 51% even as AI use accelerated.
The finding does not prove that AI use causes better development access. It does show that workforce transformation is being experienced very differently across employee groups.
Most organizations frame the AI skills gap as a shortage of knowledge. Employees need prompting skills, tool familiarity, data awareness, or stronger judgment. Those needs are real, but they are only part of the picture.
Employees also need permission to learn, time during the workday, access to relevant tools, and examples connected to their responsibilities. When those conditions are concentrated among technical teams, senior leaders, or enthusiastic early adopters, the organization creates two learning environments inside the same workforce.
One group gets practice, feedback, and progressively more valuable work. The other watches job expectations change without receiving an equally clear path to participate.
That pattern can be especially easy to miss among operational, administrative, customer-facing, and frontline roles. These employees may not be the first users invited into pilots, yet their work often contains repetitive tasks, high-volume decisions, documentation demands, or customer interactions where responsible AI use could be valuable.
Open enrollment sounds fair because everyone can register. In practice, optional learning tends to attract employees who already have interest, confidence, manager support, and schedule flexibility.
The result is a familiar L&D problem. The people most prepared to take advantage of development receive even more of it, while the employees with the least exposure have to overcome the greatest barriers to begin.
An inclusive AI learning strategy needs a clearer structure:
TTA’s learning strategy resources can help organizations connect these decisions to business priorities, workforce roles, and measurable outcomes.
General AI awareness can introduce common language, but capability develops through work-specific practice.
A customer service employee may need to draft and verify a response without exposing sensitive information. A project manager may use AI to organize risks and meeting decisions while retaining ownership of priorities. A manufacturing supervisor may need to interpret an AI-assisted quality alert and know when human review is required. A learning professional may use AI to accelerate an outline while checking accuracy, accessibility, and instructional value.
Each person is learning AI, but the decisions, risks, and performance standards differ.
This is why an effective program begins with tasks and decisions rather than a catalog of tools. TTA’s blog on business transformation and workforce capability explains why technology adoption depends on the people and processes surrounding it.
Participation data can confirm that training happened. It cannot show whether access and capability are spreading equitably.
L&D teams should examine completion, practice, confidence, application, and performance by role, location, level, work environment, and prior AI experience. The purpose is not to create a leaderboard. It is to find groups that are missing, blocked, or receiving learning that does not match their work.
Useful questions include:
The answers can reveal whether an organization is building shared capability or accelerating a small group at the front.
AI adoption will continue to move at different speeds. That does not require organizations to accept a permanent divide between employees who have access to development and those who do not.
The practical goal is not identical training for everyone. It is a common foundation, relevant learning by role, and a visible path for employees to build capability as their work changes.
Organizations that need additional expertise can use TTA’s technical and IT training talent and experienced learning professionals to design and deliver role-based programs at the required scale. Speak to a Learning Advisor to build an AI learning approach that reaches the people responsible for making adoption work.
It is the difference between the AI capabilities organizations need and the capabilities employees can currently apply. The gap can also include unequal access to tools, training, practice time, and manager support.
Every employee may need a shared foundation in responsible use, privacy, verification, and human accountability. Role-specific practice should then reflect the tasks, decisions, data, and risks each employee encounters.
Connect learning to required work, schedule protected practice time, involve managers, provide beginner-friendly entry points, and offer examples that reflect different roles and work environments.
Review access, participation, confidence, application, and performance across workforce groups. Aggregate completion rates can hide departments, locations, or job levels that are receiving less opportunity.
