A learning leader recently told me that her organization was providing a company-wide subscription to AI training. She had been impressed by the offerings and scope: The course catalog spanned everything from AI for beginners to advanced prompt engineering and agentic commands. The price per learner was hard to beat, and thousands of employees were rapidly beginning to enroll.
A few months later, disappointment had set in. Usage of these training modules had dropped drastically, and most people—and teams—were working exactly as they had before.
That narrative has become all too common as AI adoption accelerates. Many organizations are deploying large, asynchronous large content libraries, assigning AI courses at scale, and assuming that access will deliver capability. But in their rush to stand up AI training programs, they’re missing a crucial point: AI skills, like all skills, are not simply a function of exposure to new information. AI skills depend on developing uniquely human capabilities in an engaging way that’s not simply a box-checking exercise.
In other words, buying AI training is not the same as buying the mindset and behavioral changes needed to adopt and implement AI.
See also: Caveat emptor: how the AI gold rush is repeating HR’s old mistakes
AI training programs struggle to build human-centered skills
Many of today’s AI training programs struggle to build these human-centered AI skills even when they’re tailored to users’ experience level. That’s because they typically offer one-size-fits-all, generic content that quickly becomes out of date—especially in our rapidly changing workplaces. Heavy on lectures and passive learning, these training programs can feel outdated and irrelevant, especially in a world where tools, workflows, guardrails and best practices change quarterly.
In addition, instructors are often someone outside the workplace, and course materials tend to be disconnected from what’s happening in the moment and on the ground. The courses themselves are often not aligned to a company’s actual tools, products or customers, and they usually do not reflect a company’s current governance, risk or compliance policies.
Because learners cannot see a direct line from these courses to their day-to-day roles and responsibilities, they may watch the videos and complete the assessments, but still go back to working just like they always have. In some cases, learners report just using AI itself to answer the questions, quickly passing through the material with little engagement or investment. While companies can say they’ve rolled out AI training across the entire organization—which is accurate—Monday morning behavior hasn’t changed.
What would a better approach look like? From my own experience in learning and development, the most effective AI training programs tend to be led by practitioners, delivered at the point when they are needed and connected to real workflows.
Instructors with traditional credentials and active professional backgrounds can deliver the best of both worlds—the knowledge of how AI works and how to work with AI, and the ability to help learners connect this new technology to their working lives. AI trainers are more than just instructors. They’re also mentors who can share what has worked and what is still emerging. Practitioner-led sessions can provide highly contextualized instruction, with datasets, examples and exercises drawn from an organization’s current environment, as well as learning aligned with a company’s AI tools, governance and business goals.
AI training programs need to be role-specific
AI training should be role-specific as well. Real-time training has proven to be an effective approach to upskilling, and AI training embedded in the work that employees actually perform can support sustained learning and engagement. Because practitioner-led training can be so timely, it’s leading-edge instruction that can be implemented on the job quickly. If training ends Thursday afternoon, team members can use their new skills and knowledge Friday morning.
Perhaps most importantly, practitioner-led training injects a dose of humanity into L&D efforts around AI. AI skills are not simply technical skills. They depend on human judgment and discernment, such as knowing whether to trust AI outputs and when to challenge them. AI capability requires creativity, critical thinking and analytical skills, alongside precision problem-framing and deep problem-solving. They demand employees make prompt and accurate ethical, compliance and risk-mitigation decisions.
Teams must learn how to redesign workflows so AI augments their work, but humans also must remain in the loop to ensure quality and compliance. AI skills are really human capabilities that require human collaboration, feedback and continuous practice in real-life scenarios to build the capacity for employees to apply these new skills directly and confidently to their day-to-day work.
To tease out potential differences in these approaches to AI training, organizations should measure what really matters: whether the training they’re offering is actually improving financial, operational and organizational outcomes. As General Assembly reports in our new State of Tech Talent report, more than four in 10 HR professionals say difficulties in measuring the value of training programs often limit further investment. Rather than tracking AI capability by monitoring only the standard set of key performance indicators, organizations should measure and analyze factors that matter most. Certainly, this can include quality, efficiency and performance, but it should also measure any associated impact on customers, employees and partners. These metrics can more accurately capture the impact and effectiveness of AI training and whether it’s worth continuing it.
Organizations able to shift from merely accessing content to building actual capability won’t only see higher completion rates, greater engagement and increased evidence that employees are changing how they work. By adopting human-led AI training programs that promote applied learning in the context of real work, learning leaders can develop AI-capable employees who work actively and effectively, strengthening their organizations in the process and improving their products and workplace cultures. The organizations that lean into this work will find that AI doesn’t just automate more tasks—it actually helps them work better.
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