Power: AI Exposes the L&D Crisis

AI Exposes the Skill Problem

Many organizations say they are trying to prepare for AI. In fact, many do little. They give people access to tools, offer introductory sessions, and motivational testing. That could create a job. It doesn’t really create skill. This is an important distinction. AI doesn’t just introduce new tools into the workplace. It reveals whether organizations understand how power is built, sustained, and used in real-world situations. And in most cases, they don’t. This is why many of the current answers feel incomplete. Leaders have a sense of urgency. The workers are trying. Learning teams are under pressure to act quickly. Yet much of what is being presented still depends on shaky assumptions about how performance actually improves.

A Mistake Many Organizations Make

A common pattern is emerging. A new pressure is emerging. AI is becoming a topic. Workers need to be “highly skilled.” A lesson is proposed. Or, in response to academic fatigue, someone says that learning should just happen in the course of work. Both answers would miss the point.

The problem isn’t whether the answer is a tutorial, a resource, a quick library, or a workflow tool. The issue is whether the organization has correctly identified what kind of problem it is trying to solve. Often, three very different needs blur together:

1. Building strength before working out.
2. Support for recall during operation.
3. Fixing a problem that was never about learning in the first place.

When that distinction is not clear, organizations often choose solutions based on trends, convenience, or familiarity rather than operational need.

Why the “Workflow” Discussion is Often Oversimplified

Workflow support is helpful. In most cases, it is essential. But it doesn’t replace skill. A checklist can support recall. A quick guide can reduce friction. Career assistance can help someone carry out a process that is known to be reliable. These tools are important when the power is already there, and the real problem is access, consistency, or memory at the time of need. They are less effective when the job requires judgment, prioritization, trade-off decisions, or action under pressure.

People can’t rely on timely support to build a skill they don’t yet have. They can only use that support properly if the basic skills are already there. That’s even more important in AI-related work. If employees don’t understand what a good product looks like, where the risks lie, what needs to be escalated, or when human judgment should override the tool, then access to AI won’t make them better. It may simply make bad decisions quickly.

AI Literacy Is Not a Problem-Adjusting Tool

Most AI learning efforts are focused on platforms and information. That is understandable, but not enough. The most important questions are practical and have a role:

1. What function should AI support here?
2. What decisions still require human judgment?
3. What information can or cannot be used in the tool?
4. What does acceptable output look like for this job?
5. When is a review, exit, or promotion required?

Without that being clear, workers are left to develop. Others avoid AI because the boundaries are unclear. Some use it carelessly because the guardrails are weak. In both cases, the organization ends up being inconsistent. This is why learning AI should not be considered a general topic of awareness. It should be defined in relation to actual work, actual decisions, and actual performance levels.

A Better Question for L&D and Business Leaders

Instead of asking, “Should this be a lesson?” or “Can we support this in the workflow?” a better question is: “What is the least invasive method necessary to achieve the level of skill that the job really requires?”

This question changes everything. Sometimes the answer will be structured practice, simulation, coaching, or a directed action plan because the skill needs to be built before the action. Sometimes the answer will be operational support because the power is already there and the need is reinforcement or recall. Sometimes the answer will be one, because the problem is unclear process, poor system design, weak management, or unclear expectations.

This is where many organizations still struggle. They move quickly to build learning assets without first deciding what needs to be built, what can be supported, and what needs to be resolved elsewhere.

What AI Really Reveals

AI works like a stress test. It reveals whether organizations can distinguish between knowledge and judgment, between support and skill, and between work and skill. It also brings up an old problem that existed long before AI: most organizations don’t have a content problem. They have a problem with clarity. They have not clearly defined:

1. What a great looking performance.
2. What are the most important decisions.
3. Which skill should be pre-existing.
4. When support is sufficient.
5. Where accountability lies.

When those questions remain unclear, study groups are often asked to solve the wrong problem. More content is being created. Additional resources are pushed into the workflow. More awareness is brought. However, the underlying performance problem remains the same.

What This Means for Learning and Development

This time is not just about going faster or being more productive. It’s about being more precise. For L&D, that means resisting two equal and opposite pitfalls: automating studies for every problem, and over-correcting by treating workflow support as the answer to everything.

A multi-strategic role is to help the organization make better intervention decisions. That starts with a few practical questions:

1. What performance should improve?
2. What skill should be readily available at the time of need?
3. What can be supported during execution, and what must be built in advance?
4. Is this really a learning problem?

Those questions are simple, but they force better choices.

A Final Thought

AI isn’t just changing the tools people use. It raises the bar on how organizations think about talent. Access is not power. Knowledge is not judgment. Support is not the same as preparation. Organizations that respond well will not be the ones that move quickly to produce AI content or embed more resources into the work process. They will be clearer about what skillful performance requires, clearer about how skill is built, and more selective about when learning is the answer. That is a very demanding answer. And it’s very useful.