Perspective
Stop Using AI to Make Courses Faster
Using AI to accelerate the same course-first process gives you more output, not necessarily better performance.
Most AI conversations in L&D begin with efficiency. Generate objectives. Draft a storyboard. Create scenarios. Build a quiz. Turn a deck into a course before lunch.
01Speed is a tempting goal
Most AI conversations in L&D begin with efficiency. Generate objectives. Draft a storyboard. Create scenarios. Build a quiz. Turn a deck into a course before lunch.
That can save time, but it also preserves the assumption that the requested course is the right answer. We automate the existing process and call it transformation.
02A faster mistake is still a mistake
If the problem is unclear expectations, a broken tool, missing access, weak management, or information buried in six systems, a faster course does not help. It creates a polished detour.
AI removes enough friction that teams can move from request to artifact before anyone pauses to challenge the request. The output looks credible, so the assumption underneath it becomes harder to question.
The best use of AI is not faster production. It is better investigation before production begins.
03Use AI as an investigator
Give AI messy inputs before you give it a production brief. Ask it to compare policy with workflow, surface contradictions, group support tickets, identify missing decisions, and generate questions for performers and managers.
The point is not to let the model diagnose the organization. The point is to expand what you can inspect and help you notice patterns worth validating with humans.
04Make options before assets
Ask for multiple response patterns: practice, performance support, workflow changes, manager coaching, tool guidance, or no learning intervention at all. Compare them against the moment of need and the cost of being wrong.
This is where speed becomes useful. AI can help you explore more possibilities while the work is still cheap to change.
05Earn the right to build
Once the problem is clear, use every production advantage available. Generate drafts, variants, examples, and prototypes. But make production the result of a decision, not the default opening move.
The goal is not to make courses faster. The goal is to solve performance problems with less waste.
Take the thinking further
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