Sure, we can adopt AI. We can learn the tools, build the prompt library, generate assets, cut on production time, and hit every deadline. By regular standards, it’s all going according to plan. But something strange is happening. We’re more depleted than we were before.
Researchers at Harvard Business Review have a name for what we’re feeling. They call it “AI brain fry“: mental fatigue from excessive use or oversight of AI tools beyond our cognitive capacity. We have become the last line of defense for everything AI produces, and nobody warned us just how tiring that would be.
The Human in the Last Line of Defense
The more we implement AI into the way we work, the more our role changes from individual contributor to auditor and orchestrator. Every piece of AI-generated output needs to be checked before it gets near a learner: hallucinations, bias, tone, accuracy, and instructional soundness. That’s actually a second layer of work sitting on top of the other, and it’s cognitively expensive in a way that traditional workplace performance metrics don’t measure.
One Instructional Designer (ID) in our AI Accelerator program described it plainly:
“If something slips through, it’s your name on it.”
And things will slip through. Just ask Deloitte, the consultancy fined multiple times just this year alone for fraudulent AI-generated references in reports to the Canadian and Australian governments.
The exhaustion stems from the fact that generating and verifying are two different cognitive modes. When you’re creating, you’re making connections, holding the learner in mind, drawing on everything you know. It’s always been the creative work that sparks joy. But when you’re verifying, you’re skeptical, scanning, braced for the next error. You need to be on high alert.
While normally you would design and then later review with fresh eyes, with a human-AI partnership, you are only ever reviewing, fact-checking, opening up documents in different databases to verify, and so on. You finish a task and feel like you ran a sprint, even though nothing you did was technically hard.
The instinctive response to this kind of fatigue is that maybe your processes aren’t right. Maybe you should delegate more to AI, build better prompt libraries, create agents, and so on. But really, these strategies might be fencing us in deeper.
But to cure AI brain fry, we have to optimize our humanity. We need to stop treating AI as a hyper-fast conveyor belt we have to sprint alongside, and start treating it as a slow-cooking ingredient that requires human pairing, patience, and collective reflection. Here is how we change the workflow.
Some of this load is avoidable by being deliberate about what you hand over in the first place. Our Human-AI Delegation Framework for L&D is a way to decide that up front rather than mid-task.
Partnering With AI
Staying Focused
Because AI is fast, we fall into the trap of running three different AI chats across three different tabs while we wait for outputs. Maybe we’re even using multiple AI tools. This doesn’t help the “buzzing” mental fog associated with brain fry. Humans are notoriously terrible at multitasking, and AI managed to convince us that we can do it, simply because IT is fast. But we need to consider the mental tax of context switching and the high cost of critical review work.
Some of the work does not need a model at all. Automations to reduce repetitive tasks covers the deterministic wins that never need reviewing.
WE SUGGEST
If the AI is generating an output that takes 60 seconds, just sit there. Does that seem counterintuitive? Maybe. But productivity does not equal output. Moving fast may just be taking away from moving intentionally. Instead, what you can do is prime yourself for the next output by reflecting on the work you’ve done so far on this project, what else needs to be done, and refining your plan going forward.
PRO TIP
You may be using one chat, but be sure to clear your context or start a new conversation for new tasks. All LLM performance drops when the conversation gets long.
Chunking Reviews
Often, the process we undertake is to prompt first, and then evaluate output, and make edits immediately as we’re going through. This means we’re orchestrating, evaluating, and correcting nearly simultaneously.
WE SUGGEST
Evaluate the output with big-picture comments. What’s working, what’s not? Next, schedule some dedicated focus time to do those in-line changes. By the time you’re coming to the revision, you’re going at it with fresh eyes, and may pick up more issues. The comments you left earlier will also help.
PRO TIP
Expand on your initial evaluation by jumping on a quick call with a colleague to review the work together. Turn this into a casual ritual “AI Roast” to keep your skills sharp, your colleagues collaborative, and the team interconnected.
Establishing Limits
Because AI can generate a new iteration in seconds, it’s easy to fall into the trap of infinite prompting. You ask for a revision, it’s not quite right, so you ask again, and again, and again. By the fifth or sixth pass, you have a smoothie blend of ideas, and your brain is too fried to remember if the second version was actually the best one.
WE SUGGEST
Implement a strict three-strike rule for AI chats. If your tool cannot give you what you need within three prompt iterations, stop prompting. The AI is either stuck in a loop or the task requires human nuance that the tool cannot grasp. At this point, you are exhausting your cognitive effort as well as AI compute. Take the best of what it gave you and finish it manually.
PRO TIP
When you hit the three-strike limit, document what exactly went wrong. What made this quality subpar? Keep these saved in process documents that you can use as references when prompting AI to do a similar task later. For example, one of our IDs has a documented “best practices” for multiple-choice question generation that gets improved and added to each time we generate and review multiple-choice questions
Less is More
If you’re feeling exhausted when AI was supposed to help you do more, you’re not alone. The truth is that there is only so much we as humans can do in a day.
Smaller, more deliberate habits fix that; knowing when to stop prompting, when to step away before reviewing, and where AI cannot replace the judgment you bring. None of this requires new tools or a bigger prompt library. It requires a renewed focus on the one thing that AI can’t replicate: your own, unfried human judgment.
For the design-side version of this argument, see moving from AI fatigue to pedagogical depth.
