The ever-greater need for critical thinking in the age of AI has been a consistent theme of mine in this Substack. Humans are so-called cognitive misers. It comes very naturally to us to make use of techniques that make things easier for us, and humans have long used technology to outsource mental effort (digital calendars, notifications, GPS, calculators, search engines, translation apps). The big risk now of course is outsourcing our thinking to AI.
A whole series of studies have revealed the hidden (and not so hidden) downsides of this. There was the well-discussed MIT study from last year which demonstrated how research participants that had used an LLM to complete a task were significantly less engaged with that task (and had less recall of it) than a similar group that had only used their brains. Anthropic’s randomised control trial with software developers similarly found that those who used AI assistance scored 17% lower on tests of the skills they’d just used. This study from the Marshall School of Business found that passive reliance on AI at work reduces ownership, self-efficacy and meaning but that active collaboration mitigates these effects. This Wharton research showed that 80% of people accept a wrong answer from AI without questioning it and how adopting AI outputs without judgment can override human intuition and judgement.
Retaining cognitive sovereignty in the strategic process is key to mitigating these risks and is essential for strategists. That means the human stays as the driver of the process. Simply following the next steps that the AI is recommending without due consideration means that you have lost control of the process. It’s so easy to take the output that the AI confidently gives us and cut and paste it, but allowing the LLM to give us answers or steer the next steps without proper evaluation or scrutiny has ramifications. The recent BetterUp/Stanford study for example, showed the downstream consequences of workers using AI to generate content that masquerades as good work (40% of workers in that study said that they had received so-called ‘workslop’).
None of this is inevitable, however. How we use AI is at least as important as the technology itself. Research from the Universities of Michigan and Chicago on what they call ‘machine fluency’ found that ‘73% of variance in AI performance derives from the user’s ability to instruct the AI’. Another Anthropic study showed that strong behavioural indicators of ‘AI fluency’ included relatively simple things such as iterating and refining AI outputs rather than accepting the first outcome, clarifying your goal up-front, questioning and push back on what you’re given, and defining how you’d like to interact with the AI.
It’s so easy to slip into bad habits, but a lack of critical thinking in our daily use of AI is particularly hazardous when we are using it to help navigate complex challenges or opportunities. So with that in mind, I wanted to highlight several specific techniques (relevant to prompting, project instructions or agent design) for using AI in any strategic process that go beyond the more obvious evaluation, questioning and inference. The common thread with all of these is that they push the AI out of its default mode (convergence, synthesis, confident narration) into a mode that’s more analytically useful (surfacing disagreement, exposing gaps, testing robustness). AI’s natural tendency is to resolve complexity, but the strategist’s job is to prevent that from happening too early.
Think-Prompt-Think
When I wrote about think-prompt-think as a technique it was an appeal to be less passive and more thoughtful in our use of AI, particularly in the domain of strategy, or complex tasks. Start with your brain. Define exactly what it is that you want to do, or what the hypothesis is that you want to explore, or how best to approach your challenge. Then when you begin to use the AI (to open up new pathways for exploration, reframe a problem, source and summarise inputs, challenge, extend and fine-tune), you will use it in a much more deliberate and considered way, which means better outputs. And then you switch back to your brain to bring in your human judgement and intuition to assess, improve, edit, finesse, and add your own voice.
Put simply, if we accept answers at face value, we stop questioning. The risk, particularly in strategy, is premature convergence. Starting with the AI’s output anchors everything that follows. So forming a discipline around developing your own hypothesis or framing of what matters first means that AI can then challenge or stress-test those assumptions, open up new or different pathways, and enable divergent perspectives to enhance understanding. The sequence is the point.
Keeping the question open
By default AI likes to move towards a clean, well-structured answer as quickly as possible but good strategy requires you to sit with ambiguity, to hold competing possibilities in tension, and to resist the comfort of a nice, tidy narrative. Adversarial prompting can be really useful here. Rather than simply asking for the counterarguments to any analysis (which tends to produce a polite list of caveats), try pushing it to make the strongest possible case that its own conclusions are wrong. This forces the model out of its natural love of synthesis and can help to surface considerations that it glossed over in the first pass. It works better still when you give the AI a specific perspective to argue from. If you’re exploring a market entry strategy for example, and the AI has given you a case built around competitive positioning, ask it to argue from the perspective of someone who believes the category itself is in structural decline. Or from someone who thinks the real opportunity is in creating new demand rather than competing for existing share. Each of these produces genuinely different thinking rather than variations on the same theme.
This connects to a broader technique which is to ask for competing interpretations rather than answers. The cognitive miser in us wants to go straight to the solution but good strategy works by exploring a range of plausible explanations before committing to one. When you ask AI for three conflicting interpretations of the same market situation, you can quickly uncover whether the model is actually thinking differently each time or just reorganising the same information under different headings. If all three interpretations feel pretty similar, that may be a sign that AI is giving you the same analysis but in slightly different clothing.
One way I like to do this is framework switching. Get the AI to reinterpret the same situation through two or three different theoretical lenses. That market entry question looks very different through a jobs-to-be-done lens than it does through a Porter’s five forces lens. AI responds well to structured reasoning so this plays to its strengths, but it also exposes where interpretation genuinely shifts depending on the frame you apply. If the analysis moves meaningfully, you’ve probably found where the real strategic work needs to happen.
The thread through all of this is the same – don’t let the AI settle too soon. Keep it generating tension, alternatives, and disagreement for longer than feels comfortable. The moment you allow premature convergence is the moment you’ve handed over the strategic thinking to the machine.
Notice what’s missing
AI outputs feel seductively complete, and can read as though the scope has been fully covered in the analysis. But a good strategist develops a feel for when this completeness is an illusion and fluency is being mistaken for rigour. The most common gap that I’ve found is where the AI has presented a contested issue as settled. If you’re researching a category or market dynamic and the AI gives you a clean narrative, it’s worth asking explicitly where credible sources disagree on this. Often the disagreements are more strategically interesting than the consensus. Asking the AI to disaggregate its sources, to show you which ones support a claim and which ones complicate or contradict it is a simple discipline that consistently surfaces more useful material than the default synthesis.
Asking the AI to identify what the analysis hasn’t covered (that a more rigorous thinker would expect to see) sounds almost too simplistic but the results are often quite revealing. It pushes the LLM beyond its tendency to default to a narrow band of mainstream thinking and this matters because good ideas or insights can often live in the outliers, edge cases or niche behaviours. You can push this further by asking the AI to rate its own confidence in each element of its analysis. I’ve found this particularly useful when working with data or when synthesising across multiple inputs. If you ask it to score the analysis out of ten it will often come back with a mid-range score, a breakdown of where it’s strongest and weakest, and a clear differentiation between claims it can genuinely support and those it has essentially guessed at. You can extend this into what good forecasters call falsification by asking the AI what evidence or development would make its analysis completely wrong. If it can’t articulate clear conditions under which its conclusions would fall apart, the analysis is probably weaker than it sounds.
None of these techniques replace domain knowledge of course. You still need enough understanding of your subject to sense when something important has been left out. But that’s the point. Short cuts are risky in strategy work, particularly where they erode understanding.
Question the question
First-order scepticism is checking whether the AI’s facts are right but second-order scepticism is about checking that the framing of the problem is itself correct. This matters because AI can easily import assumptions about how the question should be structured. Going back to our market entry example from earlier, the AI might assume a framing around competitive positioning because it has defaulted to the most common framing in its training data and built from there. Deliberately reframing a challenge from multiple perspectives forces it to consider other angles (demand creation, impact of regulation, shifts in consumer motivation). Confirmation bias can often make this worse. If your prompt contains an assumption (perhaps without you realising it) the AI will build on that assumption rather than question it. Presenting the situation without your interpretation and checking what framings would give you the best perspective helps avoid this.
Large language models are very good at reproducing mainstream thinking fluently and are much less good at surfacing emerging, minority, or genuinely unusual perspectives unless you specifically ask for them. If an AI tells you that something is ‘widely recognised’, that should be a real signal to a strategist to start questioning. It’s a trap for strategists because genuinely different thinking and exceptional ideas rarely live in the consensus.
A final thought on this. Most AI training focuses on the tools themselves rather than on how to think with them. But as the research clearly shows, the real differential lies in the quality of human judgement when working with AI. The ability to spot flawed reasoning, to sense when complexity has been flattened, and to know when the AI has quietly done your thinking for you. Rather than AI skills, these are core strategic skills. And in an age where AI makes it easier than ever to skip the thinking entirely, they have never been more important.
A version of this post appeared on my weekly Substack of AI and digital trends, and transformation insights. To join our community of over thirteen thousand subscribers you can sign up to that here.
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