I recently watched a board chair say “good question, moving on” three times in ninety minutes. Each time, the room exhaled. The question had been acknowledged. Nobody had to answer it. That evening, I put the same three questions to my AI Board of Directors. It asked several follow-up questions I had not anticipated. Two of them changed my mind about the underlying decision.
The Toolkit Everyone Has and Almost Never Uses
The tools for critical inquiry are well established. The devil’s advocate (Advocatus Diaboli, a role the Catholic Church formalized in 1587 to vet candidates for sainthood) argues against the proposal. The Socratic method exposes buried assumptions through disciplined questioning. Dialectic, from Plato through Hegel, seeks a higher-order answer by testing thesis against antithesis. Most “best practices” documents codify some version of all three.
In the real world, however, they are rarely applied as designed. An SVP playing devil’s advocate to the CEO is structurally compromised before they say a word. They know what their bonus depends on, who championed the proposal being discussed, and who will remember being challenged in front of the boss. An executive who asks an uncomfortable Socratic question quickly earns a reputation for being difficult.
Disagreement is socially expensive. That cost hollows out every well-intentioned adversarial-thinking technique. The next question, the one that would have changed the answer, almost never gets asked because nobody in the room can afford to ask it.
Why AI Sparring Partners Work
Current LLMs do not think. They pattern-match. They have no judgment, no taste, and no scar tissue. On a good day, they’ll produce a competent answer assembled from patterns in their training data. That’s a serious limitation.
But they do have one extraordinarily useful property: they don’t care. They have no career to protect, no boss to impress, and no relationships to manage at the next industry dinner. They don’t get tired, bored, defensive, or angry. They don’t read the body language that tells the rest of us when to stop. They’ll challenge an assumption, pursue a line of questioning, or explore an alternative path for as long as you ask them to. This is not intelligence. But it is useful.
Practical Applications
Two quick examples from the adversarial environments I’ve created for myself. One is an AI Board of Directors with five personas: a F500 CFO, a COO operator, a cynical activist investor, a technical risk officer, and a chair who synthesizes. The other is a Synthetic Focus Group built from our actual newsletter subscriber data.
After months of running both, the lesson is uncomfortable. My human advisors give better individual answers. The bots give better processes. They push past the polite stopping point. They challenge premises my advisors share with me because we all work in the same industry. They have no professional history that makes certain topics awkward to raise.
The Model Is Not the Answer
AI is not a replacement for human debate. Your human advisors carry institutional memory that is in no training corpus. They have lived through prior cycles of the same mistake and remember which executive pushed which doomed initiative. They can be held accountable in ways a bot cannot. They have long-running relationships that let them tell you the things they would never put in writing. None of that is reproducible in a system prompt.
The advice AI generates is often wrong, shallow, and suspiciously overconfident. Treating raw AI output as definitive is malpractice.
That said, a well-built sparring partner forces you to think harder about your own reasoning. It exposes the move you skipped, the assumption you smuggled in, and the question you flinched away from. The value lives in what changes in your head before you write the memo, not in the words the model gives you back.
How to Build Your Own
You do not need an engineering team. You can build a credible synthetic sparring partner with one prompt:
“You are a panel of four advisors reviewing a decision I am about to make. Each of you has a defined perspective: a skeptical CFO who has seen this story before; an operator who has to execute whatever we decide; a customer who will be on the receiving end; and a regulator looking for the failure mode. None of you has any relationship with me. Your job is to build the strongest case AGAINST the decision I am about to describe. Do not balance. Do not hedge. Argue as if your bonus depended on winning. Then ask me the three questions I have not yet addressed.”
Run the prompt before you write the memo, the order is the discipline. If you state your conclusion first and then ask the model to stress-test it, the model will mostly help you confirm it. That’s laundering, not reasoning. It’s why so many well-intentioned prompts produce useless answers.
A synthetic focus group works the same way. Build five or six personas from whatever customer, subscriber, or constituency data you actually have. Ask them to react to the headline or campaign concept before you fall in love with it. Have them argue with each other.
A critical-thinking rubric works the same way too. Write down the five questions a hostile reviewer would ask (or ask the model to generate them) and force yourself to answer all five before you draft.
This Does Not Replace What You Already Know How to Do
I teach two structured-thinking techniques in my class at Newhouse. The Famous “P&G Memo” (Idea, Background, Recommendation, Rationale/Discussion, Next Steps) forces brevity and structural clarity. The Amazon “working backwards” press release forces you to write the customer-facing announcement before you build the thing, surfacing every assumption about who actually cares. Both have helped executives think more clearly for decades. Neither is going anywhere.
An AI sparring partner is not a substitute for structured thinking. It’s an amplifier. Write the P&G memo. Draft the Amazon press release. Do the work. Build the argument. Then ask the machine to attack your assumptions, expose the weak spots, and identify the risks you missed. The value of structured disagreement compounds quietly. A flawed strategy gets fixed before launch. A bad assumption gets exposed before it reaches the board. A costly mistake never happens.
Twenty-five hundred years after Socrates complained that nobody in Athens was willing to think hard enough, the tools to help us think harder are hiding in plain sight.
Every company needs a Claw strategy. Do you have one?
Author’s note: This is not a sponsored post. I am the author of this article and it expresses my own opinions. I am not, nor is my company, receiving compensation for it. This work was created with the assistance of various generative AI models.