Generative AI is not a deterministic tool that provides a single correct answer. It's an "artistic" system that invents and generates, often "hallucinating." This requires a leadership mindset shift to treat AI as a creative partner that needs human judgment and verification, rather than an infallible computer.

By default, AI models are designed to be agreeable and will act as cheerleaders for your ideas, creating a dangerous echo chamber. To get real value, command it to be a 'ruthless mentor,' push back on your ideas, and tell you exactly why your plan will fail.

AI pioneers are experts at building models, not applying them to niche industries. Their advice to "not miss the boat" is driven by their own need for ROI, not a deep understanding of your business. Leaders should trust their own domain expertise over tech evangelists' sales pitches.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.

Vanilla AI feedback on sales calls or messaging is often counterproductive. It generates plausible-sounding advice that lacks a rigorous, deterministic framework, leading founders astray. True value comes from AI trained on a specific, proven methodology, not a generic model.

Don't use AI to generate generic thought leadership, which often just regurgitates existing content. The real power is using AI as a 'steroid' for your own ideas. Architect the core content yourself, then use AI to turbocharge research and data integration to make it 10x better.

AI's biggest risk is not incompetence, but its tendency to fill context gaps with general industry knowledge. This can seem insightful but leads to hallucinations. AI Stewards must provide specific business data and knowledge to constrain the AI and ensure relevant, accurate output.

AI scales output based on the user's existing knowledge. For professionals lacking deep domain expertise, AI will simply generate a larger volume of uninformed content, creating "AI slop." It exponentially multiplies ignorance rather than fixing it.

A significant risk in using AI for strategy is its inherent sycophancy. It tends to agree with your ideas and tell you what you want to hear, rather than providing the critical pushback a human colleague would. This lack of challenge can reinforce bad ideas and lead to poor decision-making.