Don't treat AI as an omniscient expert. Instead, view it as an intern: provide clear, detailed instructions, show examples of the desired output, and always review the results critically. You wouldn't let an intern's work go straight to the board, and you shouldn't with AI either.

Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.

Jumping into AI tools without a marketing strategy and documented workflows leads to noise and frustration, not efficiency. AI should be used to augment existing team members and up-level well-defined processes, not to automate a broken system.

Rushing to adopt AI tools without a clear strategy and established workflows leads to chaos, not efficiency. AI should be the fourth step in a system, used to strategically uplevel your team and enhance proven processes, rather than just creating more noise or automating a broken system.

Despite hype in areas like self-driving cars and medical diagnosis, AI has not replaced expert human judgment. Its most successful application is as a powerful assistant that augments human experts, who still make the final, critical decisions. This is a key distinction for scoping AI products.

To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.

People often blindly trust AI output, leading to a flood of low-quality work ("AI slop"). To combat this, treat AI not as a genius, but as a lazy intern. This mindset forces you to provide context, coach it, iterate on its work, and take full accountability for the final product.

Blindly applying AI to every task results in low-quality, untrustworthy output ("slop"). The optimal approach involves using AI as an accelerator while retaining human oversight for prompting, verification, and critical judgment. Over-reliance on the AI shortcut diminishes quality and trust.

AI's success hinges on its application and the competencies built around it. Simply deploying AI tools without a strategy is like handing out magic markers and expecting art—most will go unused or be misused. The failure point is human strategy, not the tool itself.