The primary problem for AI creators isn't convincing people to trust their product, but stopping them from trusting it too much in areas where it's not yet reliable. This "low trustworthiness, high trust" scenario is a danger zone that can lead to catastrophic failures. The strategic challenge is managing and containing trust, not just building it.

Simply creating an AI "sandbox" is insufficient for risk management. Leaders who lack hands-on technical literacy tend to misjudge AI's capabilities, leading to flawed strategies and employees misusing the tools in ways that are prone to hallucination and other risks.

A key challenge in AI adoption is not technological limitation but human over-reliance. 'Automation bias' occurs when people accept AI outputs without critical evaluation. This failure to scrutinize AI suggestions can lead to significant errors that a human check would have caught, making user training and verification processes essential.

While fears focus on tactical "killer robots," the more plausible danger is automation bias at the strategic level. Senior leaders, lacking deep technical understanding, might overly trust AI-generated war plans, leading to catastrophic miscalculations about a war's ease or outcome.

The primary danger of AI in product management isn't technical failure but the abdication of critical thinking. Over-relying on AI summaries of user feedback means missing the crucial 'color' and context. Leaders risk losing their direct connection to the customer's voice by outsourcing their thinking to an LLM.

AI can generate endless answers, creating information overload. The critical leadership skill is no longer finding answers but exercising the wisdom to ask the right questions. A Citibank executive exemplified this by creating an AI version of himself to uncover his blind spots, demonstrating how leaders must provide the discernment to challenge and interpret AI's outputs.

Unlike past technologies, leaders now directly use AI for simple tasks. This limited, "happy path" experience creates a false perception of what's possible at an enterprise level, underestimating the complexity of integration, data quality, and tech debt.

Companies fail at AI strategy because their leaders haven't invested in understanding the technology's core capabilities, such as reasoning and multimodality. Without this literacy, any strategic plan for org charts, tech stacks, or workflows will be suboptimal and incomplete.

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.