The problem with bad AI-generated work ('slop') isn't just poor writing. It's that subtle inaccuracies or context loss can derail meetings and create long, energy-wasting debates. This cognitive overload makes it difficult for teams to sense-make and ultimately costs more in human time than it saves.

Using AI to generate content without adding human context simply transfers the intellectual effort to the recipient. This creates rework, confusion, and can damage professional relationships, explaining the low ROI seen in many AI initiatives.

Relying on AI without applying critical thinking produces "work slop"—outputs that look polished on the surface but lack genuine depth or substance. This can be dangerously misleading and devalues the quality of work by giving a false sense of security.

To get executive buy-in for AI-driven work, GitHub's COO built a revenue planning deck with an agent instructed to make it look "not pretty" and non-AI-generated. The CRO and CFO didn't notice, proving the content's value. Intentionally adding human-like imperfections can sidestep bias against AI and increase adoption.

AI thrives in domains with fixed, written rules and searchable histories, like programming. In ambiguous areas like organizational conflict or political negotiation, where context is unwritten and lives in people's heads, its performance plummets. Its confident output masks this unreliability, posing a danger to decision-makers.

AI can assemble data-rich presentations, but it cannot replicate the human emotional intelligence required for stakeholder management. Understanding an executive's personal values and tailoring a message—like connecting a design system to company values—remains a critical and uniquely human skill for gaining buy-in.

AI lacks the 'social IQ' to understand team history, internal jargon with baggage, or an audience's emotional state. Leaders must provide this 'political calibration' by editing AI outputs, removing potentially divisive terms, and shaping the narrative to be persuasive for the specific context.

Research highlights "work slop": AI output that appears polished but lacks human context. This forces coworkers to spend significant time fixing it, effectively offloading cognitive labor and damaging perceptions of the sender's capability and trustworthiness.

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.