To get more reliable research from AI, run the same query across multiple models or sessions. Aggregate the points where they all agree—these are likely factual. Then, focus your human verification efforts on the points where the models diverge.
For complex strategic decisions, create multiple AI personas representing different mentors or archetypes. Instruct this AI "board" to debate the issue among themselves before presenting you with a summary of their diverse viewpoints, avoiding the bias of a single AI voice.
The quality of a leader's own AI usage directly impacts their team's success with the technology. When CEOs are the most adept users, they set realistic expectations, avoid under or over-estimating capabilities, and inspire more effective organizational adoption.
Before committing to automating an operational task like a daily briefing, run the process manually with AI every day for a week or two. This trial period allows you to evaluate the output's actual utility and refine the process before locking it into a potentially flawed automation.
Instead of just giving AI a task, command it to interview you first. By having the AI ask clarifying questions about assumptions, context, and potential gaps, you can surface your own unknown unknowns and provide the necessary context for a high-quality output.
To make AI-assisted writing more effective, first create detailed personas of your target readers. Then, have these AI personas review your drafts, providing specific feedback on clarity, impact, and what would make them disengage. This allows for unlimited, targeted feedback cycles.
When providing feedback to AI on subjective tasks like writing, avoid vague comments. Instead, give it quantitative scores on specific dimensions you care about (e.g., clarity: 9/10, wit: 5/10). This gives goal-driven AI a much clearer target for improvement.