Move beyond using AI for data consolidation and generation by treating it as a tough critic. Prompt it with questions like, "What have I missed?" or "If you were a top consultant, what would you have spotted?" This reframes the AI as a thought partner, forcing it to challenge your assumptions and uncover strategic blind spots.

Increased efficiency from AI should not automatically be filled with more tasks. Instead, this newfound capacity should be intentionally allocated to "thinking time"—marinating on hard problems. This slow, System 2 thinking is crucial for leadership and judgment.

The most effective users of AI tools don't treat them as black boxes. They succeed by using AI to go deeper, understand the process, question outputs, and iterate. In contrast, those who get stuck use AI to distance themselves from the work, avoiding the need to learn or challenge the results.

Users who treat AI as a collaborator—debating with it, challenging its outputs, and engaging in back-and-forth dialogue—see superior outcomes. This mindset shift produces not just efficiency gains, but also higher quality, more innovative results compared to simply delegating discrete tasks to the AI.

A powerful workflow is to explicitly instruct your AI to act as a collaborative thinking partner—asking questions and organizing thoughts—while strictly forbidding it from creating final artifacts. This separates the crucial thinking phase from the generative phase, leading to better outcomes.

After an AI completes a task, use the time saved not to switch tasks, but to deliberately 'go deeper' on the output. This final human touch of polishing and refinement—similar to using leftover time in a Pomodoro session to improve upon completed work—is what adds taste, quality, and separates great work from generic 'slop'.

The greatest wins from generative AI will come from questioning and eliminating old processes, not just making them faster. Leaders should challenge teams to use AI to "do different things" entirely, like questioning the need for a report in the first place, rather than just using AI to write it faster.

Time saved from AI-driven efficiencies must be consciously reallocated to strategic tasks that AI can't do, like deeper customer research or improving sales enablement. This compounds the value of the initial time saving, but only if that time is actively protected and reinvested.

Since AI can generate output rapidly, the differentiator is no longer speed but the quality of your judgment and clarity. AI acts as an amplifier; if your input lacks taste or direction, you'll simply produce "garbage faster." The most valuable skills become decision-making and refinement.