Three Principles For Running At AI Speed

The bottleneck has shifted from how fast work gets done to how fast leaders can review it, make decisions and set the next direction. High frequency management is built around that reality, and I've found that three principles drive it:

1. No cycle is longer than 24 hours.

Meet daily, not just for status updates, but for direction. Every question gets an answer by tomorrow. Every analysis gets reviewed and redirected within a day. Alan Mulally ran Ford through its near-collapse with a version of this principle: senior leaders have exactly two jobs: execute the plan of record and make that plan better every day. If your team is producing work in minutes and hours, your review cycle needs to match it.​

2. Check in at 20% and 80%.

This is the first and last mile of enterprise AI. At 20% completion, review direction. Is the framing right? Are we solving the actual problem? This is where course corrections cost almost nothing. From there, AI handles the analytical middle: the research, modeling and synthesis.

At 80%, leaders are back in the loop to apply the judgment and institutional context that no model has—the read on how something will land, and the experience that separates a technically correct answer from the right one. Build these two checkpoints into every work cycle, and you'll catch nearly every misalignment before it becomes expensive.​

3. Give feedback in public.

Run your daily reviews in front of the team. When everyone hears the same direction and absorbs the same reasoning, you eliminate the telephone game that quietly kills organizational alignment.

Judgment is hard to document and even harder to train, but when leaders make their decisions visible, teams develop the same instincts over time. Every correction becomes a teaching moment. This is how judgment scales across an organization instead of staying locked in one person's head.​

The Gap Most Leaders Miss

Most organizations think they're further along than they are. AI-fluent teams use AI as a productivity layer. Work moves faster, but the operating model is unchanged: weekly syncs, monthly reviews and the same approval chains. These teams get more done, but they're running the same race at a higher speed.​

AI-native teams have restructured around AI’s possibilities. Cycle times collapse to 24 hours. Human judgment concentrates at the edges: direction-setting at the start and finishing touches at the end. Feedback happens live. The quality gate shifts from "Did the manager review the deliverable?" to "Did the team align on the decision and why?" Every function (finance, sales, operations, product, etc.) starts running the way software engineering's best teams run today.​

In a world where execution has become fast and cheap, the bottleneck is decision quality and decision speed. A manager’s output becomes the speed and quality of their decisions, multiplied by their team's ability to wield AI.​​

Start with cadence. Run the 20/80 checkpoints. Give feedback where everyone can learn from it.

In this way, you can build the conditions that let AI compound across your organization. I've seen teams make more progress in three days than in the previous three months by following this approach. That's what happens when your management cadence finally matches your execution speed.​

Strategy is the sum of decisions you make and execute over time. With AI making your organization faster at the execution part, you need to match the cadence and raise the frequency.​​

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