Yesterday, I participated in a series of round tables on topics related to, surprise, AI. One of those discussions was about Executive Alignment on AI Priorities. One comment stuck with me.
A person at the table said, “Leaders care about the bottom line. If you can’t show the financial impact, your AI initiative dies before it starts.”
I think that’s true. Then someone suggested we shouldn’t use metrics at all. That misses the point. The problem isn’t metrics. The problem is measuring the wrong things.
Most organizations obsess over output metrics: Revenue, cost savings, EBITDA, ROI, profit.
Those matter. But they’re lagging indicators. They’re the scoreboard. The better question is: What controllable inputs create those outcomes?
Jeff Bezos built Amazon around this idea. He wasn’t trying to directly manage the stock price. He worked backward: Stock price → free cash flow → cost structure → fulfillment efficiency → picking accuracy.
Amazon’s teams focused on the bottom of that chain because those were the variables they could actually improve every day.
Weight loss works the same way. Nobody wakes up and decides to lose two pounds today. You manage the inputs: calories, protein, activity, and sleep.
The output eventually follows.
AI implementation is no different. Instead of asking: “How do we generate $10M with AI?”
Ask:
- Which workflow are we improving?
- How many hours are we eliminating?
- How much cycle time are we reducing?
- How much rework are we preventing?
- How much faster are decisions being made?
- How many employees are actually using the new workflow every week?
Those are controllable. Those are measurable. Those are the inputs that eventually produce the financial outcomes executives care about.
Executive alignment isn’t about agreeing on the revenue target. It’s about agreeing on the handful of inputs that make hitting that target almost inevitable.
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