Maureen Metcalf, Founder & CEO of Innovative Leadership Institute, helping boards & executive teams turn strategy into results in the AI era
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A leader asks their favorite AI tool to run a report identifying which office hit its numbers last week and which office is on track to hit its monthly target. The data looks valuable, so they create an automation to run this report weekly, ahead of the weekly financial review. The executive team finds the data very useful. Three weeks later, the offices are still not using the reports or the recommendations to improve performance. High-performing offices exceeded their monthly goals, while low-performing offices missed them, with no discernible changes.
The analysis worked. The recommendations were solid. The business did what many organizations do: It invested in the part of decision-making that AI accelerates and assumed that office leaders would use the information to drive business and financial performance.
The analysis doesn’t drive behavioral change without a strong foundation for using data to improve outcomes. PwC’s 2026 AI Performance Study, a survey of 1,217 senior executives, found that 20% of companies capture 74% of AI-driven returns. Of the respondents, 76% led companies with $1 billion or more in revenue. The ratio may not hold exactly for a small- to medium-sized firm. The central finding, however, applies to companies of any size: The most successful companies built stronger foundations in strategy, workforce and governance.
The Bottleneck Moved
I see consistent patterns among owners and leaders who use AI: It is dramatically faster to notice what is changing (sensing) and to make sense of it (sense-making). This can also lead to weighing options more efficiently. The time required for those steps, when done comprehensively, may be significantly reduced with AI. The volume of information and the range of stakeholders often increase when companies leverage AI, so the overall decision process may change.
Two steps have not sped up. The first is sense-giving, helping others understand the context and the direction: deciding who owns the choice, what it means for each person and what will stop so the new work can start. The second is acting and execution: getting people to change what they do and how they do it. Both depend on trust, clarity and authority, and none of those can be generated by AI; they are the activity of leaders.
Now that AI-enabled and even agent-based analysis are fast, the handoff and the transition from data to execution are the bottlenecks. Leaders are often frustrated with their team for how they execute, when the system isn’t built for fast change and efficient handoffs.
Three Moves For Owners And Leaders
Take these three actions to help your team achieve both speed and accuracy:
1. Name the decision and execution owner before the analysis starts.
AI makes it easy for anyone to make recommendations. Without a clear owner, those recommendations circulate and generate input, and often the leader believes the recommendations turn into action. Before anyone opens a prompt, decide who holds decision authority and who holds execution authority. Also, determine who must be consulted, and the date by which the decision will be made and action initiated.
2. Translate the decision into a simple direction that the team can understand.
Decisions need to drive action. The people responsible for executing must have clarity about what changes, who is responsible, what resources are required, how their actions will be measured and when. If a front-line employee cannot easily restate the direction, execution will not happen as intended. For the recommendations by the office above, that direction might be: “On November 15, we identified that three offices were on track to deliver or exceed financial and client job completion results, while two other offices were behind. For the two offices at risk of missing the goals, we identified three specific actions that must be completed by November 18. Please refer to the report for the specifics. I will walk through the recommendations with each office manager by the end of the day.”
3. Check that the intended work is in process.
Many leaders provide direction, then move on to the next issue at hand. They trust their teams understand their intention and are equipped to follow it impeccably. Many don’t confirm work in process. Set a checkpoint and ask one question: Have you seen the first visible action? If so, is it producing the desired results? If it hasn’t started or isn’t producing the desired results, revisit the execution to troubleshoot. Confirm clear expectations and ownership, identify priorities and define resources. Each issue has a different fix (clear expectations, a named owner, an explicit trade-off or a reallocated budget), and none of them requires more analysis. The check-in and action interval will vary by decision; the point is to check progress early.
The Trade-Off Leaders Are Making
AI genuinely accelerates analysis speed and often quality. The challenge is that the work that remains is the human work: setting direction, holding people accountable and staying close enough to execution to see where it is incomplete. Human work cannot be accelerated in the same way, and it is increasingly what separates companies that see the greatest AI ROI from those that have simply adopted it.
This doesn’t argue for slowing AI adoption. It argues that the most effective companies need to strengthen the human foundation to leverage the AI advances. A leader who tightens process ownership, direction and follow-through will likely see greater impact from the same tools than one who relies too heavily on them.
At my firm, we call this discipline Innovative Leadership Decision Velocity: the capacity to move from insight to coordinated action without losing speed at the handoffs. Whatever name you give it, the practical question for every leader is the same. Your analysis is faster than it was a year ago. Is your business?
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