Generative AI has moved into office work so quickly that a familiar problem has emerged: reports, analyses, and code drafted by AI get used without review, and the errors surface only later. Executives are caught between the pressure to adopt AI and uncertainty about what to hand off versus what to verify themselves. This article sums up the executive's role in the AI era in a single sentence, lays out the criteria for dividing work between AI and direct oversight, and walks through the process and checklist executives can use to verify AI output themselves.
The Executive's Role in the AI Era: Architect Who Connects Technology to Results
Executives don't need to build the technology themselves. Their job is to connect new technology to business operations and fuse the two into results. Rather than spending time learning how a model works under the hood, an executive's real task is to design where this technology plugs into the company's operations to move revenue, cost, or speed. Many executives get stuck trying to master the technology directly; that time is better spent choosing which task to connect it to — that's what actually drives results.
Framed this way, the pressure to adopt AI looks different. Using it just because everyone else is isn't adoption. Adoption means deciding which of your company's activities this technology should attach to. If there's no task to connect it to, there's no reason to rush; if there is a clear task, you can start without knowing the technical details.
What Should You Delegate to AI, and What Should You Verify Yourself?
The test is where the output goes. Work that's meant to be reworked by a person anyway — drafting, summarizing materials, cleaning up meeting notes, a first pass at code — can safely go to AI. Analysis involving numbers, documents headed to customers or investors, and decisions tied to contracts, money, or legal liability, on the other hand, need the executive's own eyes. The higher the cost to the company if AI gets it wrong, the earlier an executive needs to look at it.
In practice, it helps to put this line in a table. Note, for each task, whether the output stays internal or goes outside, and whether a mistake can be undone — then any new request to use AI can be judged against the same criteria right away. The line isn't drawn once and forgotten. If a task delegated to AI keeps producing errors, move it back to the verify side; if a task has run cleanly for a long time, widen what's delegated.
How Executives Should Peer-Review AI Output Themselves
The second pillar of the executive's role in the AI era is verification. Just as a paper needs peer review before publication, AI-generated output needs the executive to personally run a "peer review" to manage risk. This isn't the same as handing the review off to a staffer — it means the person accountable for the decision looks at the underlying evidence directly. Executives obviously can't review every single output, so this process applies only to what falls on the verify side of the line drawn above. Here's the sequence.
The point is not to jump from the draft straight to a conclusion, but to look at the evidence and assumptions first, weigh the risk, and only then decide. Checking evidence means confirming that the sources AI cites actually exist and that the numbers match the originals. Reviewing assumptions and logic means asking whether the premises and reasoning AI took for granted actually fit your company's situation. Assessing risk means writing down who stands to lose what, and how much, if this output turns out to be wrong. Only output that clears all of these steps gets used as the basis for a decision.
How deep the review goes should scale with the risk. For internal meeting materials, checking the numbers may be enough; for a document going to investors, question every assumption one by one. Send errors you find back to the person who produced the work, but note what was wrong and why — that's what raises the quality of the next draft.
A Checklist to Start Using Tomorrow
- List the tasks you've delegated to AI this week, and flag any whose output leaves the company or involves money. - For flagged output, verify the evidence and figures yourself, separately from your team's review. - Write down, in one line, any assumption AI made that doesn't fit your company. - The bigger the potential loss from an error, the more review time it gets; the smaller, the more you can delegate. - When a new technology appears, decide which of your company's activities it should attach to before you study how it works.
AI is now an unavoidable current. If you can't avoid it, you might as well enjoy it — and for an executive, enjoying it means standing in the position of connecting and verifying, neither afraid of the technology nor blindly trusting it. Someone else builds the AI. The executive is the architect who turns it into results and takes responsibility for them.
