The work that eats an afternoon can be done in the time it takes to read this.

The report lands. The meeting is later today. Somewhere inside all those pages of numbers, commentary and tables is the story you are going to have to stand up and explain. It might be a board pack, monthly management reporting, a quarterly operations review, a sales pipeline readout. The format changes. The job does not.

Before you walk into the room, there are usually three things to work out: what actually changed, what people are going to ask you about it, and how to turn both into a clear narrative. That work matters, and it is also exactly the kind of work AI can compress, not by making the decision for you, but by getting you from report received to ready for the room much faster. Here are three ways to use it before your next meeting.

1. Find the story hiding in the report

A good report contains all the detail. What you need first is the signal: what changed enough to matter, where performance moved against plan, which number needs explaining, and what looks different from last time. Instead of working through the document page by page hunting for those movements, AI can take a first pass across the whole thing and pull the exceptions to the surface.

That distinction matters. You are not asking AI to shrink forty pages into five. You are asking it to tell you which three things deserve your attention first. Perhaps the headline number is on plan, but something underneath it moved in a way that will get noticed. Perhaps a figure looks stable only because a cost shifted into the next period. Perhaps a single area, one region, one product, one account, explains most of the change. Those are the things you want in hand before the meeting, whatever kind of report you are holding.

Now you are starting with the story rather than searching for it.

2. Interrogate it before someone else does

Finding the story is only half the job. The next question is usually: what are they going to ask me about? The uncomfortable questions in a meeting are usually predictable once someone looks at the numbers with fresh eyes, and AI can play that role before the room does. Ask it to approach the report like a skeptical board member, investor or division head, and to tell you where the explanation is weak, which assumption deserves challenging, what number looks unusual, and which conclusion the evidence underneath does not fully support.

This turns AI into a pressure test rather than just a summarizer. It might point out that the commentary attributes a change to one cause when the figures point to another. It might notice that a plan leans heavily on two things going right this month. It might surface a movement the written narrative barely acknowledges. None of that replaces your judgment. It means the hard questions reach you before the meeting does, so you are better prepared to answer.

Better to hear the question from AI ten minutes before the meeting than from the Chair ten minutes into it.

Agreement and disagreement are both signals

Put an important question to several independent models and, broadly, one of two things happens. Both are useful.

They converge. Different models examine the information and independently land in the same place. That does not make the answer automatically correct, judgment still matters, but it is a far stronger footing than one model’s interpretation on its own.

They diverge. One model sees it differently, and that is arguably even more valuable. A split answer reveals uncertainty hidden inside what looked like a perfectly confident response, and hands you the chance to check the assumptions before the answer becomes a decision.

A single model cannot give you that. It gives you its answer.

3. Draft the commentary

Once you know what changed and where the questions will come from, there is still the part that quietly eats the most time: writing it up. The executive summary, the management commentary, the explanation of what changed, why, and what happens next.

This is where the evidence for AI is strongest. In a preregistered experiment published in Science, 453 professionals using an AI assistant for professional writing tasks completed them 40 percent faster, with output quality rising 18 percent as rated by independent evaluators (Science, 2023).

That does not mean handing the narrative over to AI. It means starting from a structured first draft instead of a blank page. The quality of that draft depends on what you give it: the report itself, the movements you want covered, the audience you are writing for, and the tone and format you expect. Give AI the report, the movements you want covered and the audience you are writing for, and have it draft the narrative while tying every statement back to the underlying numbers. Your time then moves from assembling sentences to the work that actually needs you: challenging, editing, adding context, and deciding what your audience needs to know.

That is a far better starting point than a blinking cursor.

The catch: speed is only useful if you can check the answer

There is one condition on all three of these, and it is the whole game. The output is only as trustworthy as the evidence behind it. An answer you cannot trace back to your own numbers is a guess in a nicer font, however fluent it sounds.

That matters because AI does not remove the need to verify important work. Even the researchers behind the Science study note that real-world time savings shrink once you account for the need to fact-check the output. So the aim is not AI you trust blindly. It is AI fast enough to save you the afternoon, and transparent enough that you can stand behind what it gives you.

If the summary says a number moved for a particular reason, you should be able to see the figure behind that statement. If it flags a risk, you should be able to see what prompted it. If it drafts a sentence for the board, you should be able to trace that sentence back to the evidence. A citation does not make an answer automatically correct. It makes verification fast, and that is what turns speed into something you can actually use.

What good looks like

Good looks like getting the afternoon back and walking into the room with something you can defend. AI does the first pass across the report, surfaces the issues, pressure-tests the story and helps build the draft, and you make the judgment.

That is how Smith™ is built. You work against your own company documents and data, with answers anchored back to its source. Every prompt is elevated by your Persona: you select the role and expertise you want, and Smith shapes both its reasoning and response to match, so the work comes back written the way you need it, every time.

And because the work happens in one governed workspace, leadership also gets visibility into how AI is being used, control over who has access, and a secure home for company information, rather than sensitive reports scattered across personal accounts.

Because speed without a source is not control. Output you can stand behind is output you can defend when somebody across the table asks, “Where did that come from?”

Getting AI to do this work well, on terms you can stand behind, is a leadership decision.

Our guide sets out the questions that keep that decision in your hands: AI Under Control. A leadership guide to taking back the AI decision from shadow AI.

Want the afternoon back before your next meeting? Start a 30-day trial and put Smith™ to work on a report you already have.