What "Using AI" Actually Looks Like at the VP Level.
Dabbling with ChatGPT isn't using AI. Here's what actual leverage looks like.
What "Using AI" Actually Looks Like at the VP Level.
Most senior professionals are dabbling.
They ask ChatGPT a question. They get a pretty good answer. They think, "Huh, that's neat." They close the tab and go back to their day.
That's not using AI. That's playing with it.
There's a difference between having tried AI and actually using it — and that difference is showing up in who's building leverage right now and who's falling further behind while feeling like they're keeping up.
The Dabbling Trap
The dabbling trap is easy to fall into because it feels like engagement.
You read the articles. You've tried the tools. You can speak intelligently about large language models at a dinner party. You know the difference between ChatGPT and Claude and Gemini. You've generated a few documents, summarized a few reports, maybe drafted an email or two.
And then you closed the tab and went back to your actual job.
This is where most senior professionals are right now — informed enough to feel current, not embedded enough to have changed anything about how they actually work.
The gap between dabbling and using is not a knowledge gap. It's not about understanding AI better or taking another course or reading another breakdown of what the models can do.
It's a workflow gap. And it's the gap that's starting to separate executives who are building real leverage from the ones who will spend the next two years explaining why their approach still makes sense.
What Actual Usage Looks Like
Here's a concrete example of what using AI actually looks like at the VP level — not as a concept, but as a specific before and after.
A VP of Sales I work with used to spend 90 minutes every Sunday night building his weekly pipeline report. Pulling data from three different sources. Formatting it into the structure his CEO expected. Writing the narrative summary that explained what the numbers meant, what was moving, what needed attention.
Ninety minutes. Every Sunday. Without fail. For years.
He built one AI workflow. A prompt that ingests his CRM export, structures the data exactly the way his CEO wants it presented, and writes the narrative summary in his voice — his cadence, his framing, his way of flagging what matters.
Sunday nights: 8 minutes.
He didn't need to know how to code. He didn't take a course or get a certification or hire a consultant. He spent one afternoon building the workflow, testing it against his actual data, refining the prompt until the output matched what he would have written himself. One afternoon. Then it was done.
That's the difference between dabbling and using. Dabbling is asking AI a question and getting an answer. Using is identifying a task that consumes your time every week and building a workflow that hands it off — permanently.
Why One Workflow Changes Everything
The 90-minutes-to-8-minutes story sounds like a productivity win. It is. But the more important shift is what happens to the 82 minutes.
That's not 82 minutes of extra capacity to do more of the same work. That's 82 minutes that used to be consumed by a task that required no actual judgment — pulling data, formatting it, writing a structured summary that followed a predictable pattern — now available for the work that does require judgment.
The strategic conversations. The relationship building. The pattern recognition that comes from actually thinking about the pipeline rather than assembling a report about it. The preparation that makes Monday morning's conversations more effective because Sunday night wasn't spent on logistics.
This is the leverage that serious AI usage creates. Not doing the same work faster. Redirecting the time that was going to low-judgment tasks toward the work where your actual expertise compounds.
Senior executives are expensive because of their judgment. Every hour spent on work that doesn't require judgment is an hour of judgment-level time priced at judgment-level cost, producing administrative-level output. AI fixes that equation — but only if you actually build the workflows.
The One Workflow Framework
The instinct when people hear about AI leverage is to think big. AI strategy. AI transformation. Comprehensive implementation across the organization.
That's not the starting point. That's the trap that keeps executives in the research phase indefinitely — because AI strategy is a project that requires planning and alignment and resources, and it's easy to stay in the planning stage while never building anything.
The starting point is smaller and more specific.
One workflow. One task that eats your time every week. Handed off.
Not your entire job. Not your team's entire workflow. One recurring task — the kind that has a predictable structure, requires pulling from consistent sources, produces an output that follows a recognizable pattern — that you currently do manually because you've always done it manually.
Every VP-level executive has at least three of these. Usually more. The weekly report. The monthly board update. The competitive analysis that gets assembled from the same sources every quarter. The onboarding document that gets rewritten from scratch every time someone new joins the team.
Pick one. Build the workflow. Test it until the output matches what you would have produced yourself. Then move to the next one.
The professionals who are building real leverage right now aren't the ones who understand AI best. They're the ones who started building workflows while everyone else was still watching YouTube videos about what AI can theoretically do.
What Makes a Good First Workflow
Not every task is equally good as a starting point. The ones that translate best share a few characteristics.
Predictable structure. The output follows a consistent format every time — the same sections, the same sequence, the same level of detail in the same places. Pipeline reports. Status updates. Meeting summaries. Competitive briefings. These have templates, even if the templates only exist in your head.
Consistent inputs. The raw material comes from the same sources every time. A CRM export. A spreadsheet. A set of emails. A recurring report. When the inputs are consistent, the prompt can be built to handle them reliably.
Your voice in the output. The best first workflows are the ones where the output sounds like you — your framing, your emphasis, your way of explaining what the numbers mean. This is what makes AI output actually usable rather than something that needs to be rewritten before it goes anywhere.
Weekly or higher frequency. The leverage compounds fastest on tasks you do repeatedly. A task you do once a quarter is worth automating eventually. A task you do every week is worth automating now.
The Executives Who Are Getting This Right
There's a pattern in the executives who are building real AI leverage versus the ones who are still in the dabbling phase.
It's not seniority. It's not technical background. It's not even how much time they've spent learning about AI.
It's whether they've built anything.
The executives getting this right have a different relationship with the tools. They've moved past asking AI questions and into building AI workflows. They've identified the recurring tasks in their work that follow predictable patterns, built prompts that handle those tasks reliably, and freed up the time for higher-judgment work.
They're not AI experts. They don't need to be. They're domain experts who have learned just enough about how to work with AI to hand off the right tasks — and that combination is exactly what makes them more productive than peers who either know more about AI but haven't built anything, or know less and aren't trying.
The VP of Sales with the 8-minute Sunday night isn't ahead because he's technical. He's ahead because he spent one afternoon building something instead of continuing to spend 90 minutes every week doing something a workflow could do for him.
What This Means for Your Career Positioning
There's a career dimension to this that goes beyond personal productivity.
The executives who can say — specifically, with examples — "here's how I've used AI to change how my function operates" are having a different conversation with hiring managers, boards, and potential fractional clients than the executives who say "I've been exploring AI tools."
One is a capability statement. The other is a dabbling confession.
As AI fluency becomes a standard expectation at the VP level and above, the bar for what counts as AI fluency is rising. Knowing what the tools are, having tried them, being able to speak about them intelligently — that was differentiated in 2023. In 2026, it's table stakes.
What differentiates now is whether you've actually changed how you work. Whether you've built workflows that produce real output. Whether you can point to specific tasks that used to take hours and now take minutes — and explain exactly how you built that.
That's the conversation that matters. And you can only have it if you've actually built something.
The Starting Point for This Week
The gap between dabbling and using closes the same way the VP of Sales closed it. Not with a strategy. With one afternoon and one workflow.
Identify the task. The recurring one with the predictable structure and the consistent inputs. The one that takes more time than it should because you're doing manually what a well-built prompt could do reliably.
Build the workflow. Spend the afternoon on it. Test it against your actual data. Refine the prompt until the output matches what you would have produced yourself.
Then use it next week. And the week after. And watch the time add up.
The professionals building real leverage right now aren't waiting for a better understanding of AI before they start. They started with one workflow and built from there.
That's what using AI actually looks like at your level.
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Written by
Bill Heilmann