Let's be honest. Most business leaders think they're using AI. A lot of them aren't. They're passengers.
In Episode 55 of Revenue Rewired, Jay and I get into a framework that is starting to define how companies evaluate AI maturity inside their organizations. And it's not about which tools you have. It's about how you're actually using them.
For the full breakdown and actionable tips, listen to the full episode on Apple Podcasts, Spotify, YouTube, and Amazon Music.
The driver-versus-passenger concept is emerging in AI consulting circles as a way to assess AI competency at the organizational level. On the surface, it sounds simple. But once you start applying it to your team, things get uncomfortable fast.
A passenger is someone who opens ChatGPT, types a generic question, reads the response, and copies and pastes it into a doc. They feel like they're using AI.
A driver is someone who treats AI like a thinking partner. They push back. They probe. They run the same prompt through multiple LLMs like ChatGPT, Claude, and Perplexity to compare outputs. They bring what we call a Socratic prompting mindset, which means they keep challenging the system until they get something worth acting on.
The difference in output quality between these two approaches is significant. And the gap is only going to grow.
One of the things Jay brought up that I think gets overlooked is the hallucination problem. Early on, a lot of people had bad experiences with AI confidently giving them wrong answers, and that eroded trust. A lot of those people quietly went back to doing things the old way.
When you walk into AI with skepticism and a low-effort prompt, you're going to get a low-effort result, and that just confirms the skepticism. You stay a passenger. Drivers don't accept the first output as final. They treat it as a starting point, ask follow-up questions, push on assumptions, and add context until the output reflects what they need. That's a skill that takes a little practice to build but pays off fast.
This came up in our leadership meeting, and I want to be direct about it because I think it's causing real confusion inside a lot of organizations.
AI doesn't save time. It raises the ceiling on what you can accomplish in the time you already have.
If you used to spend two hours reading summaries and you can now get through them in thirty minutes with AI, you're not taking a ninety-minute walk. You're filling those ninety minutes with more work, which is actually the whole point. But if you're selling your team on AI as a way to reduce their hours, you're setting up a disconnect between the promise and the reality, and that disconnect creates frustration.
What AI actually does is let you accomplish more without burning people out on low-value tasks. That's a very different message, and it matters for how you roll this out inside your organization.
I said it on the episode, and I'll say it again here. If you're a CEO, COO, or founder who's waiting for your marketing team to figure out your AI strategy, you've already made a mistake.
AI isn't a marketing function. It's a business-wide operating shift. Your CMO is probably already overwhelmed, and layering AI ownership on top of their existing responsibilities isn't a strategy. It's avoidance.
Jay shared a story about a mid-market business owner he met for breakfast. Smart guy, mid-fifties, running a real company. He was frustrated because his younger staff weren't adopting AI, but when Jay asked how he used it himself, the answer was almost nothing. Strong leaders lead by example, and if you're not using it, you really can't expect your team to either.
Every time you push an AI decision off, it doesn't disappear. It just sits there, taking up space in the back of your mind, like the garage you've been meaning to clean out or the closet that keeps filling up. It creates what I'd call mental residue, and it compounds over time.
The moment you start doing something, even something small, that weight lifts. And you don't need a massive rollout plan to get started. As the CEO of Section pointed out in a recent webinar, AI is already built into most of the tools you're already paying for. There's no budget excuse and no good reason to keep waiting.
Ryan, our Director of Service Operations, shared a simple habit during our quarterly planning session that I think is genuinely worth stealing. Take a prompt you've already built and run it through ChatGPT, Claude, and Perplexity. See what each one gives you, pick the strongest output, and then keep refining from there.
Each platform has different strengths. ChatGPT tends to shine on creative and conversational output. Claude handles nuance and document-level reasoning well. Perplexity is great for research that needs live citations. Knowing those differences makes you a smarter driver and helps you stop treating every LLM like it's the same tool.
My answer here might surprise you. Don't start with a new tool. Start with what you're already paying for.
Most companies have AI baked into their existing software stack and aren't using it. Microsoft Copilot, Notion AI, HubSpot's AI features, Google Workspace, they're already there. Get comfortable with those first and build the habit before you start adding new subscriptions to the pile.
The goal in phase one is to get leadership aligned on the value and start to build real internal use cases. Phase two is about accountability, ownership, and experimentation across departments. But you can't get to phase two if phase one is still stuck in 'we'll get to it eventually.'
A driver brings a challenger mindset and treats AI as a thinking partner rather than a shortcut. They push back on outputs, add context, and keep refining until they get something actually useful. A passenger copies and pastes whatever the first prompt produces and moves on.
Look at output quality, not usage volume. A team racking up large AI bills isn't necessarily a team using it well. Better metrics include ROI per employee and how quickly your team tests, fails, and pivots on AI experiments.
Not your CMO. AI's a business-wide operating shift, not a marketing function. Someone at the leadership level needs to own it with dedicated focus, whether that's a Chief AI Officer, an internal champion, or, in smaller companies, the CEO directly.
Not exactly. It raises the ceiling on what you can accomplish in the same amount of time. You're not banking hours, you're doing more with the ones you have. That distinction matters a lot for how you set expectations with your team and how you measure ROI.
Start with the AI features already built into tools you're paying for. Most companies are sitting on unused capabilities inside Microsoft, Google, HubSpot, and others. Get comfortable there before layering in new platforms or subscriptions.
If this episode resonated with you, subscribe to Revenue Rewired and share it with a business owner who is still on the fence. And if you want to go deeper on aligning your revenue engine, check out the Revenue Rewired book on Amazon.