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What Does It Mean to Be “Good at AI”?
Somewhere along the way, “using AI” became a proxy for innovation.
Teams are buying tools, prompting ChatGPT, and hiring self-proclaimed AI experts who check a box no one has clearly defined. But being good at AI? That’s a different question entirely. In the race to keep up with innovation, companies can find themselves stuck chasing AI trends.
According to Gartner, only 15% of CEOs believe their marketing leaders are AI-savvy, and by 2027, a lack of AI literacy is predicted to be a top-three reason enterprise CMOs are replaced. CMOs are being asked “how are we using AI in our marketing activities?” In fact, I recently got asked how I might consider using agentic AI in paid search. Don’t get me wrong – it should be asked and is rightfully being considered; however, there’s so much noise around if we’re using it that we’re not stopping and asking why or how.
It’s the AI FOMO. If we’re not using it, we’re doing something wrong. We’re getting left behind.
Right now, most businesses are mistaking:
- Access to AI → capability
- Tool usage → skill
- Output volume → impact
In the tech world specifically, the goal seems to be: “Let’s use AI.” Teams invest in a few tools, or bring on a consultant who calls themselves an AI expert. The little box that needed to be checked is now checked, and that’s usually where the initiative stops. They start using some tools that make their jobs a little easier, and that’s it.
But if you look a little closer, what’s actually changed?
- There’s more content, but not necessarily better content
- There’s more speed, but not always more clarity
- There’s more output, but not always more impact
The risk isn’t that companies aren’t using AI. It’s that they think they are and stop there.
So, it raises the question – What does it actually mean to be good at AI? Because right now, most organizations can’t answer that. And yet, they’re investing in it anyway.
Why AI Is So Hard to Define (Unlike Martech)
Part of the problem is that we’re trying to define AI skills the same way we’ve defined other marketing competencies. However, in martech, it’s straightforward.
There are clear signals:
- Are you certified in HubSpot?
- Are you proficient in Google Ads?
These are tangible, measurable indicators of skill. They’re tool-based. Repeatable. Standardized, and most importantly, known. AI doesn’t work that way.
It’s not tied to one platform. It’s evolving constantly and the quality of the output depends less on the tool and more on the person that’s using it. AI isn’t a platform you learn. It’s a layer you apply.
How to Be “Good at AI”
From what we’re seeing, being “good at AI” comes down to a set of underlying capabilities and use cases, not checkboxes.
1. Smart Problem Solving
According to research, teams that use AI strategically report up to 44% higher productivity. The difference isn’t the tool — it’s knowing what problem you’re solving first.
Strong operators don’t start with an AI tool, they start with the problem. They know when AI can accelerate something vs. when it’s the wrong tool entirely. They’re not asking, “Can we use AI here?” They’re asking, “What’s the smartest way to solve this?”
2. Prompting Further Thinking
Yes, prompting matters. But not in the way people think. It’s not about memorizing tricks or templates. It’s about clarity of thought. AI tools are called tools for a reason. They’re not thinking for us; they’re helping us think further.
The best outputs come from clear context, structured inputs and iterations. I like to treat AI as a collaborator and sounding board to help further an existing thought or solution. The right marketer knows the problem, has a solution, but could use it with some collaboration.
3. Sound Judgment and Experience
This is often the most overlooked skill. AI can generate a lot of ideas, but it can’t tell you what’s actually good. Once you’ve been talking to a tool like ChatGPT for a while, it’s built to subjectively cater to you. “That’s a great idea, Nima, but here’s another layer to add.” Good marketers know it might not actually be a great layer to add, and question it. If you question it, it might say something like “good catch – you were right to challenge that thought, here’s a better fit for you…”
People who are good at AI typically don’t accept the first output, can quickly spot the difference between a generic answer vs. a differentiated one, and know how to refine something that fits something clearer and better.
The Bottom Line
AI isn’t going to separate good teams from great ones. It’s going to amplify the gap that already exists. The teams with strong thinking, clear strategy, and sound judgment will get exponentially better. The ones without it will just move faster… in the wrong direction.
CEOs and CMOs shouldn’t be asking:“Are we using AI?” A better question is: “Are we better because of it?”
If you want to work with a team that understands what “great” actually looks like, we’re always happy to chat.
