So You Think You Understand AI… But Do You Really?

AI is everywhere. Scroll through LinkedIn and you’ll find endless hot takes, tool lists, and “5 hacks that will change your business forever.” Open your inbox and there’s another webinar promising that ChatGPT will run your company while you sit on a beach. TV's even come with AI built in these days!

It’s no wonder so many business leaders feel they “get” AI. After all, it seems simple enough: type a prompt, get an answer. Maybe you’ve already dabbled - asked an AI to draft an email, summarise a report, or brainstorm some marketing copy. Easy. Done. Box ticked.

But here’s the uncomfortable truth: dabbling in AI isn’t the same as understanding it. And it’s definitely not the same as using it to create meaningful value for your business.

The Illusion of Understanding

AI feels intuitive because the interface is deceptively simple. Ask a question, get a response. That accessibility is both its greatest strength and its biggest trap.

When leaders mistake surface-level usage for true capability, they risk:

• Misplaced confidence: Believing “we’re already using AI” when the impact is cosmetic, not transformative.

• Missed opportunities: Overlooking where AI can genuinely improve processes, reduce costs, or create new revenue.

• Misdirection: Chasing shiny tools rather than aligning AI to strategy, data, and culture.

The danger isn’t just wasted time - it’s the false sense of progress. Businesses end up “AI-washing” their operations without ever realising tangible benefits.

Why Dabbling Isn’t Enough

Think of AI as you would finance, law, or HR. Few would trust their company’s accounts to someone who’s watched a couple of YouTube videos. Yet when it comes to AI, many leaders fall into the trap of thinking a bit of trial and error is enough.

But real value comes from more than tinkering:

• Context matters: AI without a clear business problem is just noise.

• Data matters: If your data is poor, your AI outcomes will be too.

• Governance matters: Who owns the outputs? What about ethics, compliance, and accountability? (and the scary one - who is out there listening to all this?!)

• Culture matters: Teams need to trust, adopt, and build on AI - not feel threatened or sidelined by it.

Without these foundations, AI experiments rarely scale. They stay as clever side projects, not business-changing assets.

From Curiosity to Capability

So, what does it take to move beyond amateur dabbling? Three things:

1. Clarity of purpose – start with the outcomes you want, not the tools you’ve seen trending.

2. Structured exploration – test, measure, and iterate with a roadmap rather than random trial-and-error.

3. Confidence in scale – put in place the data, processes, and governance that let successful experiments embed and grow.

At Talisman, this is what we call moving from exploring to enabling and finally to embedding AI. It’s not about complexity for complexity’s sake - it’s about turning curiosity into capability.

The Bottom Line

Yes, anyone can play with AI. But if you want AI to work for your business, not just impress your LinkedIn feed, you need more than dabbling.

The difference between amateur and professional use of AI isn’t access to the tools—it’s the approach, the structure, and the mindset.

And that’s where businesses like yours can either stumble… or accelerate.

Ready to move beyond the dabbling stage? Start with our AI Readiness Assessment or explore our Playbooks to see how you can turn experimentation into real results.