Why Business Leaders Should Be Cautious
Every technology wave creates its own experts. The internet did. Cloud computing did. Digital transformation certainly did. Now it’s happening again with AI. LinkedIn is full of AI strategists, AI specialists, AI thought leaders, AI consultants, AI transformation experts, AI futurists, and AI gurus. Some have deep technical knowledge. Some have years of experience applying AI in real business environments. Many are genuinely helping organisations navigate an exciting and uncertain space.
But there is a question business leaders should be asking: Can anyone truly be an AI expert right now?
Expertise Still Matters
Before we go further, it’s important to acknowledge that expertise does exist. There are researchers, engineers, data scientists, ethicists, governance specialists, and practitioners who have spent years, and in some cases decades, building deep knowledge in specific areas of artificial intelligence. We don’t question whether surgeons, cybersecurity professionals, or scientists can be experts simply because their fields continue to evolve. In many ways, AI is no different. The challenge is that “AI” has become an umbrella term covering everything from large language models and machine learning to governance, automation, change management, security, and ethics.
Someone may be an expert in AI governance without being an expert in model development. Another may have deep technical knowledge of machine learning but limited experience helping organisations adopt AI successfully. Both may be experts, but in very different ways.
Perhaps the real issue isn’t whether AI experts exist.
It’s whether we are being clear about what someone is actually an expert in. For business leaders, that distinction matters. Broad claims of AI expertise can sound impressive, but understanding the depth, context, and evidence behind that expertise is often far more valuable. As AI becomes a bigger part of business conversations, perhaps the more important question is not: “Are they an AI expert?”
But rather: “What is their expertise, and how do they demonstrate it?”
The Pace of Change Changes Everything
Traditionally, expertise was built over years. You studied a field, then gained experience. You developed specialist knowledge. You became recognised as an expert. AI doesn’t behave that way. Models, platforms, capabilities, pricing structures, governance requirements, and best practices can change in a matter of weeks.
Advice that was accurate six months ago may already be outdated. A framework that worked last year may no longer be relevant. Features that once required specialist tools can suddenly become built into products organisations already own.
This doesn’t mean expertise doesn’t exist but that expertise looks different. The most credible people in AI today are often those who are willing to admit what they don’t know.
Confidence Is Not Evidence
One of the challenges facing business leaders is that confidence is easy to market. It’s easy to make bold predictions. It’s easy to claim certainty. It’s easy to present a polished roadmap that promises transformation. Evidence is much harder.
Real credibility comes from showing how conclusions were reached. It comes from practical examples. It comes from testing, learning, adjusting, and sharing both successes and failures.
When evaluating AI advice, organisations should pay less attention to titles and more attention to questions such as:
- What evidence supports these recommendations?
- What real-world examples exist?
- What assumptions are being made?
- What risks have been considered?
- How recent is the information being used?
The people worth listening to are often less interested in impressing you and more interested in helping you make an informed decision.
The Danger of Certainty
Perhaps the biggest red flag in AI is absolute certainty. Anyone claiming to know exactly what AI will look like in three years is guessing. Anyone claiming to know exactly how regulation, vendors, technology, or market adoption will evolve is making assumptions.
The honest answer to many AI questions is often: “It depends.”
That may not be as exciting as a bold prediction, but it is usually more accurate. The organisations seeing the most value from AI are rarely chasing certainty. They are learning, experimenting, measuring outcomes, and adapting as the technology evolves.
What Credibility Looks Like Instead
In consulting, credibility should not come from claiming expertise alone. It should come from demonstrating capability.
That might mean:
- Bringing evidence rather than opinions.
- Showing real use cases rather than theoretical possibilities.
- Explaining both benefits and risks.
- Being transparent about limitations.
- Continuously learning alongside clients.
Personally, I work in this space every day. I spend time reading, testing, experimenting (playing around as I often refer to it), and following developments. I help organisations understand what’s possible and where value may exist. But would I call myself an AI expert? Not in the way the term is often used.
There are people with deeper technical knowledge than me. There are researchers advancing the science and engineers building the models that many of us use every day. What the Kambium team does, is bring curiosity, practical experience, a commitment to continuous learning, and a focus on helping organisations separate hype from value.
In a field changing this quickly, perhaps expertise is less about having all the answers and more about asking better questions.
Maybe We Need Fewer Experts and More Guides
As AI continues to evolve, organisations may be better served by guides rather than gurus. People who are curious rather than certain. People who test rather than speculate. People who can translate complexity into practical decisions. Most importantly, people who are willing to say: “I don’t know, but let’s find out together.”
The people I trust most in this space are rarely the ones claiming certainty. They’re the ones asking thoughtful questions, sharing evidence, testing assumptions, and being honest about what they know and what they don’t. In a field moving this quickly, perhaps that’s the real measure of credibility.
Be wary of people who claim certainty. Trust people who demonstrate capability, evidence, learning, and humility.
The irony is that the most credible people in AI are often the least likely to introduce themselves as experts. Not because they lack knowledge. But because they understand just how much there is still to learn. The organisations that will benefit most from AI are unlikely to be those chasing the loudest voices or the boldest claims. They will be the ones asking better questions, seeking evidence, learning from experience, and remaining adaptable as the technology evolves. In a world increasingly filled with self-proclaimed experts, humility may be one of the most valuable signals of expertise we have left.