My business origin story is unlike most, because on the surface, it has nothing to do with ‘business’. My lifelong quest has been (and still is) studying what makes people tick – what drives them, what they respond to, why they do what they do, and what genuinely helps them grow. This curiosity is the thread running through everything I’ve built, and it’s also why I invested 30+ years in learning and development. That desire to learn runs much deeper than any particular area of my work and shapes how I see the world, how I interact with people, how I approach almost everything I do – and it’s also what led me into focusing on AI literacy and enablement.

With AI, the technology is the straightforward part. The most important element is the ‘people’ – how they think about it, how to better empower them to use it effectively, and ultimately whether they truly adopt it, or simply go about blindly, just hoping for the best.

What makes us different comes down to two things. We bring 30+ years in learning and development and around a decade of experience with AI, so the enablement rests on genuine expertise. We also work across a wide range of organizations and sectors, which gives us a wide-angle view of what’s actually happening on the ground – so much more than simply what affects our particular business.

We are in a unique situation to see what’s working, where the real momentum is, and what people are ready for (and need) next. Our clients gain perspective from well beyond their own walls, and that shifts how they approach the whole thing.

There is plenty of AI ‘tools’ training available in the world. What is missing – and the gap we fill – is the more fundamentally essential training of ‘why’ use a certain tool, ‘when’ might be best to do so, ‘how’ to decide if what it tells you is accurate – and above all, how to critically think about and analyze the entire process. AI is an excellent tool, but unless you understand the deeper questions around how it should and should not be used, the risks can become very real, very quickly; and you can very easily become the cautionary tale everyone else learns from.

At the heart of everything we do is a ‘human-first’ approach.

The majority of my new business has always come through relationships, referrals, people who have worked with me before, and people who have followed my work over time. After more than 30 years in business, that network is extensive, and a surprising amount of work still begins with someone saying, “You really need to talk to Bella.”

Where I would like more of it to come from is discovery – people and organizations finding our AI literacy and enablement training because they are actively attempting to work out what AI means for them and their people.

I’m not talking about showing up more in AI search – that is evolving. I mean I wish there were more organizations actually asking, “how can we better equip our people to successfully navigate the new (and rapidly changing) AI landscape?”

There is enormous interest in AI at the moment, but there is also an enormous amount of noise. Organizations are buying tools and experimenting with platforms, while many of the people expected to use them have had very little help understanding how to use AI well, how to judge what it gives them, where the risks are, what needs verifying, or even what questions they should be asking.

It still baffles me that so many organizations are investing in tools and simply expecting their people to have functional AI literacy – not just how to use the tool, but ‘why’. So, I wish more business came from leaders asking the questions about how to upskill their people, rather than because they have found themselves in a hole when their people are not adequately trained.

I would much rather begin a conversation with someone who is already thinking, “We know our people need to become AI capable – how do we actually make that happen?” than spend time trying to convince somebody that AI matters in the first place.

This one is interesting, because I am probably more conscious now of what I should NOT hand over to it than I was a year ago.

One thing I have genuinely handed over to AI is some of the tedious first-pass work – organizing information, creating comparison tables, pulling together starting points, and helping me get to a useful starting position faster.

The second part is more challenging to answer, because I cannot honestly think of something I handed over completely and then took back. I have always used AI as a tool rather than as an authority, so there has never really been a point where I said, “You do this now, and I am out of the loop” – but I have needed to help several coaching clients to bridge that gap when they went ahead without considering the consequences and upskilling first, and things didn’t go as they had planned.

What has changed is the level of scrutiny I apply. One might think that in the early days, more scrutiny was required – but it’s actually the reverse.

Why? The more capable AI becomes, the more important critical thinking becomes. These systems can sound extraordinarily convincing while being wrong. They can (and often do!) invent facts, misinterpret context, rely on outdated information, miss nuance, or cite something that does not actually support the claim being made.

Consider the recent case in Mississippi where lawyers on both sides of a federal dispute submitted AI-generated legal citations that did not exist (AI made stuff up! and the lawyers didn’t bother to check!). All four attorneys were sanctioned, two were barred from practicing in that court for two years, and the trial itself was canceled. The problem was not that they used AI. The problem was that they trusted the output without properly verifying it. That is the difference between simply using AI and being genuinely AI literate.

That is also why I think true AI literacy goes far beyond learning how to prompt. It is knowing how much to trust the output, when to question it, when and how to verify it independently, when to push back, and when to reject it completely.

For me, it is not, “I gave this to AI and then took it back.” Instead, it is, “I can give AI more tasks on which to work, but I refuse to give away my ultimate responsibility and personal accountability. I need to be continually mindful that AI is a ‘tool’ and as with any tool, it is the user of that tool who needs to know how and when to use it effectively.”

Referrals and relationships are still the dominant route, but one thing that has changed is that the research people often do before reaching out has changed dramatically – and that can be good, and not so good.

AI gives anyone the ability to explore a subject in extraordinary depth within minutes. That is enormously valuable, but it also creates a modern version of “a little knowledge is a dangerous thing.” Someone can have a long conversation with an AI, learn the terminology, explore different approaches, gather examples and walk away feeling extremely well informed. The problem is that they may have no way of knowing which parts were accurate, which were incomplete, which lacked important context, and which were simply wrong – because without an adequate level of AI literacy, they don’t even know how to find out what questions they should ask. “You don’t know what you don’t know.”

Where coming in with some background information can be beneficial is in a shared language. For instance, if someone has seen every episode of Star Trek, it’s easy to say, “it’s like that whatsit in the episode where so and so did this or that” – instead of having to explain from scratch.

Where this can be a challenge is that once people latch onto a piece of information without a broader context or understanding, it can be difficult (and stressful) for both parties to navigate to the real issue in order to focus on the solutions.

In terms of people arriving specifically through AI assistants such as ChatGPT, that is beginning to happen, and this highlights an important point. What is already clear is that businesses need to think beyond traditional search. People are increasingly asking AI systems for recommendations, explanations, and comparisons before they ever visit a website – IF they ever visit a website.

That means the first impression of your business may no longer happen on your website at all. It may happen inside an AI-generated answer – an answer that may provide no context and over which you have almost zero control.

I would tell them to stop treating AI as a side project, stop confusing AI adoption with AI capability, and start building genuine capability around it.

AI can be a wonderful tool in the right situation, with the right oversight.

The first step is doing an analysis of where AI can truly support. I would also tell them to deliberately broaden where they look for ideas.

AI gives us access to thinking, research, examples and lessons from industries and disciplines far beyond our own. That is one of its greatest strengths. If all you are doing is asking what your competitors are doing, you are missing much of the opportunity.

I would pay very close attention to how your organization is represented in AI-assisted search, and how it will combat a zero-click world – meaning that out of all the businesses in the world who do what you do, AI is only going to mention a small number. OK, so you have a strategy to do your best to show up in AI search, but what is your strategy in case you are not in that list at all?

What would I ignore? The pressure to chase everything. ‘Shiny Squirrel Syndrome’ is already claiming casualties. Only a few weeks ago, I did a clean-up with one of my coaching clients, and we found around $2K/mo. that was being spent on subscriptions to what was “the next best AI thing” that they no longer use.

Every week there is another tool, another feature, another prediction that supposedly changes the entire landscape. It is simply not humanly possible to become experts in every new platform. What people need is enough genuine AI literacy to evaluate what is useful, what is relevant to their work, and what can safely be ignored.

The focus for the next 12 months should be less about trying to keep up with AI and more about what will still matter when this week’s latest tool has already been replaced – making sure your people are genuinely AI literate.

Whatever you decide and whatever you delegate to AI, my strongest suggestion is this: “Don’t delegate responsibility to AI, or to people who are not trained to effectively use AI. Doing so is almost certainly guaranteed to come back and bite.”