Learning becomes the advantage
For a long time, business rewarded certainty. Know the answer, follow the proven process, build the detailed plan, avoid mistakes. Those things still count for something. But when the assumptions underneath a business are shifting this quickly, being able to learn is worth more than being able to defend the way it's always been done.
Smaller businesses have a genuine edge here, and I don't think they always realise it. They may not have the budgets, the teams or the technology estates of larger organisations, but they can move. You can spot a problem on Monday, test an idea on Tuesday and change how you work by Friday. That advantage only exists if people are actually encouraged to experiment.
Experiment, don't bet the business
A good AI strategy starts with small experiments. Find something that feels unnecessarily slow or repetitive, try a different approach, see what happens, keep what works. I think of it as a loop:
Notice. Ask. Build. Refine. Align. Adapt.
Notice where there's friction. Ask whether there's a better way, and whether the thing needs doing at all. Build something small. Refine it based on what you learn. Make sure it aligns with what the business is actually trying to achieve. Then adapt, because the tools will have moved on by the time you're done.
The gap between having an idea and trying it has got a lot smaller. A rough internal tool, a new workflow or a first version of something can be put together far faster than it used to be. Not every experiment will work, and that's fine. The expensive mistake is making one large bet before you've learned enough to know whether it was the right one.
Curiosity still needs guardrails
Experimentation doesn't mean being careless. Teams need access to capable tools, but they also need to know what can and can't be shared, and sensitive client, employee and commercial information shouldn't simply be pasted into a public AI service. You want enough structure to make experimenting safe, and not so much that nobody bothers trying. What that structure looks like in practice is a subject of its own, and I've covered it separately in the first part of How do I AI?
The same applies to what comes out the other end. AI can produce more drafts, options and attempts than you know what to do with, but volume isn't value. Someone still needs to decide whether the work is good, useful and relevant to the business.
The mindset
The AI mindset starts with curiosity. Explore what's out there, try things for yourself and stay close to what's happening, even when the value for your business isn't obvious yet. Knowing what's available and understanding the direction of travel will shape better decisions long before you're ready to act.
So when the time comes to bring AI into your business, you won't be starting from scratch. You'll have a clear sense of the options, a feel for where things are heading, and the confidence to choose what fits.
This article is part of a short series on approaching AI in a business. See also: Don't use AI to rebuild the business you already have.
By Josh Gosselin, Founder, DEXM