AI presents a genuine opportunity for small businesses. To work more efficiently, reduce costs, and get better insight from the data they already have. Most SMEs know this and many are already experimenting.
The challenge is that experimenting isn’t the same as having a strategy. And without one, even the best AI tools lead nowhere.
Most SMEs approach AI the same way. They try a tool like ChatGPT, Claude or Gemini, see what happens, move on. The result is scattered experiments with no thread connecting them, no clear outcome, and no way to measure whether any of it worked.
The issue isn't which tool they picked. It's that there was no plan behind picking it.
Different people in the same business end up using different platforms for different tasks. What started as a $20 subscription becomes $60 a month across three different tools doing three different jobs – with no coordination and no one owning the outcome.
Two things go wrong without a strategy in place.
The first is tool sprawl. No coordination, no ownership, and a growing list of subscriptions solving problems in isolation. The business ends up with AI everywhere and no strategy.
The second is key-person risk. Let’s say an employee builds an AI agent to check overdue customer payments. It becomes part of their daily work, but then they leave. What remains is something nobody else understands, running in the background, with risk quietly built in. It's not unlike having a programmer write a custom system and walk out the door.
The agent is easier to build, but it's still someone's intellectual property, designed to think a certain way. If that person is gone, the business is left holding something it can neither maintain nor explain.
SMEs aren’t looking for AI. They're looking for savings.
Many businesses are reluctant to approve discretionary spending without a clear return. But they will have the conversation if AI can demonstrably reduce cost or improve margin. The first question isn't "what AI tool should we use?" It's "what problem is costing us the most, and can AI help with it?"
That shift – from tool-first to problem-first – is the difference between an experiment and a strategy.
Let’s take wholesale and distribution businesses as an example. For them, some of the biggest opportunities lie in better inventory management. How old is my stock? How fast does it turn? How much working capital is tied up in it? Are there complementary products that could be bundled to help sell slow-moving stock?
These are the problems worth solving first. And they're also the problems AI can genuinely help with – but only if the data behind them is clean, structured and accessible.
Using an ERP like SAP Business One can provide a useful foundation, with business data already captured in a structured environment. The next step is assessing whether that data is suitable and accessible for the intended AI use.
The conversation shifts from "where do we even start?" to "which problem do we tackle first?"
An AI plan doesn't need to be complex, but it does need to exist. And like a business plan or budget, it should:
Involve the whole organisation
Map the problems
Agree on which ones to tackle and in what order
Assign ownership
Set a timeline and a measurement point for each
And, most importantly, prioritise by payback, not ease. The biggest problem may take more effort to solve, but it's usually worth more than the low-hanging fruit.
A business with a plan knows what its AI is doing, who owns it, what it costs, and what success looks like. If a key person leaves, the work doesn't leave with them. If token costs start showing up in the profit and loss statement, there's a framework to evaluate whether the spend is justified. And if something isn't working, there's a measurement point to catch it – it’s not a mystery running quietly in the background.
Most businesses have the tools. What they're missing is someone who can look at the whole picture and say: start here, for this reason, and measure it this way.
Key Business Solutions works with SMEs to understand the problems worth solving, identify where AI can realistically add value and make sure the data foundations are in place before anything is built. That includes helping teams understand when to use AI and when not to, because, without direction, it can waste as much time as it saves.
Ready to have that conversation? Contact our friendly team today.