Most SMEs are already using AI in some form. But are you using the right kind, for the right tasks? And are those tools actually moving the business forward?
We’re hearing a lot about generative AI and agentic AI at the moment. But they are both different pieces of the AI puzzle – and are not interchangeable. They each perform different tasks, at different stages, for different reasons. Understanding the distinction is what separates businesses that simply experiment with AI from businesses that get value from it.
Generative artificial intelligence (generative AI) is a type of AI that creates new content in response to a prompt. You send it a prompt, and it will come back with an answer. Nothing happens without your input. It doesn’t take action on its own.
The real power is the breadth of knowledge it draws on. Think of it as collaborating with a huge team of people you never see and getting different perspectives on a particular piece.
But it's all about the quality of the prompt. The more context you give it, the better the output. And with that better knowledge, you can make a better agentic loop (we’ll get into this below). And that's really the trick.
Generative AI levels the playing field. An 11-person business has access to the same quality of information and output as a 200-person one. That is a significant shift.
Agentic AI is artificial intelligence that acts. It’s the doing part. Executing tasks, making decisions and completing workflows autonomously toward a defined goal.
The agentic loop is the repeating execution cycle at the core of every agentic AI system. It repeats until the task is complete or a predefined stopping condition is met – for example, when it should stop and ask a human.
Generative AI is the assistant who writes the email. Agentic AI is the system that decides whether to send it, pulls the customer history to personalise it, sends it at the right time, monitors whether it was opened, and adjusts the next step accordingly.
Agentic AI can:
Create a purchase order
Route an incoming request
Trigger a workflow
Notify a warehouse or a customer
This is where it becomes powerful.
The most common mistake is treating agentic AI as a standalone tool – setting up automations without first establishing the quality of the decision that triggers them.
Generative AI is what feeds the agentic loop.
And the best payback is going to be for repetitive tasks.
For a stock reorder, the agentic part is straightforward. For example, it creates a purchase order when stock falls below the minimum. In a system like SAP Business One, the agent references live operational data (stock levels, sales history, product status) to make more informed decisions.
But generative AI is where you can make better decisions by asking a few questions:
Is the product slow-moving?
Has it been marked as end of life?
Is the overall goal to reduce inventory holdings rather than replenish them?
Take a customer email as an example. Someone writes in to order five units of a product. Generative AI interprets the intent first. Is this a confirmed order, or a quote request? Is there a nuance? A timing condition, a "don't proceed until I confirm"?
Get that right and the agentic loop fires the correct action – creating the sales order in SAP Business One and notifying the relevant team. Get it wrong and the system ships something to a customer's door when they were still deciding.
Generative AI also helps you go the extra mile. A customer asks for a quote, generative AI can include detailed product information they never asked for but will find useful. More relevant, faster, and at almost no additional effort. That kind of responsiveness has an enormous effect on how quickly support cases get resolved.
This is why you shouldn’t skip straight to agentic AI. With generative AI in the mix, the agent has the context to make a smarter decision. Not just to act, but to act appropriately.
The most practical approach for an SME is straightforward:
Use generative AI to prove that a use case is worth pursuing
Use agentic AI to remove the friction from it once that is established
Agentic AI delivers results where there is a proven, repeatable process to automate. Here’s a useful test: if you cannot describe the process clearly in plain language, an agent is not ready to run it.
AI for its own sake is not a strategy. The value case has to come first. Once you have identified something that genuinely improves how the business operates, that is the right place to start building an agentic loop around it.
Start small. Prove it works. Build from there.
Knowing which tool to use, when, and how to connect it to your existing systems is where most small businesses get stuck. Key Business Solutions works with SMEs to identify where AI can add genuine value and how to layer it on top of existing systems.
Ready to explore? Start a conversation with our team today.