SME businesses are adopting AI fast. Public tools like OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, X’s Grok and Meta’s Muse are being used to draft client emails, summarise documents, create proposals and “sense-check” decisions.
That speed is real. So is the risk.
Two risks that matter most in an owner-led firm
If your business is regulated, or simply depends on its reputation, two risks matter more than any others:
Privacy leakage
Hallucinations (confident inaccuracies)
These are not abstract. They show up as breached confidentiality, wrong advice, incorrect compliance statements and client-facing errors that damage your credibility.
In an owner-led firm, where the owner’s judgement drives direction, one bad output can become the wrong decision that costs you.
Privacy leakage: the silent liability
Public large language models (LLMs) are external systems. The moment you paste information into one, you may have exported sensitive business data outside your governance controls.
In professional services, that “helpful paste” often includes:
client names, personal data and case details
contractual terms, pricing, HR and staff issues
draft reports or regulated communications.
Most teams do not do this maliciously. They do it because it is quick.
But speed does not change the underlying problem: you cannot treat a public model like an internal system. You cannot reliably prove what was shared. You cannot easily audit it across the organisation. You cannot confidently ringfence liability if it becomes part of a complaint, claim or regulatory event.
The issue is governance: uncontrolled data leaving your environment. Free versions of public tools are public. The information you give them is out there, and you have lost control of where it goes next.
Hallucinations: polished output that can still be wrong
Public LLMs are excellent at producing language that sounds right. They are not designed to verify truth.
That is why hallucinations are so dangerous. They look credible enough to survive a skim read, until they are tested in the real world.
A real-world example: in Mata v. Avianca (US), lawyers submitted a court filing that contained 6 case citations that did not exist. An AI tool made them up.
The lesson is not that lawyers were careless. It is that AI output can look authoritative and still be false, and public AI does not know the difference. When your customers rely on you being right, a hallucination becomes brand damage.
Where public AI is fine, and where it is not
You can use public AI for low-risk tasks. It can be better than nothing. A simple test helps your team decide: would you be comfortable seeing this exact input, and this exact output, in a client complaint file?
If the answer is no, because the task is sensitive or because you need to get it right and count on it, use a private system.
What a safer alternative looks like
It would be good to have an AI that is secure by design, does not rely on random internet context for business-critical work, and reduces hallucinations by grounding answers in its own knowledge base of 8000 hours of business expertise plus your own documents and knowledge.
Wait a minute… that rings a bell. That is what Clarity from AMPLIFYY is built to be.