The Small Business Guide to Responsible AI Use: Part II

October 1, 2026|By Ted Brown

Which AI Use Cases Pay Off, and the Risks to Watch

In Part I of this four-part blog series, we defined responsible AI use, and here we cover where it pays off and what to watch out for.

If you're just starting out with AI, or you haven't started yet, it can feel overwhelming. There's pressure to adopt something, and no shortage of vendors ready to sell you a reason to. The real problem shows up when a business forces an AI solution onto a task that didn't need one.

This part is about avoiding that. It looks at where AI actually pays off for a growing business, and the risks worth watching once your team starts using these tools.

Key takeaways

AI earns its place on tasks where a good, fast result is more useful than a perfect one.
Ask one question of any task: does it have to be right every time, or just better and faster than what you do today?
Strong fits include reviewing large volumes of text, rewriting for a new audience, first drafts, and directing calls.
In each case AI does the heavy lifting while a person stays close enough to catch what matters.
Two habits to keep: no AI output goes out without review, and don't speed up a process that's broken to begin with.
The common risks of team AI use, from data ending up in training to unvetted tools, each have a practical fix that starts with approving tools and data up front.

So before you spend a dollar, start with your own work. Which tasks would get better with this kind of help, and which ones are fine the way they are?

The tasks where AI pays off are the ones where a good result is more useful than a perfect one. Here's a question worth asking about any task: does this need to be correct every single time, or does it need to be better and faster than what we do today? If the answer is the second one, you've found a good candidate.

Use case	Why it works	What it doesn't do
Reviewing large amounts of written material	It can read 100 sales call transcripts and identify patterns like objections that come up repeatedly, how customers describe their problems, and which explanations seem to work	Replace being on the call. Tone of voice and body language aren't in a transcript
Rewriting for a different audience	It can turn a technical explanation into something a CFO can act on, or turn engineering notes into a customer update	Decide who your audience is or what matters to them. You provide that
First drafts	Documentation, meeting summaries, proposal outlines. It gives you something to edit instead of a blank page	Produce a finished document. Every draft needs review
Directing customer calls	Voice-capable AI can identify what a caller needs and either answer it or connect them to the right person without a menu tree	Handle complex or sensitive calls. Those should reach a person quickly

You'll notice a theme running down that last column. In every case, AI does the heavy lifting and a person stays close enough to catch what matters. That's not a limitation to work around. It's the whole reason these use cases are safe to hand off in the first place.

What to be careful about

Running AI without review. If a process produces work that goes straight to a customer, a regulator, or a financial system with no one checking it, an error can reach the outside world before anyone notices. Keep a person in the process wherever the cost of a mistake is high.
Adding AI to a process that's already inefficient. Speeding up one step in a workflow that has five unnecessary steps won't change your results much. If you want a meaningful improvement, look at the whole process first and decide which parts should exist at all.

What are the real risks of employees using AI tools?

The table below contains six common risks of your team members using AI. Each one has a practical fix.

Risk	What happens	How to prevent it
Your data is used for training (and available to competitors)	Some consumer versions of AI tools use what you type to improve their models. If an employee enters financial figures, a customer list, or your pricing, that information may leave your control permanently	Decide which tools are approved and what data can go into them before you roll out
Tools with unclear data practices	The large AI platforms publish detailed terms explaining what they do with your information. Smaller tools and browser add-ons often don't, which leaves your team no way to evaluate them	Keep the approved tool list short and review each tool's data terms before adding it
Use spreading without visibility	Employees adopt tools independently because they're trying to work faster. Most of that use is harmless, but you can't support or protect what you don't know about	Ask your team what they're using and why. You'll usually find good ideas alongside anything that needs redirecting. You can also use web or DNS filtering to block unapproved AI services while allowing approved exceptions. These kinds of technical controls can't eliminate risks, but they can help minimize them
Trusting output too much	Employees who haven't been shown where AI models are unreliable may accept an answer that needs verification	Training sessions (before the rollout) help cover common mistakes and misconceptions
No way to measure results	The tools go out, nothing obvious changes in the numbers, and leadership concludes AI doesn't work. Often the value was there and no one was measuring it	Decide what should improve, and by how much, before you begin
Being targeted by AI, not just using it	Attackers use the same tools. AI-written phishing reads cleanly, and a cloned voice can impersonate an owner or a vendor on a phone call asking for a wire transfer or a payroll change	Verify any request to move money or change banking or payroll details through a second channel you initiate, no matter how legitimate it looks or sounds. Cover this in the same training session

Next up is Part III, which turns to rollout: what to put in place before you hand these tools to your team, and how to choose tools worth keeping. If you want help sorting your own task list into good fits and poor fits, an Ntiva AI Assessment does exactly that

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