The Small Business Guide to Responsible AI Use: Part III

How to Roll Out AI to Your Team and Choose Tools That Stick

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

By now you know which tasks are worth handing to AI. The next question is how to get it in front of your team without creating problems you'll be cleaning up later.

This part covers the groundwork you'll want in place before rollout, and how to separate a tool that earns its keep from one that's just riding the trend.

Key takeaways

• Get four things in order before rollout: team training, a written policy, business-tier tool versions, and a clear definition of success.
• Start tool selection with a problem you can name, not a demo you saw.
• The thing limiting results in most companies is a process, not a lack of technology.
• Ask five questions before you buy, including what implementation really involves and whether people will still use the tool in three months.

What should you put in place before rolling out AI to staff?

There are four things you'll want in place before you get started, in this order: training for your team, a written policy, business versions of the tools you choose, and a clear definition of success.

Pre-rollout checklist

• Train the team before the tools arrive. Cover what these models do well, where they're unreliable, and a few practical skills: how to give a model enough background to be useful, how to share a document with it, and how to write a clear request.
• Engage your team with regular office hours to offer support. Ntiva's own internal AI immersion program showed that concentrated learning periods outperformed short weekly sessions because participants avoid repeatedly ramping back up. Pairing immersion with recurring office hours, where employees bring real work and develop reusable solutions, was one of our biggest drivers of adoption among our team. 
• Write the one-page policy. Approved tools, permitted data, prohibited data, and decisions that stay with people.
• Confirm you're using the enterprise/business version. Consumer and business versions of the same product often have different terms for how your data is handled. Checking takes a few minutes. If your business runs on Microsoft 365, you may already own most of the guardrails: Copilot with enterprise data protection, data loss prevention and sensitivity labels in Purview, and Conditional Access to control who can sign in and from where. None of it protects anything until it's configured, so make that part of the rollout.
• Define success in advance. For example: reduce the time to draft a proposal from four hours to ninety minutes by the end of the quarter.
• Assign an owner. One person to answer questions keeps small uncertainties from turning into policy problems.

How do you spot a genuinely useful AI tool versus one riding the trend?

When you see an example of a business using AI for a specific reason online or in a demo, you might feel compelled to move toward that solution. But you'll want to hesitate that feeling. Instead, begin with the problem in your business rather than the features in the demonstration. A tool you'll derive value from should solve a problem you can describe and fit your team's existing workflow well enough that people are still using it three months after implementation starts.

In most companies, the thing limiting results is a process rather than a lack of technology. Identifying that process makes every purchasing decision easier, because you have a specific standard to evaluate against.

What questions should I ask before investing in an AI tool?

There are five questions you and your team should consider before moving forward with any AI platform:

• Which specific problem does this solve for us? If you can't name it, it's worth pausing before you evaluate tools.
• How does it work? Ask the vendor to take you "under the hood" and explain the mechanics. Reputable vendors expect this question and answer it directly.
• What does implementation actually involve? Most of the effort in an AI project isn't installation. It's configuring the system to handle one specific job reliably. A vendor whose plan accounts for that has done this before.
• How much information does it need to work well? These models perform better when you give them relevant background. There's a limit, though. Past a certain point, adding more material makes the results worse rather than better, because the important details get lost among the rest. Selecting the right information matters more than providing all of it.
• Will people still use it in three months? Adoption determines whether the subscription pays for itself, and it usually depends on how well the tool fits the way your team already works.

Next up in this series is Part IV, which looks at what to do once your team is trained and your tools are chosen. We'll discuss what the months after rollout actually feel like, and the signs it's time to bring in outside help. If you'd like a second set of eyes on your policy, tool choices, and rollout plan before you start, Ntiva can help you with all three.

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