Before you add AI: Set it up for success

Once you've identified a valuable use case for AI in your business, it’s tempting to move straight to implementation. But first, you need to understand what it will require from the systems, data, and workflows you already have in place.

Those requirements can vary significantly depending on what you're trying to do. An AI tool that summarizes internal documents has very different needs from one that accesses customer data, works across several systems, or takes actions on a user's behalf.

Can your data support it?

An AI capability is only as good as the data behind it. If the information it needs is difficult to access, inconsistently structured, incomplete, or out of date, the AI may struggle to find the right information or produce reliable results.

That doesn't mean all of your company's data needs to be cleaned up before you start. The question is what data this particular capability needs and what, if anything, needs to change to make it work.

Can your existing systems support it?

AI rarely operates in isolation. Depending on what you want it to do, it may need to pull information from existing applications, share data between systems, or take action within them.

How easily those systems can connect with AI can affect what's possible, how complex the work will be, and ultimately, what it will cost. Older or highly customized systems don't necessarily rule out AI, but they may require additional work or a different approach.

What happens to the workflow?

Adding AI is rarely just popping it into an existing process and leaving everything else unchanged. If AI can automate a step, eliminate several steps, or change what information is available at a particular point, the workflow around it should change, too.

Otherwise, you can end up using new technology inside a process designed around the limitations of the old one. Looking at the workflow as a whole can reveal opportunities to simplify the process, change where people need to be involved, and get more value from the AI capability you're adding.

What happens when AI gets it wrong?

Not every AI mistake carries the same risk. Misclassifying an internal request is very different from making an error that affects a customer, financial transaction, or patient.

The greater the consequences of an error, the more crucial it is to think about how the AI's work will be checked and where people need to remain involved. Every implementation needs a way to check that it works reliably for its intended use. Depending on the consequences of an error, that may include reviewing outputs, testing representative scenarios, requiring approval before actions are taken, and providing a fallback when something goes wrong.

The goal isn't to eliminate every possibility of error. It's to make sure the safeguards match the risk.

What can it access?

Giving AI access to your systems and data can inadvertently create new ways for people to reach information or take actions. Existing security, privacy, and permission controls don't automatically carry over just because AI is working across systems that already have them in place.

You need to ensure someone using an AI capability won't be able to see information or take an action they wouldn't otherwise be authorized to. Those permissions need to be enforced by the applications and connected systems, rather than relying on instructions to the AI alone. And where sensitive or regulated data is involved, you need to follow any privacy or regulatory restrictions on how that data can be accessed and used.

Set it up for success

Adding AI doesn't mean you need to clean up all of your data, modernize every system, or overhaul your entire operation. But you do need to understand what the capability you're adding will depend on and address the things that could keep it from working as intended or introduce new issues.

Define how you'll measure success, including the time saved after review and corrections, the cost of operating the capability, and whether its results remain reliable in everyday use.

Doing that work upfront can help you avoid unnecessary complexity and expense along the way, and make it far more likely your AI investment delivers what you expect.