How do we need to improve our data infrastructure to deploy AI-driven automation for our manual processes?

If we want to get serious about AI-driven automation, the main thing we have to look at first is whether our data infrastructure can actually support it end to end. Right now, we’re not quite there, but it’s fixable if we focus on a few areas.

Here’s what I’d say we need to improve:

• Centralizing the data.
  A lot of the information we’d want AI to act on—customer records, transactions, order details—lives in different systems that don’t talk to each other cleanly. If we don’t consolidate that into a shared platform, automation will end up either missing context or triggering errors. This is really the foundation.

• Making sure the data is clean enough to trust.
  Automation assumes that whatever it’s looking at is correct. If we have duplicate records, missing fields, or inconsistent formats, we’ll end up spending just as much time cleaning up after the AI as we would doing things manually. We need some validation processes to catch and correct this automatically.

• Getting data to move in real time.
  Many of our workflows—like approvals or alerts—depend on up-to-the-minute information. Right now, a lot of our data updates overnight or on a delay. We’ll need streaming or event-based processing so AI can act the moment something changes.

• Agreeing on who owns what.
  This is an area that trips up a lot of teams. If we don’t have clear definitions—what’s an “active customer,” who updates product records, what each field means—then automation just amplifies confusion. We’ll need some basic data governance so everyone’s on the same page.

• Locking down security and compliance.
  Automating processes means AI models will be accessing sensitive data. We have to be sure we’ve got role-based permissions, audit trails, and encryption in place so nothing slips through the cracks.

• Setting up infrastructure to run and monitor models.
  Once we train the AI, we need somewhere to host it, monitor for drift, and retrain when the data shifts. Otherwise, predictions will degrade over time, and nobody will notice until things go sideways.

If we tackle these improvements up front—even in a phased way—we’ll avoid the trap of “automating chaos,” where the AI just makes the same mistakes faster.

We don’t need to boil the ocean. My suggestion is to pick a couple of high-impact processes we want to automate first and then focus on getting the data and infrastructure for those in good shape. From there, we can scale out to other areas more confidently.