The Unglamorous Truth About Connecting Your Business Tools

At some point in almost every growing business, someone opens a spreadsheet to copy data from one system into another. Then someone else builds a zap to automate that. Then the zap breaks, nobody notices for three weeks, and by the time it's discovered the two systems are quietly out of sync in ways that take days to untangle.

This is not a technology failure. It's an integration failure. And it's far more common than any software vendor would like to admit.

Your stack is probably fine. Your data flow isn't.

Most SMEs don't need to replace their tools. They need their tools to talk to each other properly. A CRM, an accounting package, a project tracker, a support inbox — these are all sensible choices individually. The problem is the space between them.

That space is where data goes to die. Leads that were closed in the CRM but never updated in the finance system. Jobs marked complete in the project tool but never triggering the invoice. Customer notes living in someone's inbox rather than anywhere the rest of the team can see.

None of this requires a sophisticated AI solution. It requires a clear picture of where data is created, where it needs to go, and what should trigger the movement.

What "integration" actually means in practice

There are broadly three ways business tools share data, and understanding the difference matters when you're choosing how to connect them.

Webhooks are the simplest: one system fires a notification the moment something happens — a form is submitted, a payment is made, a status changes. The receiving system catches it and acts. Fast, lightweight, and reliable if set up correctly. The catch is that if the receiving system is down when the webhook fires, you may lose the event entirely unless you've accounted for that.

APIs let systems query each other on demand. Rather than waiting to be told, one system asks another: "What are your open invoices?" or "Has this customer's status changed?" This is more flexible but requires more thought about when and how often you're querying, and what you do when an API call fails or times out.

Scheduled syncs — often done through tools like Zapier, Make, or a custom script — check for changes at regular intervals. They're the easiest to set up and the most likely to cause subtle problems. If your sync runs every 15 minutes and something time-sensitive happens, you've already got a lag. If the sync runs into an error and you don't have alerting in place, you won't know until something downstream goes wrong.

Where AI fits into this picture

Increasingly, businesses are adding AI into their data flows — summarising documents before passing them to a system, extracting structured data from emails, enriching records with information pulled from external sources. Done well, this is genuinely useful. Done carelessly, it adds a new layer of unpredictability to an already fragile pipeline.

The practical advice here is simple: treat AI steps the same way you'd treat any other transformation in a data pipeline. Ask what happens when it produces something unexpected. Ask whether a human should be reviewing the output before it writes to a system of record. Ask whether you have logging in place so you can audit what the AI did when something looks wrong three weeks later.

Many SMEs are now using AI to classify inbound enquiries, draft responses, or flag anomalies in data. These are legitimate, low-risk use cases. Where things go wrong is when the AI output is trusted implicitly and fed directly into a business process with no human checkpoint and no way to trace what happened.

The questions worth asking before you build anything

Before connecting two systems — with or without AI in the middle — it's worth being honest about a few things:

Start with a map, not a tool

The most useful thing you can do before touching any technology is draw a simple diagram of how data moves through your business today. Not how it should move — how it actually moves. Include the manual steps, the spreadsheets, the copy-paste moments. Those are the integration gaps, and they're the ones worth prioritising.

From there, you can make sensible decisions about what to automate, what to connect, and where — if anywhere — AI adds something that a simpler solution wouldn't. Most of the time, the answer is a webhook and a clear data contract, not a large language model.

The businesses that get the most out of their tools aren't necessarily using the most sophisticated ones. They're the ones who've taken the time to understand what they've already got and made it work properly together.

Evolvin is a London studio that designs and builds software, AI, and automation for growing businesses. If you'd like a straight conversation about what your current setup could do better, get in touch.