AI was supposed to remove repetitive work.

In some businesses, though, something different is happening.

People are spending large parts of their day moving information between systems so the AI can function properly.

Copying notes from one platform into another, checking whether data matches in different apps, rewriting prompts to give an AI tool more context, and manually correcting outputs that almost worked, but not quite.

Sound familiar?

There’s a name for this: Human middleware.

Employees end up acting like the glue holding disconnected systems together.

Once you notice it, you’ll start seeing it everywhere.

Someone downloads information from one system because another can’t access it directly.

A team member pastes customer details into an AI tool to generate a response, then copies the finished result somewhere else.

Data gets checked manually because nobody fully trusts what the systems are producing automatically.

It eats away at time.

The strange thing is that businesses can still feel more productive overall while this is happening.

AI genuinely does help people move faster in many situations. Emails get drafted quicker, reports take less time, and information becomes easier to summarise.

But new layers of admin appear around it.

AI tools often arrive faster than the systems underneath them evolve. A company adds one assistant here, another automation there, a separate AI-powered feature somewhere else… but the tools don’t naturally connect in a smooth way.

So, people bridge the gaps manually.

That creates an odd working environment where employees spend increasing amounts of energy translating between systems instead of doing the work those systems were meant to support.

And that can quickly become exhausting.

You end up with busy days that feel productive on the surface, but a lot of effort is going into coordination rather than progress.

If systems don’t integrate properly, if data quality is inconsistent, or if processes still rely heavily on manual handoffs, AI can sometimes layer extra complexity on top rather than removing it.

That’s why you may need to approach it differently.

Instead of adding isolated tools everywhere, focus on how information moves through the business.

Look at where data lives, how systems connect, and whether people are still spending too much time acting as the translator between technologies.

Ultimately, your team shouldn’t be spending their day helping software talk to other software.

They should be spending their time solving problems, helping customers, making decisions, and doing the work that creates value.

If your employees are constantly switching between apps, correcting AI outputs, or manually stitching workflows together, that’s a sign the technology strategy needs tightening up a little.

Is it time you reviewed your technology to make sure it’s reducing workload, rather than creating more of it behind the scenes? We can help. Get in touch.