Rubens Perdomo
AI was supposed to make work more simple. But in some businesses, people are spending more time stitching systems together than using them. And yet, it can still feel productive while it’s happening…
Have Your Employees Become AI "Middleware"?
Many employees now spend large parts of their day collecting and moving information between systems just so AI can function properly. Finding the relevant data, and then putting it into an AI tool takes more time than most people think about. Many times all of this work goes in just for AI to give you an answer that isn't polished anyways.
Plenty of effort goes into maintaining AI; copying notes from one platform into another, checking whether data matches throughout different apps, rewriting prompts to give an AI tool more context, and manually correcting outputs that almost worked, but not quite.
They get stuck in between softwares, trying to piece together holes of information, while getting less help than all of this work is worth. Slowly, employees become less a part of the work that they are skilled at doing, while spending more time training AI tools to help them.
Does this all sound familiar?
There’s a name for it: Human middleware.
How It's Fatiguing Employees
With a broken system like this, employees end up acting like the glue holding all of these disconnected pieces together. Any time AI is used, it requires human touch from end-to-end. Many employees believe it is a simple short-cut, but it often takes the same amount of effort, just through a different way. And once you notice this, 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. Much of the energy that is saved not answering the question ultimately gets used just inputing and checking the tool's output anyways.
The strange thing is that businesses can still feel more productive overall while this is happening. At first glance it seems like problems are getting solved by AI without any work going into the solution. The work that employees put in catering AI to their needs can end up being much more than they realize.
Over time, they feel tired of using AI, but when it's so deeply implemented into their routine, it happens anyways. And that's where teams can fall into the habit of overusing AI while feeling overstrained.
Where AI Slips Into Being Unhelpful
AI genuinely does help people move faster in many situations. Emails can get drafted quicker, reports take less time to write up, and information becomes easier to summarize. But many people look at all that AI is capable of helping with, without factoring in all the time that they will put into using it.
Many businesses have been eager to implement AI into their processes to cut out the tedious tasks that can become tiresome over time. When so many speak highly about all that AI can do, why wouldn't you be quick to hop on the bandwagon?
But when AI tools arrive faster than the systems underneath them evolve, things get complicated. 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. Their existing software doesn't support the AI tools, and small cracks within systems go overlooked. And no one notices this disconnect until it becomes a major inconvenience.
During issues like these, people start by bridging the gaps manually. It 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 of your work, rather than removing it.
That’s why you may need to approach it differently.
Building A System That Works
Instead of adding isolated tools everywhere, focus on how information moves through the business. Take a step back to look at the bigger picture of your business. This will help you get an idea of what specific tools would work best with your business's specific needs.
Look at where data lives, how systems connect, and whether people are still spending too much time acting as the translator between technologies.
Finding a set of tools that can be used fluidly through your systems and employees will make the transfer of information and AI output much more consistent.
People want to save time from using tools like these, not spend more just by managing it. Building a system that makes AI use productive for your team leaves room for employees to get more work done, while getting repetitive tasks finished quicker.
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.
Get Back in the Driver's Seat
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.
Pay attention to the needs of your business and what features are most important to you in an AI tool. Artificial intelligence can be a very powerful tool when used intentionally, but can quickly slip into a strain if not applied appropriately.
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.
