We all remember when ChatGPT 3.5 first came out at the backend of 2020. Since then, businesses have been inundated with AI chatbots and assistants. Claude, Gemini, Copilot, Perplexity, and even ChatGPT 5.2, just to name a few.
Some businesses rushed to adopt AI. Some hesitated. Some are still driving blind without an AI policy. No matter which category your business falls into. We’re seeing the same recurring themes, problems and headaches:
And yet many leaders are sat asking the same question:
Why hasn’t anything really changed?
That’s because there are two sides to the coin. There’s AI, and there’s automation. Now, the promise of AI was improved productivity, faster operations, and better decision-making. But for many organisations, the day-to-day reality still looks very similar to how it did before.
When we see this happen, it’s almost always the result of something we call shallow automation.
Shallow automation occurs when businesses introduce AI tools without changing the processes within which those tools sit.
Instead of redesigning workflows, companies simply layer AI on top of existing work.
The result is usually:
In other words, the technology changes, but the way work actually gets done doesn’t. And then that’s where the real cost starts to rear its head.
Businesses are paying for an additional tool, for the same person to do the work in pretty much the same way.
Most organisations have great intentions, none of them set out to create shallow automation.
And yet it happens because the adoption process often starts with the technology instead of the outcome. A common pattern we see looks like this:
But without first understanding how work flows through the business, those tools rarely change the underlying process.
A lot of customers are saying ‘we want to play around with AI’, so they give employees a few Copilot licences and away they go. Yes, meeting notes are transcribed quicker, emails are drafted in seconds, and corporate data can be found quicker.
The intention is good, and the instant gratification of these quicker tasks is great, but it’s a trap, as we’ll discuss below.
Because again, the result is usually the same: AI becomes another tool in the toolbox rather than something that fundamentally improves how work happens. Scratching the surface like this doesn’t go deep enough to deliver a clear, real return on investment.
One of the most common examples of shallow automation is what we call the Copilot trap.
Businesses deploy AI assistants (such as Copilot) expecting them to dramatically improve productivity.
In reality, employees typically end up using them for things like:
That’s useful, of course.
But as we said, it’s a trap, it’s not transformational. Copilot isn’t even really making admin faster. It’s just an additional tool that serves a lot of the same purposes as Google.
When AI is used mainly as a better search engine or writing assistant, the business impact tends to be minimal.
The underlying workflow still relies on people doing the same work they always have.
Simplifying what we tend to see across most organisations, you start to see a very similar pattern.
At the start, there’s excitement. AI feels new. Powerful. Full of potential. People start using it for everything.
Then comes the dip. Outputs don’t quite hit the mark, they feel repetitive and “samey” (this is what the internet calls “AI Slop”). Results feel inconsistent. People begin to question whether it’s actually useful.
And this is where many businesses stop.
But the organisations that get real value push through this stage. They start refining how they use AI. They improve how they prompt it. They give it more context. They begin integrating it into workflows rather than using it in isolation.
And that’s where the value starts to appear.
AI isn’t a plug-and-play solution. It’s a capability that improves over time when used properly.
Touching on one of the most common frustrations with AI: the output often feels generic. You can usually tell it’s been written by AI.
It lacks context. It misses nuance. And sometimes, it’s just not quite right.
So why does this happen? Why do we all feel this at some point? This happens because AI doesn’t understand your business by default.
It doesn’t know your customers, your services, your processes, or how you operate. Therefore, it defaults to providing what it believes is the right answer based on what it’s trained on.
That’s why it feels generic, because it’s using the generic data for your generic prompt.
Without that context, the output will always feel surface level.
If you provide clear direction, context, and expectations, the output improves significantly.
Over time, as AI is used more consistently and with better inputs, the results become far more aligned with how your business actually operates.
But that only happens if you treat it as something that needs to be developed, not something that works perfectly from day one.
The biggest cost of shallow automation isn’t the technology itself.
It’s how your people spend their time.
In many organisations we work with, highly skilled employees spend a surprising amount of time on work that adds very little value.
For example:
None of these tasks requires the expertise for which those employees were hired. Yet because the processes haven’t been designed properly, the work still exists.
Look at it this way, if you’ve hired graphic designers, you pay them to design graphics. Not to spend a considerable chunk of their time dealing with paperwork and admin. Yet it happens. All the time. Because shallow automation doesn’t remove these tasks, it simply makes parts of them slightly easier.
Another reason shallow automation happens is because businesses often confuse AI with automation.
They’re related, but they solve different problems.
Automation works best when:
Examples include:
Automation removes the need for a person to perform the task at all.
Because where we are now is similar to where we were when computers were first implemented in the workplace. Remember when everything went from paper-based and stamping something for approval, to being digitised on a computer? Well, we’re at that same point now, where instead of how you do the work, it’s who does the work… and that “who” doesn’t have to be a person, it can be a process.
AI is most useful when a task requires interpretation or analysis.
Examples include:
AI adds intelligence to a workflow. But if the workflow itself is poorly designed, AI won’t fix the problem; it will just amplify it.
You can learn more about AAG’s AI and automation services.
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