At Hello Starling, that’s the approach we’ve taken. We’re not anti-AI; there are plenty of things it is very good at, and we’d be missing a trick if we didn’t utilise it.
We use AI to work with large datasets, tidy spreadsheets, sort large amounts of qualitative information and help structure complex results and insights. We use meeting notes on conference calls, and we use it to search for information and summarise things when we need to get to the useful bit quickly.
What we don’t use it for is the stuff that makes us good at what we do.
We don’t use it to create our media plans. We don’t use it to write our articles, website copy or social posts. We don’t use it to replace the strategic thinking, experience and creativity that our clients come to us for.
Ultimately, that’s because we don’t think those things should be outsourced to a machine.
We know that a lot of times, businesses will argue that the efficiency AI can offer makes the decision to replace people with it stand firm, but I think it’s important to recognise that efficiency isn’t the same as capability. There is nothing wrong with looking for efficiencies. Every good business should.
The problem comes when we confuse removing inefficient work with removing the people who have the capability to do the important work.
If AI can save someone three hours tidying a spreadsheet, fantastic. Give them those three hours back. If it can find a piece of information in seconds that would previously have taken someone an hour to locate, brilliant.
But what happens to the time you’ve created? That’s where businesses have a choice. You can look at those efficiencies and say we don’t need as many people anymore, or we could see it as we’ve given our people more time to think, create, learn, develop and grow the business.
The risk is in assuming that every piece of human work handed to AI is simply an efficiency gain, when in reality, doing the work is how we learn.
Someone at entry level doesn’t develop judgement simply by reading the answer to a difficult problem. They develop it by working through the problem themselves. They research, make mistakes, and challenge assumptions, and that’s how experience is built.
If we gradually remove those opportunities because a machine can produce an answer instantly, we shouldn’t be surprised if people eventually have less experience
to draw on.
An even bigger question for businesses is what happens if lots of companies make similar decisions at the same time, reducing roles because AI can perform parts of them. We could end up with a significant loss of experience and expertise across entire industries.
And what happens if, a few years from now, we discover that we need more human expertise than we thought? You can’t manufacture ten years of experience overnight. You can’t instantly rebuild a generation of people who have learned how to solve problems, make decisions and exercise judgement in the real world.
That is the part of the AI debate that we all need to take much more seriously. We spend a lot of time talking about whether AI is capable of replacing humans, but perhaps we should spend a little more time asking whether humans will still be capable of doing the work once we’ve stopped practising the things AI used to do.
None of this means businesses should ignore AI. We should use it to take away repetitive work, make sense of large amounts of information and help people work more efficiently. At Hello Starling, we’re comfortable with AI doing the things that help us get to the interesting work more quickly. We’re much less comfortable with it doing the interesting work for us.
Our strategic thinking, experience and creativity aren’t inefficiencies that need to be removed. They’re the reason our clients appoint us.
So perhaps the mindset businesses need to take into the next phase of AI is a simple one: Don’t start by asking how many people can be replaced; instead, start by asking what could our people do if we removed the work that gets in their way?
The future of work shouldn’t be humans versus AI. It should be humans with AI, using machines for what they’re good at while protecting and developing the qualities that make humans valuable in the first place, because the greatest risk isn’t that AI becomes too capable; it might be that we become less capable because we stopped thinking for ourselves.
