From tool to teammate: driving meaningful collaboration with AI

"People want to feel important," says Jobin. "There’s a feeling that ‘if everybody can do the same things, I'm not important any more'. Vibe coding is a great example. Even in our company, the really competent people are often the ones who are hesitant about AI and are feeling nostalgic about the past. In previous years, everybody relied on those people to come up with something brilliant. Now they feel that everybody is coming up with something brilliant themselves with AI. It may not be the truth, but that’s how people are feeling."

Jobin says this sentiment manifests in a few obvious ways, including "an increased pressure on everyone to show their creative value. And people are scared about putting their ideas out there because AI can start using them. So there's a resistance building."

"The problem we're seeing is that everything looks the same and is so monotonous, because it was all built by the same AI tools. We need people who will build things differently. Different stands out from the competition, and human creativity is vital.”

Jobin says it's very clear that everyone is still figuring out AI. "Most of the customers that we work with, especially the bigger ones, are spending millions on AI. But then what do they do with all their people? Those conversations are happening, and as a result, employees are concerned about job safety. This is exactly what our research shows".

Jobin says, “It's a balancing act for executives. Of course, you want to preserve your human workforce, but then you're spending all this money on AI initiatives, often mandated by the board. You can't always have both. This is why having a good foundation is so important. It's not just about moving faster – you have to realise value faster and actually show the ROI."

Our research shows that 42% of workers spend more time verifying AI output than they save using it, and 52% regularly correct AI-generated work from colleagues. But according to Jobin, we might already be on a downward curve with product vendors and businesses trying to find ways to eliminate the verification tax.

“A lot of companies that have tried to adopt AI have done so on the left side of the Software Development Life Cycle (SDLC), which is mostly the coding part," explains Jobin. “So people are spending time verifying the code that’s written. But we’re going to see the verification tax come down, because a lot of the platforms, whether it’s Atlassian, GitLab, or Datadog, are building AI into the other side of the cycle – the testing, building, and releasing part – to make that easier. You still need to human oversight and expertise, but with less manual work as before."

Companies are aware of the shifted burden being placed on teams and are starting to reassess their SDLC in light of it. “A lot of the research shows that AI magnifies what your company is already doing,” says Jobin. “So if you're doing things wrong, those problems are going to become even bigger with AI tools involved. Leading organisations are sorting out their foundations first, whether that’s in terms of data, infrastructure, or implementing DevOps processes. That way, they have the right foundations to take that leap with all the AI-enabled features these vendors are putting out."

A big challenge for our customers is to think beyond a human vs. machine mindset, which can be hard when you’re comparing output data on a dashboard. “Obviously, you can’t compete with the machines – they don’t sleep!” says Jobin. Organisations need to think about how they can magnify the impact that humans have in building the software. “

He also says that we’re in a transitional period where people are vibe coding so much that they’re losing their work–life balance. And this intense use of AI is not sustainable. Some of the research is already showing that, eventually, people who contribute a lot with AI slow down and plateau." And just like traditionally written code, it still requires a ‘human-in-the-loop' approach to put into production, which is where the imbalance is now occurring.

But what's going to make the biggest difference to which side of the fence people land? Jobin says a lot of that is down to upskilling and enablement. "Some people are already upskilling, which is great, because as you learn the technology more and more, you will understand what can realistically be done. And often this comes with the realisation that your fears aren't based in reality.

"This goes for executives, too. A lot of leaders either don't realise the power of AI, or they overestimate it, which means they're unwittingly making decisions out of ignorance. That causes a lot of panic. But once people realise the actual power of AI and how they can use it to unlock business value, they realise they need humans to help them to do that, and that fear of obsolescence will eventually go away."