Today’s tech blogger: Mathieu Imfeld, WMPC Head of Engineering Strategy and UBS Distinguished Engineer

Artificial intelligence fuels innovation across diverse business use cases, with its capabilities focused on areas of greatest strategic impact.

What happens when you give AI capabilities to an excited engineering population? They get creative.

Here are four perhaps less expected and culturally relevant examples I have recently come across.

One: Overcoming with AI – in ways you didn’t expect it

Some of the junior colleagues I mentor are not yet used to speaking in front of an audience. I certainly remember how anxious I also felt the first time I presented something to a larger audience at the beginning of my career.

How AI can help? I recently discovered that my mentees schedule meetings between themselves and the AI-driven tools we use to take meeting minutes – just to practice their presentation. While I’m certain the tools weren’t created with this use in mind, it’s a fantastic and creative way of checking that you’re bringing your ideas and thoughts across.

It also appears to remove some of the fear of talking to an audience. You still have to talk, but deep down you know it’s not real people you’re talking to. Better still, the AI behind this functionality is designed to be independent, unemotional, and collaborative. AI-assisted summarizations are concise and the perception of talking to an independent reviewer boosts confidence.

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Two: Bringing more tech and non-tech people together

Let’s take our global UBS Hackathon as an example. I enjoy offering technical and project advice to the participating teams every year in my capacity as an engineering peer and Distinguished Engineer. This year’s edition felt different – in a good way. Teams weren’t just made up of engineers anymore. People from other areas joined in, especially those who work with tons of documents and data. The language barrier we sometimes see between those who define requirements and those who build solutions was significantly reduced (if not eliminated). Thanks to AI tools that understand plain English, everyone could contribute and collaborate more easily. It was inspiring to see how this newfound collaboration brought great tech solutions and was cheerfully celebrated – with custom T-shirts and cupcakes for everyone involved.

We’re using AI not only for its obvious technical coding assistance, but also as a cultural equalizer to improve, clarify and translate. It clears up confusing jargon, translates ideas between teams, and makes it easier to work together. That leads to better products, built faster.

When you combine that with a platform controlling the data security, flow and its regulatory boundaries you get a high-productivity solution removing many technical hurdles of the past.

Three: Introducing Agentic AI

Adding Agentic AI makes platforms more powerful, but these AI agents need to connect with your existing business systems. Agentic AI helps identify tasks that couldn’t be automated before – like older services that relied on manual requests. Carrying out these tasks is a learning in itself, because it changes how people think about the overall system design. The next step is to update the architecture to support these AI agents and the rules they need.

This includes further enabling data products and non-interactive automation, which improves how data is used and enables more automation. The result is a better setup for both people and AI to work together effectively.

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Four: Faster, safer and more effective experimentation

Finally, doing all this requires refactoring. Changing existing software and infrastructure is often perceived as risky and time-consuming effort, especially when it’s of an experimental nature. This is where our teams truly leverage AI-assisted coding to do all the tedious ancillary work of refactoring the parts that aren’t core to a theory to be proven.

In the past, teams with a particularly large code base would think twice before spending days making preparatory changes when, at that point, their vision for an improvement was still at an early stage. Now they simply create a branch, tell their AI assistant about their theory, then run their tests to confirm it’s correct.

AI is a technical – but also – a cultural game changer.

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