During the many years he has been in the tech industry,  Alan Reed, head of platform innovation at bet365’s Hillside Technology platform, says he has not witnessed the possibilities and adoption of a technology move as fast and as widespread as generative AI (GenAI).

Given that GenAI technology hit the mainstream with GPT 4 two years ago, Reed says: “It was like nothing ever before.” And while the word “transformational” tends to be generously overused in technology he describes generative AI as “transformational with a capital T.”

But transformations are not instant and businesses need to understand how to apply GenAI most effectively, and figure out where it does and does not work well. “Every time you hear anything with generative AI, you hear the word journey and we’re no different,” he says. “We are trying to understand it. We’re trying to understand its capabilities and understand our place with generative AI,” Reed adds.

Early adopters are keen to understand how to use GenAI in day-to-day work, which, he says, can range from being an AI-based work assistant or a tool that changes the way people search for information to using AI as a gateway to the heavy lifting required in many  organisations.

He points out that bet365 is no different. “We have a sliding scale of ambition, but obviously like anything we do in an organisation of this size, it must be measured, it must be understood and we do need to be very, very clear what we’re using generative AI for.”

One of the very clear use cases for GenAI is in software development. As Reed notes, the entire tech industry is trying to work out how to get the best out of what the technology can offer. Using it for coding is no different to when GenAI is used to write an essay, where English is substituted for a programming language during training. However bet365 is looking for more than AI-based coding. He says: “Writing code is great, but what we are very interested to see is if it can read code.”

The particular problem bet365 is interested in solving is using the AI to understand a code base without someone having to read the code itself.

“That’s very appealing to us,” he says. “Like any larger tech company or organisation that’s been around for a number of years, you start to think of your technical data and your legacy code base and that there is a percentage of your workforce just maintaining this legacy code base.” 

This was the original use case for the team. “Most projects that you implement start to age and erode the second the code goes live when you move into run mode. It’s more of a maintenance model and it becomes more and more complex,” he adds.

In Reed’s experience, if someone does not have visibility of the code and is not present during the maintenance of that code, it can sometimes be more difficult to understand how the code actually works. “Every now and again you realise you don’t actually understand the code to the degree you need to for the task ahead,” he says. 

The initial goal was to improve the business’ understanding of its maintenance code base. But like many businesses, there has been an opportunity to modernise this code base,  as Reed points out: “No organisation of any size isn’t trying to modernise its own code base.”

And for Reed, AI has to be part of that. “We’re trying to understand what that means for us.”

According to Reed, once the AI can provide the business with a level of comprehension about its legacy code base, it then becomes possible to ask more complex architectural questions that will often be raised as organisations modernise their IT systems. This enables IT decision makers to identify where functionality is duplicated and how to segment the code to move the application to a cloud native architecture. GenAI’s ability to read and understand the code can also help software developers identify where certain code can be replaced by improved functionality, or more performant code, available in a software repository.