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Developers are embracing AI coding assistants

But with this trend comes a number of tricky questions:��

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• What role should AI play in the development process, and where in the lifecycle is it most effective?��

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• What risks come with relinquishing more aspects of software development and engineering to AI tools?��

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• How does the role of human talent change in an increasingly AI-powered space?��

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• How long can the answers to any of the above hold true as technology continues to evolve?��

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Based on their early work in AI-first software delivery (AIFSD), ��Ӱֱ�� practitioners have reached a number of conclusions that they advise engineers and business leaders to bear in mind:����

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AI adoption is not as advanced as it might initially appear. “Most developers and engineers are applying purpose-built AI tools that are small in scope to a limited subset of coding,” Fowler points out.��

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AI-first software delivery goes beyond coding. ‘Real’ AIFSD is an end-to-end, collaborative and iterative process that’s designed to leverage the potential of AI from the ground up - from framing the scope of a software product, all the way to long-term support and maintenance. Coding assistants can serve as a gateway to this process, but “only a small number of organizations are trying to do more than that, or to treat AIFSD as more than the introduction of a tool that doesn’t require any change management,” says Birgitta Boeckeler, ��Ӱֱ��’ Global Lead for AI-assisted Software Delivery.��

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“There’s significant potential in the testing space, but the barriers are that in some areas, the tools are not quite there yet�� - or people are waiting for specialized tools when they might not have to,” she adds. “Chatbots for advanced features can actually get you quite far in areas like analysis and requirements descriptions, and come with a very low barrier to experimentation.”����

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Legacy modernization is where AIFSD could have the most impact. Changing competitive realities and the rise of AI have made modernization a more urgent priority for , but reforming the legacy tech estate remains a formidable task. AIFSD promises to help enterprises address this challenge.��

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“(AIFSD) will significantly accelerate reverse engineering, so we’re no longer spending months just trying to understand the scope of a system, or locked in ‘analysis paralysis,’” explains Alessio Ferri, Lead Software Engineer at ��Ӱֱ��. “We can use the time we gain to go deeper or wider, ultimately learning more about what we’re working with and further de-risking the whole modernization program.”������

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“Acceleration can impact not just the development of software, but the whole software feedback loop, including putting things into production, learning from that and changing the business’s processes and growth trajectory,” Fowler points out. “In that sense, the effects of AIFSD can be dramatic – it affects costs, but also what you can do. Once you’re able to move faster, many other things change as well.”������

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“There’s a big focus on speed, but also significant potential for AIFSD to make knowledge management easier for developers, enhancing onboarding and staff mobility,” says Boeckeler. “It introduces not just new ways to write code, but also new ways of finding and accessing information.”

Boeckeler also notes that as AI tools grow more sophisticated, they can give engineering teams the ability to simultaneously tackle much larger problems. Beyond simply recommending code for a single method or function, newer AI agents can handle complex tasks like exposing a new data field in an API, by changing files, running tests and preemptively addressing any errors that result.��

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“The basics are all automated, but human engineers supervise and adjust the results,” she explains. “Some teams are already using agents 100% of the time, and in certain cases, that gives them an 80% productivity boost.”����

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These kinds of advances have been borne out in recent ��Ӱֱ�� client projects that drew on AIFSD approaches, including:�� ��

Boeckeler believes test-driven development is likely to be among the practices AI affects most, even if it’s not yet clear if it will be for the better, or the worse.��

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“Test-driven development is based on the principle of advancing with baby steps,” she explains. “Good design is driven by writing the test first, because that forces developers to think about their specifications and what they actually want to achieve. But now AI can just generate tests, code and entire functions in one go. Is that a good thing or a bad thing? And in which situations should that be used?”������

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One concern is that the ease and speed made possible by AIFSD could breed complacency. “It’s become so quick and easy to build and put in features that engineers aren’t always conducting proper analysis of what a complete, well-rounded feature would be, and what other consequences it might have, because it was never originally properly planned or prioritized in the product roadmap,” Boeckeler says. “In that sense, AI-accelerated development can suddenly create a tail end that a team has to come in and clean up.”��

“Speed doesn’t necessarily mean you scale back the team; it can also mean you do more with the same amount of people,” she explains. “There’s always a backlog of more to do. Organizations are sitting on a mountain of accidental complexities that have been building up for decades, and many of our clients are struggling to get things out the door because of an existing estate that’s getting harder and harder to maintain.”��

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What will change with AI are the capabilities engineers need to bring to their roles.��

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“People will have to learn different things, like the formalisms of the high-level programming languages, how to interact with LLM tools, and dealing with the fact that non-deterministic systems are producing code that will run deterministically,” says Fowler. “Knowledge about what good APIs look like, and how to separate things into modules could become a major part of the role. Somebody coming into the profession now might learn different skills and ways of working, but could have an advantage because they won't come with any ballast.”��

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For now, rather than aiming to retool teams entirely, ��Ӱֱ�� experts advise businesses to build AI-readiness by encouraging experimentation, which is an essential step on the road to adoption.

“Many large organizations don't have a playground where developers can experiment with AI,” Ferri points out. “People need to be able to make mistakes. The question is how you allow for safe mistakes to happen, so that you can learn about the impact on the organization and the data that the organization controls.”

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“You have to use AI for a while to get an intuition for when to reach for it,” agrees Boeckeler. “There’s a lot of social learning. Sharing stories with each other about challenges and achievements is a huge lever. Structured training programs can only get you so far, because the space is changing so fast under your feet.”��

“The tagline we've been using from the start is that Gen AI amplifies indiscriminately,” says Boeckeler. “When you ask it to generate code, it doesn’t distinguish between good and bad, and when you have bad quality code, it's going to amplify that.”����

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Next steps

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Make the next step towards empowering your engineering function with AI.