How CIOs can ease the generative AI transition for developers

Editor's note: This article was originally published on ciodive.com.

Generative AI is poised to disrupt software development, but CIOs can help ease the transition for workers as they infuse the technology into their daily tasks.

Executives should explicitly define goals and metrics, provide training and keep the lines of communication open throughout the adoption process, Forrester recommends.

Businesses that rush deployments face risks that extend beyond a worsening developer experience, including security issues, performance problems, litigation and disjointed teams.

“The first thing is to make it clear that it is a support for developers and not a replacement,” Andrew Cornwall, senior analyst at Forrester, told CIO Dive. Some organizations are expected to buck the trend and try to trim their developer headcount in response to AI-driven productivity gains, but Cornwall said “I think they’re going to crash and burn.”

In the eyes of CIOs and tech teams, software development is a prime target for generative AI implementation.

“Developers for a couple of years now have started looking at things like cogeneration,” Cornwall said. “Organizations that don’t provide some sort of that mechanism have developers that are bringing their own.”

Ungoverned AI usage can put businesses at risk. Traditional shadow IT has long been a bane for enterprises, but the accessibility of popular models has caused organizations to lose ground in the ongoing battle. More than three-quarters of employees admit to using AI tools that were not approved by their employer, according to a Microsoft and LinkedIn report published in May.

“That presents a significant challenge, not least in terms of data protection and data integrity,” Thomas Humphreys, compliance expert and content manager at third-party risk management company Prevalent, told CIO Dive. “The data protection risks can be potentially quite severe.”

The shadow AI effect

CIOs can combat shadow AI by updating procurement and vetting processes, installing guardrails and promoting acceptable use policies. Organizations that get ahead of the curve will set employees up for success later down the line.

AI-powered coding tools are expected to become fairly ubiquitous within enterprises in the next four years, according to Gartner research. The analyst firm predicts around three-quarters of software engineers will add AI coding assistants to their workflows by 2028, a considerable jump from the 1 in 10 enterprise developers leveraging the tools early last year.

Generative AI will impact more than coding, though, Cornwall said. “Writing code is a chunk of developers’ time, but it’s not a huge chunk.”

Developers can use generative AI to interpret code bases, assist in debugging, enhance onboarding processes, translate programming languages and expedite testing processes, according to Forrester’s research.

As AI tools gain traction in developer communities, there are concerns around employee dependency and skill level. Junior back-end and full-stack developers are on average nearly twice as reliant on AI tools compared to more experienced engineers, a Docker report in April found.

“We are going to see possibly a disruption in the pipeline of new developers, because if I am as productive as I can be with a couple of senior developers and a couple mid-level developers, why would I hire junior developers who can write crummy code when I can have a generic AI do it for me,” Cornwall said.

On the other hand, Cornwall said, some organizations are pairing junior developers with AI for boilerplate tasks. Either way, there could be a disconnect between senior development and junior development on the horizon, according to Cornwall.