AI is writing more code than ever. Software specialist Sonar’s 2026 State of Code Developer Survey, based on more than 1,100 professional developers, found that 72% of those who’ve tried AI coding tools now use them every day, and that today, AI now accounts for 42% of committed code, a number that’s expected to reach 65% by next year.
But while developers embrace AI-enabled coding services, many professionals are uncertain about how their businesses will cope with this influx of AI-generated code. Sonar’s research additionally discovered that 96% of developers don’t fully trust it, and only 48% always verify it before committing.
In fact, 38% of developers told the researchers that reviewing AI code requires more effort than analyzing code written by human colleagues. That’s a pattern recognized by Tim Chilton, a managing geospatial consultant at UK mapping agency Ordnance Survey (OS). “There’s truth in the headlines that AI can get carried away, and developers spend more time fixing the code that’s been created than not,” he says.
While that’s not a pattern he sees at the OS, Chilton says his organization is cautious about how developers use AI coding tools, and conversations with other digital leaders suggest a similar approach. AI-enabled coding is indeed almost ubiquitous, and it can allow developers to reach new, higher levels of productivity, but more doesn’t necessarily mean better code.
To ensure that such coding services produce business benefits, CIOs must take control of the development process. They should exploit agents carefully, set professional responsibilities, and establish guidelines, policies, and organization-wide training.
Exploiting the agents
Snowflake co-founder and president Benoît Dageville suggests there are two ways to view AI-powered coding. One is the scary way, where AI takes over your tasks without human input. The second is the efficient way, where AI helps you do the same things as before, but at a fraction of the cost. He’s a big believer in the latter, both for himself and his teams.
“The route depends on the organization; their application of AI coding depends on their priorities,” he says. “However, I hope that overall, AI will have a positive effect on humans. I’m using AI every day to code, and if I compare myself to one or two years ago versus now, I’m more efficient and productive, but I’m also writing five times more code.”
Steve Lucas, CEO of integration technology specialist Boomi, also notes the increase in output. He quotes research that suggests 4% of GitHub public commits are already being authored by Claude Code. That proportion is expected to rise to 20% of all daily commits by the end of this year. Staff at Boomi already use AI tools to produce more code.
“We’re writing 10 times the amount today than we did two years ago,” says Lucas. The increase in AI-enabled coding is so significant that he’s spoken with Boomi chief product and technology officer Ed Macosky about goals for the proportion of self-modifying code.
“I believe AI should recursively modify 80% of a software organization’s code,” Macosky says. “Infinite improvement is demanded. If AI is extraordinarily good at breaking security protocols, the only way to keep up isn’t the kind of accordion model of human code design. AI use must be linear in scale and continuous.”
Crucially, Lucas suggests using AI to check and modify that AI doesn’t mean coders go away. “In fact, I think we’ll need more engineers, not fewer,” he says. “We need these professional experts to ensure the quality of the code that’s written.”
At OS, Chilton uses Coco, Snowflake’s coding agent. The tool does as much as 90% of a development task that he would otherwise complete from scratch. “It’s taken an hour to finish, instead of weeks or never,” he says, reflecting on the power of AI coding services.
However, the tool’s influence is carefully constrained. The agent doesn’t work in isolation, and Chilton checks its output in detail. He says all organizations need a similar process and should value experienced professionals who can use AI coding tools effectively.
“If you’re giving a tool to someone who isn’t capable of checking an agent’s work, as a digital leader, you run the risk of putting too much faith in AI just running with its output,” he says. “I think that’s a big risk, so there’s still a need for experienced developers.”
Setting clear responsibilities
While placing humans in the loop can help ensure AI-enabled code is safe and effective, developers shouldn’t be weighed down by their new management responsibilities.
Gill Haus, CIO at Chase, the consumer banking arm of JPMorgan Chase & Co, says AI coding benefits can only be realized when agents are introduced with a strong focus on automating supporting technology and processes, which reduces the need for too much human input.
“If you have 12,000 engineers, they can write 12,000 engineers’ worth of code in a day,” he says. “If those 12,000 engineers have agentic technology, they can write much more. But if you don’t have automation left to right with confidence, you create more manual processes. You can’t move as quickly since there are only so many humans you can put in the middle.”
Haus says Chase never releases services in production that don’t pass its high bar for quality and security. Strong engineering practices, pipelines, and architecture, plus a tried-and-tested approach to automation, can help ensure AI coding produces solid outputs without a burdensome amount of human intervention.
“We’re focused on effective automation so we’re able to unleash this technology safely and effectively,” he says. “This is something many other companies should be focused on if they aren’t already, because otherwise you can’t keep up with the pace of change.”
François-Xavier Pierrel, group chief data and ad tech officer at French TV network TF1, is another digital leader who says maintaining control as AI writes more code means keeping the human in the loop in a clearly defined manner. His team documents agentic processes using Atlassian project management and collaboration tools, such as Confluence.
Pierrel says policy management is crucial as the agentic transformation gathers pace. One key step is ensuring professionals who use agents conform to internal standards. Smart businesses set policies that state clearly when staff can explore an agent, delegate work to it, or release it in production.
“We don’t have hundreds of agents deployed yet,” he says. “But the moment you reach a certain level of volume, if you don’t have compliance and policies in place, AI becomes a critical mess, and then it can be disruptive in a negative way.”
Building strong foundations
Kirsty Roth, chief operating officer at content and technology specialist Thomson Reuters, agrees digital leaders must be proactive. Her firm’s explorations with models and agents suggest that AI can help with time-intensive coding processes, such as writing. This capability means her organization carefully considers the roles people fulfil.
“In the last 12 months, we’ve put lots of thought into where we’re seeing benefits,” she says. “It’s great AI can do some of the coding work, but what does that mean for people’s jobs? We used to have thousands of engineers. Do we now need more architects or product people?”
Roth refers to security. While cyber is always a priority for Thomson Reuters, the firm dedicates significant resources to controlling threats in the AI age. Developing high-level development and security skills requires a concerted effort. Yet Roth says ensuring people have the right skills to manage AI is in many ways a familiar IT trend.
“All this effort is no different than digital, cloud, mobile, social, or other big tech trends,” she says. “They all take several years to have an impact as the initial hype comes, and then you’ve got to work through the reality and think about how you upskill people for the new world.”
Crucially, coding is no longer the sole preserve of the software developer. AI coding tools make it much easier for non-IT professionals to vibe code applications. Tal Carmi, CIO at technology specialist WalkMe, says non-developers can create useful tools. However, effective outputs rely on a careful balance between enablement and guidance.
He adds that most of these employees don’t need in-depth capabilities, such as a database or a content delivery network. Instead, non-IT staff require a dedicated place to upload their products. WalkMe’s engineering specialists ask key questions to ensure their outputs are safe, considering credentials and potentially problematic packages.
“You need to add guardrails,” he says. “A lot of the work for in-house engineers is shifting from building to testing. The people who aren’t coders don’t necessarily have security in mind.”
The key takeaway, suggests Emmanuel Frenehard, chief digital officer at biopharmaceutical giant Sanofi, is that digital leaders must help professionals feel comfortable. Successful digital leaders ensure the business generates value from agents, and his firm is helping its staff, particularly its developers, to embrace these tools.
“I genuinely worry for professionals who don’t use AI to code, because in five years, they may not have jobs,” he says. “So we’re guiding, coaching, and training them, but people must take the journey. The train has left the station. You can decide to be on it, or just look at it.”
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