Dive Brief:
- AI coding tools are helping developers complete roughly 21% more tasks, but time spent reviewing outputs rose 91%, according to Bain & Co.’s 2026 Global Technology Report released Tuesday. The analyst firm surveyed nearly 300 senior technology leaders for the report.
- Developers have become orchestrators as they grapple with 47% more workstreams happening simultaneously, a sign that AI is generating work faster than processes can absorb it. Leading AI labs and platforms are investing billions in forward-deployed engineering models to help companies absorb AI into existing workflows.
- “The bottleneck has moved from writing code to trusting it,” Purna Doddapaneni, partner at Bain and one of the authors of the report, told CIO Dive. “Every change still has to be understood, reviewed, tested, and secured, and most of that work still runs through people. Output goes up, but the system settles at the speed of its slowest human checkpoint.”
Dive Insight:
The growing gap between how quickly developers can generate code using AI tools and how quickly it can enter production is creating a new engineering management challenge for CIOs.
Adding more technology without adapting the processes themselves will not solve problems but just create new ones, Bain’s report found.
The stakes are significant for enterprises. Leaders anticipate a 148% improvement in release-cycle speed and a 95% boost in software developer productivity over the next one to two years — far ahead of the 20–27% gains organizations are capturing today, according to Bain.
Closing that gap will require giving AI systems better context. Making information about a company’s codebase — including architecture, documentation and standards — accessible for AI models can reduce the amount of context developers need to manually provide, while giving agents clearer boundaries for the work they can perform.
“Businesses should stop treating verification as a human-only activity and build confidence into the system itself,” Doddapaneni said. “In our work, we see three areas CIOs need to focus on: fixing the inputs, building a deterministic quality harness and measuring the whole system, not just coding speed.”
Rather than relying on developers to catch problems during increasingly lengthy review cycles, the report also suggests organizations build automated testing, security checks and policy controls directly into AI-assisted development workflows.
Human review should be maintained for the decisions where it adds the most value.
CIOs also need to measure whether AI is improving the entire development process rather than individual tasks. Metrics such as code volume or developer activity can show that an AI tool is being used. Release frequency, cycle time, defect rates, review workloads and deployment success can also provide a clearer picture of whether AI is improving end-to-end delivery.
“Today’s workflows and team structures were designed to coordinate people. AI changes what needs coordinating,” Doddapaneni said. “The companies that pull ahead won’t be the ones with the best model. They’ll be the ones that built the architecture that lets AI run reliably at scale.”
