Dive Brief:
- AI is transforming engineering roles, as the technology takes over some of organizations’ coding responsibilities and shifts job responsibilities to managing AI outputs, The State of Engineering Excellence 2026 report, released Wednesday, said. The report, conducted by software platform Harness, surveyed 700 developers and engineering professionals at enterprise companies.
- Adopting AI into engineering workflows has become the default, the report found, but teams are struggling to measure the productivity impact and the ROI of the technology. More than half of survey takers said they fear performance evaluations based on AI data and want a clear separation between improvement data and their performance evaluation.
- AI coding is shifting modern software for developers in a way that no other technology has done, according to Trevor Stuart, SVP of product and general manager at Harness. “Engineering leaders are being asked to make multi-year AI investment decisions using dashboards built for a different era of software development,” he said in a statement.
Dive Insight:
AI is changing the way engineering teams complete work and measure productivity, with more time spent on reviewing code, fixing bugs and context switching between tools. When AI generates an organization’s code, output metrics improve, cycle times shorten and developers report feeling more productive for moving through work more quickly.
But 81% of engineering leaders said much of the time saved on coding is now spent reviewing AI’s work. Nearly a third of a developer’s day is spent on this invisible work that doesn’t appear in productivity metrics such as output, the report found.
“It is not the work organizations are trying to accelerate; it is the overhead attached to the work,” the report said.
Enterprises are also raising expectations of their software developers, a HackerRank report found last year. More than two-thirds of developers said pressure has mounted to deliver projects faster.
Engineering responsibilities have expanded to include scrutinizing code quality and security, taking accountability for downstream outcomes, and making judgment calls about when to trust Al and when to override it. Many enterprises have well-established technology stacks to measure engineering outcomes, but they no longer have the right tools to evaluate if productivity gains are real, the Harness report said.
Tech leaders can take steps to update their evaluation systems such as tracking an organization’s code delivery rate or how long engineers spend reviewing AI outcomes, according to the report. Leaders may start by auditing what their organization’s framework captures versus what their AI adoption creates.
As is applicable in most AI adoption scenarios, Harness also recommended planning for more governance and security reviews; it also suggested tech leaders work with their developers to build systems and guardrails by which they will be measured.
Technology advances haven’t affected engineers and the way they do their jobs drastically until now, Stuart said. The onset of cloud and internet infrastructure operated a layer beneath developer roles, but AI is forcing major change.
“AI is reshaping the developer’s job entirely,” he said. “And the measurement frameworks that the industry has relied on for the past decade weren’t built for this new unit of work.”
