Software engineers need new skills in the age of AI

AI is transforming software engineering, changing what software engineers do and the skills they need to succeed. A new survey from Uplevel, of over 100 senior engineering leaders at mid-to-large technology companies, looks at what they believe will be the most important skills for their teams.

It finds that validation of AI outputs and quality assurance (QA) is valued highest, cited by 66 percent of leaders, followed by performance monitoring and optimization (39 percent), and system architecture and integration skills (34 percent)

Code generation is the activity most likely to require less human effort according to 50 percent of respondents, as well as being the area most likely to be transformed by AI (56 percent). However, A previous Uplevel analysis found a 41 percent increase in bug rates with generative AI (GenAI) for coding -- underlining why QA is now seen as mission-critical.

“The potential of AI to deliver customer value is far greater than just code generation,” says Uplevel CEO Joe Levy. “Leaders should consider new use cases that clarify customer needs and automate time-consuming tasks like reviews, deployments and testing. That’s where AI begins to deliver real customer value -- in the outcomes, not just the code.”

The survey finds 87 percent of respondents say their business is ‘prepared’ or ‘very prepared’ to implement AI solutions. But there are concerns that hidden bottlenecks could slow AI’s long-term impact. Top of the list is technical debt -- the extra work created by quick software fixes that speed things up now but create headaches and complexity later. 27 percent see this as the greatest strategic threat to AI’s potential, followed by a lack of clear AI strategy (22 percent).

Other concerns cited include data security and privacy risks (30 percent), quality control and reliability issues (19 percent) and skills gaps, with a lack of AI expertise (18 percent).

Engineering leaders want AI to increase operational efficiency (53 percent), accelerate innovation (40 percent), improve decision-making (28 percent) and boost competitive advantage (23 percent).

Yet their measurement habits lag behind their ambitions. Many still lean on individual productivity metrics, even though their biggest delivery constraints are systemic, including cross-team dependencies (31 percent), complex architectures and technical debt (21 percent), and unclear project requirements (14 percent).

“Until leaders modernize their measurement frameworks, the very outcomes they hope AI will deliver may remain stubbornly out of reach,” Uplevel CEO Joe Levy adds. “The organizations that get it right will look beyond activity metrics -- tracking how AI improves teamwork, accelerates delivery, and drives business results that matter.”

The full report is available from the Uplevel site.

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