{"id":32224,"date":"2026-05-15T09:20:00","date_gmt":"2026-05-15T08:20:00","guid":{"rendered":"https:\/\/www.engineernewsnetwork.com\/blog\/?p=32224"},"modified":"2026-05-13T15:13:10","modified_gmt":"2026-05-13T14:13:10","slug":"uk-engineering-leaders-are-likely-overstating-ais-impact-on-their-teams-report","status":"publish","type":"post","link":"https:\/\/www.engineernewsnetwork.com\/blog\/uk-engineering-leaders-are-likely-overstating-ais-impact-on-their-teams-report\/","title":{"rendered":"UK engineering leaders are likely overstating AI’s impact on their teams – report"},"content":{"rendered":"\n

CTOs and engineering managers across the UK are\u00a0likely overstating the productivity gains being delivered by AI coding tools, according to a\u00a0new report<\/strong><\/a>\u00a0from\u00a0technology advisory firm HI Technology & Innovation. The study, based on interviews with more than 100 CTOs, CISOs, VPs of Engineering and senior engineers, finds that AI is producing real and measurable improvements in engineering productivity.\u00a0<\/p>\n\n\n\n

However, companies which actually track those improvements consistently report more conservative gains than those operating on impression alone, suggesting that leadership confidence in AI is, for many organisations, running ahead of the evidence.<\/p>\n\n\n\n

Measured productivity gains attributed to AI coding tools sit at around 20% on average\u00a0across the firms that quantify them, with some teams reporting concrete wins such as project timelines halved and sprint output up 20% to 25% after adopting modern coding assistants. The report makes clear these are real gains worth pursuing. The challenge is that, in the absence of measurement, perceived gains tend to drift higher than the figures organisations would actually arrive at if they tracked them. This is likely because engineers feel much faster with AI than they truly are and may find that their mental burden is lower when using it. In reality, code rewrite rates can creep up and engineers can spend more time on reviews and rearchitecting poorly designed code, something which qualitative feedback and positive bias can obfuscate.<\/p>\n\n\n\n

That gap matters because investment is already large and accelerating.\u00a091% of businesses are now investing in AI tools within engineering, yet only\u00a022% have a formal documented AI strategy\u00a0and\u00a083% are not tracking any metrics to quantify AI’s impact. Without that data, leaders cannot reliably tell which parts of the software development lifecycle AI is genuinely accelerating, where it is quietly creating rework, or which teams and use cases deserve more (or less) investment. The result is one of the largest line items in engineering budgets being managed on anecdote rather than evidence.<\/p>\n\n\n\n

Why this matters now<\/strong><\/p>\n\n\n\n

AI is firmly established in UK software engineering. The question for technology leaders in 2026 is no longer whether to invest, but how to invest well. The report argues that strategy and measurement are what separate organisations that are already capturing AI’s gains from those that are spending without a clear picture of return. The companies which pair their AI investment with a written strategy and a small number of trustworthy productivity and quality metrics are best placed to direct AI where it delivers most, accelerate teams that are already winning with it, and avoid expanding it into areas where the evidence does not yet support it.<\/p>\n\n\n\n

Key findings<\/strong><\/p>\n\n\n\n