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AI tools alone won't boost engineering productivity; teams must adapt

From El Nacional · () Spanish

Translated from Spanish, summarized and contextualized by DistantNews.

At a glance

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  • Engineering teams are not automatically more productive with AI tools alone; success depends on rebuilding team structures around them.
  • AI amplifies existing team strengths, making strong teams better and struggling teams more chaotic, highlighting workflow issues over tools.
  • The shift is from

Companies are discovering that simply adopting AI coding tools does not automatically boost engineering productivity. The real gains come from fundamentally restructuring teams to leverage these new technologies, according to Claudio González, CTO of a German software engineering consultancy.

While AI adoption is widespread, with an estimated four out of five developers using it by 2025, a paradox has emerged: confidence in AI-generated output has declined as usage has increased. González theorizes this is because many clients expect AI to magically improve output without changing their underlying processes. "Adding a code assistant on top of the old way doesn't get you there," he stated.

Adding a code assistant on top of the old way doesn't get you there.

— Claudio GonzálezExplaining why simply adopting AI tools does not automatically lead to increased productivity.

Google's 2025 DORA report, a key study on software delivery, suggests AI acts as an amplifier. It enhances the performance of already strong teams but can exacerbate chaos in weaker ones, depending entirely on the surrounding workflow. Speed improvements in one area can expose bottlenecks elsewhere.

These advances come from redesigning the workflow.

— Claudio GonzálezHighlighting the importance of process overhaul for achieving gains with AI.

González emphasizes that the solution lies in workflow redesign, not just tool acquisition. His firm's clients achieved significant increases in development output, faster feature cycles, and quicker modernization only after overhauling their processes from start to finish. "These advances come from redesigning the workflow," he said.

McKinsey's research supports this, finding that combining generative AI with genuine process changes yields far greater results than simply adding an AI assistant to existing workflows. The shift is from a model of numerous developers manually handling tasks to a smaller group of highly skilled individuals acting as "architects of intent." These architects, supported by AI agents for boilerplate code, testing, and legacy system decoding, focus on strategic direction and validation. The limiting factor is no longer the number of hands but the quality of decision-making and verification.

The limitation is no longer how many hands you have, but how good your people are at deciding what to build and verifying it is correct.

— Claudio GonzálezDescribing the shift in engineering focus from manual labor to strategic intent.
DistantNews Editorial

Originally published by El Nacional in Spanish. Translated, summarized, and contextualized by our editorial team with added local perspective. Read our editorial standards.

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