• AI adoption demands maturity frameworks that assess risk and context, and organisations must be honest about what they are actually trying to achieve with AI in their specific circumstances.
• Collaboration patterns are changing fundamentally: Teams are shrinking, roles are blurring, and the question is no longer how to structure teams but how to make collaboration more effective in whatever form it takes.
• The explosion of AI-generated code demands entirely new processes for quality, cognitive load management, and accountability.
• Organizations that failed to embed agile fundamentals face catastrophic risk with AI adoption; fast feedback loops, reflective learning, observability, and shipping the right value to the right users remain as critical as ever.
• Engineers are shifting from contributors to custodians: The defining future skill is not writing code but effectively directing, validating, and building guardrails around AI agents.
• The industry must confront the environmental costs of AI usage, the erosion of diversity of thought from homogenized agent outputs, and the accountability gap when no individual fully owns AI-generated systems.

In the annual Culture & Methods Trends Report podcast, a panel of QCon speakers and InfoQ contributors discussed AI adoption maturity and risk, the transformation of engineering team structures and roles, and the human dimensions of software development that must not be lost in 2026. The discussion was hosted by Shane Hastie (Lead Editor, Culture & Methods) and included Ben Linders, Rafiq Gemmail, Craig Smith, Vanessa Formicola, Shawna Martell, Phillip Mortimer, and Yinka Omole.

Source: InfoQ Culture and Methods Trends Report – 2026

I was quoted in the article:

It’s the gap between the good and the not so good and the haves and the have-nots. Some organizations have embraced this and ran with it, but there are many organizations… they never really made the leap to agility practices in the twenty-five year span that we had in order to do that. And now they’re trying to adapt to these new practices and still building it on organizations that are still tending software’s being built in 1995.

Source: Culture & Methods Trends 2026: The Human Side of AI Engineering