Helping Teams Adopt AI
90% of developers use AI at work.
Yet McKinsey found only 7% of organisations have fully scaled AI.
That gap isn't a tooling problem. It's a team problem.
Last week I shared how I automated most of my Android SDLC with 4 Claude Code commands. Individual productivity goes through the roof.
But here's what McKinsey, DORA, and every adoption survey in 2026 keep showing: individual wins don't become team wins automatically.
The 2025 DORA report calls AI "an amplifier" - it boosts strong teams and exposes dysfunction in fragmented ones. AI adoption correlates with higher throughput AND higher instability. More code ships. More things break.
The WRITER/Workplace Intelligence survey found 79% of organisations face challenges adopting AI - up double digits from last year. And 75% of executives admit their AI strategy is "more for show" than actual guidance.
So what actually works?
From what I've seen, the missing piece is never the AI tool. It's the process around it.
Structured review gates. Clear ownership of AI output. Spec-driven guardrails that the whole team agrees on - not just the one dev who figured out the prompt.
AI doesn't replace engineering discipline. It demands more of it.
If you're leading a team through this transition - what's the hardest part? Tooling, trust, or process?
🔗 The SDLC framework I mentioned - 4 Claude Code commands for the full Android user story lifecycle: https://github.com/guidovezzoni/SDLC
Sources:
* McKinsey State of AI 2025: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
* DORA 2025: https://dora.dev/dora-report-2025/
* WRITER/Workplace Intelligence 2026: https://writer.com/blog/enterprise-ai-adoption-2026/