Why AI Works for Them but Not for You

Overview
Why AI Works for Them but Not for You explores why some engineering teams successfully leverage AI tools while others struggle. The article shares lessons from real teams, common pitfalls, and practical steps leaders can take to enable AI adoption across their organization.

Key Takeaways

• Successful AI adoption starts with clear problem definition and measurable goals.
• Providing easy access to data and tooling removes friction for engineers.
• Leadership must champion experimentation while managing expectations about speed of results.
• Investing in upskilling and knowledge sharing accelerates collective AI competence.
• Aligning AI initiatives with product strategy ensures tangible impact.

Who Would Benefit

• Engineering managers looking to introduce AI tools.
• Technical leads responsible for team productivity.
• CTOs and VP of Engineering planning AI strategy.
• Individual contributors interested in practical AI adoption.

Frameworks and Methodologies

• Experimentation framework (hypothesis, pilot, measure, iterate).
• Data readiness checklist.
• AI governance principles.