AI-enabled tools have become part of everyday work almost overnight. 90% of teams now use AI daily, from code generation to accessibility reviews and automation. But as adoption rises, results look very different from one organisation to the next.
This blog distils the key insights from the latest DORA AI findings and explains what they mean for leaders trying to turn emerging tools into real, sustainable business advantage. In short: AI is not the differentiator – your foundations are.
AI Isn’t a Fix – It’s an Amplifier
The biggest misconception about AI is that it will smooth over broken processes or speed up struggling teams. The data shows the opposite.
- Strong foundations + technology = faster growth, smarter decisions, better outcomes
- Weak foundations + technology = bigger gaps, inefficiency, chaos
As the report puts it:
“Tech doesn’t fix broken systems – it amplifies what’s already there.”
This is why outcomes vary so dramatically. AI helps good teams become great – but it also means struggling teams simply struggle faster.
Where Teams Stand Today
Many organisations are already taking positive steps – experimenting with code generation, strengthening documentation, and automating routine reviews. These aren’t just isolated improvements; they’re early signs of teams that are willing to modernise how they work and explore new ways of delivering value.
But the DORA insights show that AI magnifies existing conditions. For some, this means accelerated performance. For others, instability and low throughput rise to the surface.
The Seven Team Profiles: Why Performance Diverges
DORA’s research identifies seven clusters that reveal why teams experience AI so differently:
- Foundational challenges: Lowest performance across all metrics; need fundamental support, not more tools.
- Legacy bottlenecks: Low throughput and high instability driven by technical or process debt.
- Constrained by process: High friction, slow delivery, moderate burnout – often caused by bureaucracy.
- High impact, low cadence: Valuable work but extremely slow output.
- Stable and methodical: Predictable and low-stress, but not top-tier performers.
- Pragmatic performers: Balanced, healthy benchmarks for many teams.
- Harmonious high-achievers: High-performing, low-friction, low-burnout teams.
AI doesn’t move a team from one cluster to another – it simply intensifies the patterns that already exist, for better or for worse.
The Foundations That Matter Most
The report highlights seven capabilities that consistently differentiate high-performing teams:
- Clear policy and governance
- A healthy data ecosystem
- High-quality platforms
- User-centred design
- Validation and quality discipline
- Fast feedback loops
- A culture of continuous learning
These foundations turn experimentation into measurable performance. Without them, AI becomes a spotlight on existing issues rather than a driver of improvement.
How Organisations Can Respond Now
To turn AI adoption into genuine performance gains – not just experimentation – organisations need a clear, structured approach that builds on the foundations highlighted earlier. That starts with understanding where teams are today and creating the conditions for safe, focused scaling:
- Baseline team profiles through short surveys and interviews.
- Visualise adoption and capability in a clear heatmap.
- Recommend specific actions for each team type – no generic playbooks.
- Scale targeted, safe, high-value AI use cases across the business.
Or as the report reminds us:
“Measure where you are before you accelerate where you’re going.”
Closing Thoughts
AI isn’t autopilot. It won’t save teams from underlying challenges, and it won’t replace strong foundations. But when the right conditions are in place, AI becomes a powerful amplifier of performance, learning, and innovation.
If you’re exploring how to strengthen those foundations, or want to map where your teams sit today, we’d love to continue the conversation.
