The AI-Fluent SDLC
Software teams have adopted AI fast, but benefits are still anecdotal. This paper examines why, and what separates the teams that convert AI into delivery gains, from those that do not.
It’s grounded in DORA’s research, experience from our team of AI Forward Deployed Engineers, and our 20+ years of experience building software for highly regulated industries.
- Adoption fluency. A large part of the AI budget is currently spent on coding, which is a fraction of the entire delivery cycle. This is why productivity gains are elusive and cycle times are largely unchanged. There are spikes of productivity across the org but these are not predictable and sustained.
- Enabling fluency. Fluency rests on two organizational layers. Capability - the culture, learning, judgement, and documentation practices that let people direct and check AI across the lifecycle. Conduct, the standards, regulatory rules, and guardrails enforced while AI generates code.
- Measuring fluency. Fluency is when the full delivery cycle is compressed without losing trust in what ships. It can be measured by how much a team trusts the AI-assisted work, along with how much they've been able to reduce their cycle time and enable flow.
*Figures cited from individual engagements are illustrative observations, not industry benchmarks.
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