AI adoption is universal. AI-native software delivery is not.
94% of engineering leaders say their teams use AI somewhere in the SDLC, and 36% already run agentic workflows with limited human intervention. But adoption alone isn’t the differentiator anymore. AI has compressed the implementation phase—74% report accelerated code generation—while leaving the phases left-of and right-of code largely untouched. Planning, testing, review, and ship-and-monitor are now the new bottlenecks.
Close the gap between AI adoption and AI formalization
This is a research-backed playbook for CTOs, VPs of Engineering, and engineering leaders who need to:
- Move from experimental AI use to a formalized, AI-native system of work—only 6% of organizations say they have done so today.
- Build the systems needed for teams to trust AI-generated code, specifically the governance guardrails and foundational context to enable safe AI adoption.
- Redesign their SDLC around the four expanded phases—planning, testing, review, and ship-and-monitor—and not just shrink the implementation phase.
- Reinvest the dividends from AI, and why software quality, depth, and engineering capacity are the top priorities industry-wide.
You’ll learn how to build an engineering system of work that gives human-and-AI teams the shared context, governance, and judgment they need to pull ahead in the next 12 months.
In this report, you’ll find:
- The five findings that define the agentic pivot in software delivery
- Why 88% of leaders say they need a governed system of record—and only 19% have built one
- How the definition of the engineering role is becoming broader as code implementation compresses
- Where engineering leaders plan to reinvest AI-driven capacity gains (hint: quality first)
- A stage-by-stage remap of the SDLC—planning, implementation, testing, review, ship & monitor—showing what’s changing and where it’s heading
- Three moves engineering leaders should make now to lean into the expansion and stay ahead over the next 12 months