Enter the access password to view the site.
We've built the framework for an AI-Enhanced FDLC. The question for this offsite is whether we go forward together. Four asks of leadership turn intent into delivery: alignment on the path, protected time to change how we work, budget for the tooling that actually moves the needle, and a FY27 adoption OKR that holds the org to it.
Each ask reflects a constraint the framework cannot solve on its own. Without all four, the shift either gets diluted in execution or runs in parallel with the work it's trying to replace.
Confirm that the AI-Enhanced FDLC is the path we are taking. While we still need to validate the specific activities and tasks with our pilot, there should be alignment across all PM, UX, and Engineering leaders that we need to embrace an AI first way of building software.
The next pilot phase should be for calibration, not a go/no-go gate. The signal it produces shapes how rollout sequences across the org. Without alignment at this level, teams risk optimizing for capability level outcomes vs. platform driven alignment.
Changing how we work takes time. Asking delivery teams to ship the roadmap and change their workflow at the same pace will fail. We need explicit, protected capacity for the change itself: companion file authoring, constitution grounding, spec sufficiency work, gate adoption, and the reflection that compounds into the next iteration.
Without protected time, AI gets bolted onto the existing process. The activity moves to AI, the workflow stays the same, and the outcomes don't change.
The right tooling does not come for free. We have evidence that Copilot on its own is not sufficient for the outcomes we want: constitution-grounded generation, deep context loading, multi-step agentic work, and the persistent context that makes spec-driven development reliable.
The budget ask covers an enterprise Claude license, CI/CD pipeline setup for Track B teams, token budget governance, and the platform infrastructure (Textura MCP, agent landscape, observability) that the workflow depends on.
Going into the start of FY27 (July), we need a set Agentic SDLC adoption OKR with named owners. This cannot be an aspirational metric tracked in isolation — it has to be a rollup of team-level OKRs across the organization so every team's progress aggregates into the same org scorecard.
Without a committed OKR at this level, adoption signal stays anecdotal and rollout loses the feedback loop it depends on. With it, the AI FDLC Review has the outcome data it needs to keep calibrating where the framework goes next.
Not asks in their own right, but adjacent topics where shared understanding at this level keeps the rollout calibrated to the right outcomes.
The point isn't more lines of code. It's a shorter path from problem definition to release-ready, with higher implementation fidelity and less rework. Productivity is measured against cycle-time delta and cost-per-outcome, not activity volume.
Adoption failures usually look like silence: people quietly working around the new workflow rather than naming what's broken. Surface friction early, treat tooling pain as a signal, and separate workflow friction from identity strain. They need different leadership responses.
The shift is from doing the work to directing, reviewing, and certifying it. That changes what each discipline is great at. Invest in the skills: spec authorship, prompt and context design, review craft, and judgment under uncertainty.
These aren't asks. They're commitments and signals the leadership group should be aware of so the conversation lands in the right place.