Preparing for AI SDLC transformation.

The 80/95/6 problem — sourced.

Why 80%+ of AI projects fail (RAND), 95% of gen-AI pilots show no P&L impact (MIT Media Lab), and only 6% of organizations report meaningful business results (McKinsey). Not pundit quotes — primary research.

Why it's a process problem, not a tools problem.

The METR controlled study on perceived vs actual speed — where developers using AI believed they were 20% faster but were 19% slower. How that 39-point gap compounds into $2–4M/year of invisible rework for a 50-engineer team. Full methodology and assumptions shown.

The 16-question readiness assessment.

A structured gap analysis you can run with your leadership team in 30 minutes. Identifies where AI SDLC adoption will have the biggest impact on throughput, quality, and cycle time — and where it will backfire if deployed today.

• Do Product, Design, and Engineering share a common workflow system?
• Can you measure your team's actual output — not just lines of code?
• Is code review a formal quality gate, or a rubber stamp?
• Do AI-generated PRs get rejected for the same reasons repeatedly?
• Can your design system enforce constraints on generated code today?
• … 11 more, with scoring rubric

10 criteria for any AI SDLC platform.

Cross-functional by design · structured workflows · provider-agnostic · quality gates before review · learning from feedback · auditable by default · runs on your infrastructure · measurable ROI framework · gradual adoption path · stack-agnostic. Evaluate any platform against this list — including Stochastic Macro.

A pilot structure you can propose this week.

The four-phase pilot we'd propose to your leadership team — scope, metrics, gates, and kill criteria. Timeline, team composition, and the before/after data points that will prove (or disprove) the investment within 60 days.