Company-level lift inside the program window.
Portfolio operating model
AI Software Development Acceleration Program
Drive step-function productivity gains across portfolio engineering teams by enforcing AI-first software development that is measured, repeatable, and scalable.
Reduction in delivery drag and review delay.
Companies benchmarked on the same cadence.
01 / Target company profile
Only enter companies where leverage is real.
Inclusion Criteria
- Revenue: $50M-$2B+
- Engineering: 20-300 developers
- Software is a core driver of revenue or operations
- Non-software companies with fragmented internal tooling and sluggish delivery
Exclusion Criteria
- <20 engineers with insufficient leverage
- Highly regulated environments in early waves
- Large outsourced dev shops with low control and weak accountability
02 / Executive readiness gate
No mandate, no program.
No company enters without CEO and CTO commitment to enforce AI-first development, review metrics weekly, remove blockers immediately, and tolerate zero passive resistance.
CEO / CTO explicitly mandate AI-first development
Weekly metric review cadence committed
Leadership agrees to enforce adoption
Blockers are removed immediately
If this fails, the company is excluded. No exceptions.
03 / Multi-company cohort model
Shared metrics, shared patterns, shared pressure.
5-8
Companies per cohort
Each company runs an identical cadence.
Weekly benchmark sessions compare metrics across companies.
The pressure system creates leverage, pattern acceleration, and portfolio-wide lift.
04 / Six-week compression
Move from mandate to scaled operating model.
Executive Alignment
Force clarity and commitment around goals, baselines, non-negotiables, and failure modes.
- Signed mandate
- Metrics baseline
- Named accountable executive
Pilot Team Selection and Readiness
Exactly 2 engineers and 0.5-1 product lead work from a real backlog with narrow scope.
- Tool access configured without delays
- Backlog groomed and scope narrowed
- Friday 3-hour scope lock with acceptance criteria and success metrics
Acceleration Sprint
Every task goes through AI first. No exceptions.
- Daily 3-hour structured session with both engineers and product
- Live development using Copilot, Cursor, ChatGPT, and similar tools
- Prompting, iteration, debugging, and real-time problem solving
- Friday demo, metrics review, and retrospective
Scale and Replication
Expand from pilot to 2-4 teams while reusing Week 1 patterns.
- AI-assisted code review
- Prompt libraries
- Standard workflows
- Weekly cohort sync to expose underperformers and share playbooks
05 / Measurement framework
No ambiguity. Weekly tracking required.
Core Metrics
- % of AI-assisted PRs
- PR cycle time
- Lead time to production
- Deployment frequency
- Output per engineer
- Defect / regression rate
Diagnostic Metrics
- Prompt iterations per task
- Time to first working version
- % of code generated vs modified
06 / PE-grade governance
A central program office drives accountability.
Company-level metric review led by the CTO plus cohort benchmark session.
Executive checkpoint with CEO and PE sponsor.
Track top performers, identify laggards, and intervene aggressively.
07 / Failure modes
Call them out early.
This is not training. It is a portfolio-wide operating system upgrade.
Run this across 5-8 companies simultaneously and the portfolio gets immediate performance signal, reusable transformation playbooks, and compounding advantage in engineering velocity. Or it exposes which companies are structurally incapable of moving fast. Both outcomes are valuable.