AI adoption without a strategy is just expensive prompting.
Your team has access to AI tools. That is not the hard part. The hard part is designing the operating model — the system that turns scattered AI usage into structured, measurable engineering leverage.
I help engineering teams bridge the gap between "we use AI" and "AI is embedded in how we operate."
Two ways to adopt AI. One of them works.
Most teams are on the left. The ones getting real leverage are on the right.
Ad-hoc AI adoption
Designed AI operating model
I help teams move from the left to the right — in weeks, not quarters.
What an AI strategy actually contains.
Not a PowerPoint. A working system your team can execute from.
Workflow architecture
Where AI agents execute vs. where humans decide. Clear insertion points in your existing development lifecycle — not a parallel process.
Agent configuration
Prompt templates, system instructions, and tool configurations specific to your codebase, standards, and domain. Not generic ChatGPT prompts.
Review & quality gates
How AI-generated output is reviewed, tested, and validated. Concrete checklists and automated checks — not "just review it carefully."
Governance framework
Security policies, data handling rules, compliance boundaries, and approval workflows. Documentation your legal and security teams can sign off on.
Measurement system
How you track whether AI is improving velocity, quality, or both. Baseline metrics, ongoing measurement, and reporting for leadership.
Team adoption plan
Training materials, adoption schedule, and support structure. How to roll out to the full team without losing the skeptics.
Strategist and practitioner. Same person.
I design AI strategies AND I ship production code with AI agents every day. When I recommend a workflow pattern, it is because I have already built it, tested it, and iterated on it.
30 years of enterprise engineering — Fortune 500 commerce platforms, teams of 100+ developers across multiple continents. I have seen what works at scale and what breaks.
The methodology behind this is Mission Command — intent-driven leadership that works for both human teams and AI agents. I also build my own products with AI agents daily. The advice I give is informed by daily practice, not quarterly reports.
Common questions
What teams ask before designing their AI strategy.
What is an "AI operating model" exactly?
An AI operating model defines how AI is embedded in your development workflow at the system level. It answers: Where do agents execute vs. where do humans decide? How is AI-generated output reviewed? What quality gates exist? How do you measure improvement? It turns scattered AI usage into a designed system.
We are a small team (10-20 devs). Is this relevant?
Yes — and it is actually easier to implement at smaller scale. Small teams can adopt a structured AI workflow in weeks, not months. The patterns I design scale up, so you build the foundation now and grow into it.
How long does an engagement typically take?
A focused audit and implementation plan takes 2-4 weeks. Ongoing fractional leadership for guided adoption is typically 3-6 months. The scope depends on your team size and how deeply AI needs to be integrated.
What if we have already tried AI tools and the team is skeptical?
Skepticism usually means the team tried AI without structure and got inconsistent results. That is a normal reaction. A designed workflow with clear patterns and quality controls changes the experience. I work with teams to build confidence through demonstrated results, not mandates.
Do you work with specific AI tools or are you tool-agnostic?
I work with whatever tools fit your constraints. I have deep expertise with Claude Code, GitHub Copilot, and custom agent systems. The operating model I design is tool-agnostic — the patterns work regardless of which AI provider you use.
How do you handle security and compliance concerns?
Governance is built into the operating model from day one, not bolted on after. I design security boundaries, data handling policies, and compliance checkpoints as part of the workflow. Your legal and security teams get documentation they can approve.
What deliverables do we get?
A documented operating model including: workflow diagrams, prompt templates, review checklists, quality gates, measurement criteria, and training materials. Not a slide deck — a playbook your team can execute from.
Can we start with a small pilot before committing?
Absolutely. I recommend starting with a single team or a specific workflow (e.g., code review, testing, documentation). Prove the model works in a contained scope, then expand. The discovery call is free and carries no commitment.
Find out if your team is ready for a designed AI operating model.
A 30-minute readiness assessment covering your current AI usage, workflow gaps, and the specific next steps to move from ad-hoc to designed.
You will leave with a clear picture of where you are and what to do next.
Request an AI Readiness AssessmentFree 30-minute assessment. Start with a pilot. Scale from there.