AI Strategy & Adoption

Move your engineering team from experimenting with AI tools to fully AI-native. Strategy, tooling, and hands-on coaching that changes how your team actually works.

95% of generative AI pilots fail (MIT/Fortune, 2025). Not because the models are bad — because organizations adopt AI without assessing whether their codebase, team, or processes can support it. Worse: DORA 2024 found system-level throughput and stability decrease with AI adoption, despite 75.9% of developers reporting productivity gains. The gap between perception and reality is the trap. The teams winning aren't the ones with the best tools — they're the ones who changed how they work.

Who This Is For

• Engineering leaders whose teams are experimenting but lack strategy or coherence
• CTOs getting pressure from the board on AI and need a credible answer
• PE portfolio companies driving efficiency gains across acquired software teams
• Companies watching competitors ship faster and wondering what they're missing

Service Areas

Workflow Assessment

Evaluate where AI helps your specific workflows and where it doesn't. Prioritized by impact, sequenced for adoption.

Tooling Evaluation

Code assistants, testing, documentation, code review, infrastructure automation. Evaluated against your stack, team size, and security requirements.

Context Engineering

The discipline of curating what AI tools see to get better results. Project rules files, coding standards, MCP integrations, and codebase organization that's AI-friendly. The difference between AI that generates plausible code and AI that generates correct code.

Team Coaching

Embedded with your engineers, in their codebase, on their problems. Building AI-assisted workflows that become habit, not a lunch-and-learn.

Leadership Advisory

Sounding board for CTOs on AI investment decisions and board communication. Often delivered as part of a fractional CTO engagement.

Approach

Bottom-up adoption over top-down mandates. Start with engineers already experimenting, give them better tools and structure, let results spread, then formalize what works. Communities of practice over company-wide rollouts.

When AI tools spread beyond the engineering team — to product managers, designers, and others pushing code — adoption becomes a builder experience problem, not just a developer tooling problem.

Beyond the Engineering Team

AI adoption doesn't stay inside engineering for long. Product managers, designers, and other roles are already using agents to push code. The question shifts from "how do we adopt AI tools" to "what happens to the system when everyone can ship."

This is where the engineering role changes. You're no longer gatekeeping who can build — you're designing the platform that makes it safe and productive for anyone to build within their domain. That means rethinking review processes, deployment guardrails, and how you maintain codebase coherence when PR volume goes up 10x.

• Builder experience design: agent configurations, tool scoping, and constraints that keep non-engineers productive without requiring a deep technical mental model
• Review and deploy pipeline: automated checks that replace human review bottlenecks as volume scales
• System resilience: monitoring, alerting, and feedback loops that catch problems in production rather than slowing everyone down at the PR stage

Frequently Asked Questions

How do you get an engineering team to actually adopt AI tools?

Bottom-up, not top-down. Start with engineers already experimenting, give them better tools and structure, let results spread through the team. Communities of practice over company-wide mandates.

Which AI coding tools should my team use?

It depends on your stack, team size, and security requirements. The engagement includes a tooling evaluation covering code assistants, testing, documentation, code review, and infrastructure automation, matched to your specific context.

What's the difference between AI strategy and actually using AI?

Most AI strategies are slide decks. This engagement is hands-on: embedded with your engineers, in their codebase, building AI-assisted workflows that become habit.

How do you measure if AI adoption is actually working?

System-level metrics, not developer sentiment. DORA 2024 showed a perception-reality gap where developers feel more productive but system throughput doesn't improve. I measure deployment frequency, cycle time, change failure rate, and planned work ratio alongside adoption surveys — if sentiment improves but delivery metrics don't, I dig deeper.

What happens when non-engineers start using AI to push code?

This is already happening. The answer isn't to stop them — it's to design the system so they can build safely within their domain. That means agent tooling scoped to safe zones, automated review pipelines, and system-level guardrails. The engineering team shifts from gatekeeping to platform-building.