AI Advisory
Let's find the high-leverage shift that changes your outcomes. I conduct a structured flow assessment of your product and engineering systems. I help you visualize your value stream, trace queue backlogs, align coding output with upstream discovery, and set up the review gates needed to sustain delivery flow.
The Reset
The AI Product Development Lifecycle Advisory is a targeted, flow-focused engagement designed to solve the friction where product management meets AI-assisted coding.
Instead of selling generic engineering tools or abstract process checklists, I work directly with your technology and product leadership to map your actual value stream. We look at the interfaces where requirements are defined, code is generated, and pull requests are reviewed.
You emerge with a clear, metric-driven operating rhythm that subordinates local code generation to the actual constraints of your delivery pipeline, eliminating whiplash and building sustainable flow.
Why It's Needed
AI changes the end-to-end flow of work. When your developers write code 40% faster or your content creators generate drafts in seconds, the bottleneck in your system immediately moves.
If your operating model cannot handle this shift, the bottleneck moves upstream to product discovery (creating a backlog of vague, half-baked specifications) or downstream to code reviews (creating massive PR queues and rubber-stamped approvals that compromise quality and security).
This is what I call acceleration whiplash: you are generating more code, more artifacts, and more meetings, but seeing zero business impact. We reset your operating system so the entire organization can absorb and steer the new speed.
Is This For You?
This advisory track is specifically designed for VP of Engineering, CTO, and Product executives who have deployed AI assistants (like GitHub Copilot or Cursor) and are noticing that local speed gains have failed to move overall delivery metrics.
The time is right for this reset when:
The Resistance Risk
The biggest risk in AI tool adoption isn't the model; it's the resistance of the people who actually do the work. When leadership mandates tool rollouts from the top down, teams push back. They check the compliance box, use the tool to generate some noise, but their core workflow remains unchanged.
I help you treat AI adoption as a **product challenge, not a project rollout**. If your teams are resisting a new AI tool or workflow, it is because the "product" (the new workflow) has not solved their actual, daily operational friction.
We build adoption through **invitation rather than decree**. We identify your natural early adopters, find where their workflows are stuck, and invite them to co-design the AI-enabled changes. We treat their friction as telemetry to improve the workflow. When the rest of the organization sees how much easier it is to get work done, they naturally pull for the new operating model.
What to Expect
We NDA and onboard. We integrate lightweight tracking to measure your actual lead time, review latency, and spec starvation rates. We run developer surveys to locate their biggest daily friction points.
We run an interactive workshop with product, tech leads, and QA to map your end-to-end technology value stream. We identify where AI has shifted the constraint and locate the true bottleneck.
We co-design targeted workflow changes with your teams: implementing small-batch reviews, defining upstream spec-readiness gates, and setting up automated pre-review checks to restore developer flow.
Your teams roll out the new flow mechanisms. I provide hands-on coaching, steer-coaching for tech leads, and track queue telemetry for up to three months to ensure velocity translates to actual value.
Sound Familiar?
Coding has become incredibly cheap and fast. But product managers cannot write clear requirements at the same speed. The result: developers use AI to instantly code vague, unverified specifications, leading to waste and rework.
Teams write code 40% faster, but the code review queue is stuck. Pull requests back up, review latency climbs, and developers start rubber-stamping approvals. Velocity collapses under technical debt.
Your engineers report that they are coding faster, but your actual business throughput, time-to-market, and GTM cycles remain completely flat. The bottleneck moved, and you are just generating more noise.
You bought expensive copilot licenses, but adoption is patchy. Developers use it as an autocomplete but resist changes to their core workflows because they feel top-down mandates ignore their actual day-to-day pain.
A Different Operating Model
They're managing the engineering lifecycle differently. After 20 years working with organizations on product operating models and developer flow — and seeing the same patterns now playing out with AI — here's what separates the winners.
They do not look at developer keyboard metrics or local velocity. They track the actual cycle time from customer request to production deployment, showing exactly where the work is waiting.
They measure lead time in product discovery and spec quality. They ensure coding capacity does not outpace the product team's ability to define clear, small-batch experiments.
They replace massive, multi-day pull requests with micro-PRs and continuous integration. They prioritize code review speed as a team outcome, rather than treating it as an individual distraction.
They use static analysis, automated testing, and LLM-assisted pre-reviews to catch low-level issues before a human ever looks at the pull request, keeping senior developers focused on architecture and design.
If the bottleneck is testing, deployment, or product discovery, they deliberately slow down code generation. Generating code faster than the system can deploy it only creates inventory and defects.
Instead of pushing tools, they invite team representatives to map their own bottlenecks and co-design the gates. When developers see how a move reduces their daily frustration, they naturally pull for it.
Questions I Get
Next Conversation
Bring your current engineering situation: what tools you've deployed, where your queues are backing up, and what still feels stuck. No pitch, no preset framework. I'll share what I see and you'll leave with a clearer picture of where the real flow leverage is.