Overview
AI adoption consulting helps a product team move from experimenting with AI to using it in daily work. The job is not buying tools. It is closing the gap between what AI can do and what your team actually ships: the workflows, the skills, the guardrails, and the operating model that make adoption stick.
Most teams already have AI access. What they're missing is a clear answer to where it earns its keep, how to use it without creating risk, and how to make the habit outlast the initial excitement. That's the work I do.
Who this is for
This works best when you have:
- A team that has tried AI tools but can't point to work that's measurably better or faster
- A pilot that impressed everyone in the demo and then quietly stalled
- Leadership asking for an "AI strategy" without a clear use case underneath it
- Real constraints (regulated data, security review, brand risk) that generic AI advice ignores
If you want a single workflow built and shipped fast rather than an adoption program, a Sprint Engagement is the better fit.
What you get
In week one, I assess where you actually are: which workflows are good AI candidates, where your team's comfort and skills sit, and what's blocking adoption. You get a written AI readiness assessment, not a maturity-model slide.
Next, I turn that into a prioritized roadmap. We pick the few use cases where AI clearly pays off, define the guardrails (what data goes where, what gets human review), and build the workflows and prompts your team will use day to day.
Before I leave, I document the operating model: how you evaluate AI output, how you onboard the next person, and how you decide what to automate next. The goal is a team that keeps adopting without me.
How we work together
The engagement is scoped to what you need, from a one-off AI readiness assessment to an embedded adoption program that runs until the habit sticks. We start by agreeing on the few outcomes that matter, then size the work to reach them.
- Scope: A one-time readiness assessment, a focused roadmap, or an embedded program
- Commitment: Sized to the outcomes, not a fixed retainer
- Format: Embedded with your team (remote, with on-site as needed)
Frequently asked questions
The skills behind this
Agent skills I run during this engagement
EU AI Act Readiness
Assess an AI system against the EU AI Act and produce a readiness and remediation plan.
Responsible AI Policy
Draft an internal responsible-AI policy: principles to concrete do/don't, human oversight, transparency, fairness, and a named enforcement owner.
AI Risk Register
Build and maintain a living AI risk register across accuracy, bias, security, privacy, cost, and compliance.
AI Vendor Risk Assessment
Evaluate an AI vendor or model provider before buying: data handling, lock-in, pricing risk, and compliance fit.
AI Guardrails Design
Design layered defenses for an AI feature: input validation, output filtering, jailbreak and abuse detection, calibrated against false-block cost.
AI Health Check
Run a structural health check of an AI system. Use when an AI feature is live or near launch and you need to find risk, quality, and alignment gaps before they bite.
Agent Reliability Audit
Audit a live AI agent for reliability: termination caps, runaway cost, tool-error recovery, context bloat, and gating on irreversible actions.
Exec AI Literacy Program
Design an AI literacy program for leadership: capabilities, limits, risk, governance, and ROI judgment. Use when execs need to make AI decisions, not prompt.
Try it yourself
Free tools related to this service
Related writing
Posts that go deeper on this topic
EU AI Act Readiness Assessment: What to Check Now That the Deadline Moved
An EU AI Act readiness assessment inventories every AI system you build or use, classifies each against the Act's risk tiers, and lists the gaps. The high-risk deadline moved to December 2027, but prohibitions, AI literacy, GPAI and transparency rules already apply today.
Consentful Software: Designing for FRIES
We know consent has to be freely given, reversible, informed, enthusiastic, and specific when it's about our bodies. Our data is a body too. Here's what happens when you run your product's data handling through the same five tests, and why a cross-tenant leak is a consent failure, not just a security bug.
Eval-Driven Development for AI Products
Eval-driven development means writing the eval alongside the AI feature and using its score to guide every change. Here's the loop, and how to grade LLM output you can trust.
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