Start with one recurring workflow
Pick work that happens often enough to matter: status synthesis, evidence gathering, intake triage, or reporting. Define sources, instructions, output, review, and ownership before scaling.
AI adoption · Practical AI adoption
Practical AI
adoption consulting.
For organizations where AI interest is high but useful adoption, clear guardrails, and repeatable workflows are not yet.
Who it is for
Teams where important work spans more than one group, tool, or plan.
- Leaders who need AI to produce operational value, not pilot theater
- Teams experimenting in chat tools without an approved path for recurring work
- Organizations that need governance that makes useful work easier, not slower
Situations I enter
When the plan and the organization are out of sync.
- 01Experiments stay personal; nothing becomes a team workflow
- 02Policy exists but nobody knows how to do useful work on Tuesday afternoon
- 03Output looks polished but traceability and review are unclear
- 04Leaders cannot tell which use cases are safe, repeatable, and worth scaling
What I do
Hands-on leadership, clear scope.
Make the safe path obvious
Approved tools, data boundaries, named reviewers, and examples that resemble real work. Governance should reduce uncertainty, not add another approval layer people route around.
Build toward durable adoption
Train on workflows, not interfaces. Document what works, what fails, and where human judgment stays mandatory. Adoption follows usefulness when the organization can repeat the win.
What changes
Results from recent delivery work.
Drawn from my résumé and field experience. Full employment history ↗
Repeatable AI-supported workflows
At Capstone, built AI-supported workflows for R&D tax-credit analysis, PI kickoff briefs, Jira/Rovo reporting, and executive updates, with clear sources, review steps, and owners.
Governance that enables work
Practical guardrails tied to specific workflows: what data is used, where it goes, what the model may do, and who signs off before action.
Less manual synthesis
Teams spend less time rebuilding the same story from scattered systems when a workflow can assemble a first draft from known sources and a human reviews the result.
Case studies
How engagement starts
Clear places to begin.
Each engagement has a defined scope, a clear price, and a sample of what you get. Most work starts with a Health Check or single-workflow diagnostic. Fractional leadership follows when the problem needs sustained ownership. See sample deliverables ↗
Most common starting point
Practical AI Workflow Audit
A practical assessment of one recurring workflow to determine where AI can help, what must stay human, and what a realistic future-state workflow looks like.
Project Health Check
When AI adoption is tied to a specific initiative, start with an independent read on delivery health before expanding scope.
Sample deliverableIllustrative health assessment format. ↗Fractional Delivery
When adoption needs operating-model change across teams, not just tool training. Same fractional model: senior leadership, defined term, no account-team markup.
Sample deliverableIllustrative 90-day operating plan. ↗From $5,000/mo
Up to 10 hrs/week · 90-day term
Enough senior ownership to move a multi-team program forward. Not a retainer for slide decks.
Start this engagement ↗Related reading
Field notes on this work.
AI adoptionAI governance should make useful work easier↗Delivery systemsMost transformation problems are context problems↗
View all field notes →Ready to start?
Let's talk about practical ai adoption.
Start with a Practical AI Workflow Audit if you want a defined first step, or email me with what is stuck and we will figure out the most useful path.