AI systems, adoption & learning

Making AI useful in everyday work.

I design and operate AI-supported workflows, develop people's ability to use them, and help organizations decide where to invest.

This work brings together organizational effectiveness, learning, and practical system design. I focus on useful tasks, reliable sources, clear responsibilities, and the judgment people need to bring to the work.

See the work

Systems I design and operate

Connecting information with useful work.

Organizational knowledge

Turning project experience into shared insight

I created and run the Client Insight Framework and its recurring forum. The process connects conversations with project teams, AI-supported synthesis, human review, and action tracking so knowledge can inform decisions across an organization.

Context carries forward between sessions, and operating instructions make the process explicit. Through use, I have refined how the workflow records responsibilities and makes follow-up visible. People conduct the conversations, validate the interpretation, and decide what to act on.

Evidence and assessment

Making claims traceable

I built and used an evidence-assessment prototype that connects source records, structured information, retrieval, and a scoring rubric. It preserves where information came from and limits scores when supporting evidence is missing.

I have used the prototype on real requests to identify relevant evidence and make missing support visible. Source validation and review are part of the workflow.

Coaching and review

Giving feedback a clear standard

I built and used a rubric-based coaching workflow as a proof of concept. It combines AI-supported feedback, self-review, and final human scoring, with explicit rules for leaving untested criteria unscored.

The design makes the basis and limits of feedback visible so a person can judge what to use and what needs more evidence.

People and teams I develop

Building capability through learning and practice.

Completed learning programs

Staff fluency and deeper technical practice

My team authored and delivered an introductory AI learning series covering assistant selection, prompting, research and writing workflows, learning support, and privacy. We used commercially available tools so staff could apply the skills in everyday work.

I championed the program, developed and directed the team, protected time for delivery, and contributed readiness-learning design and facilitation.

I also led the AI Accelerator's program model, roadmap, and workshop design. Delivered with a technical partner, the completed bootcamp offered deeper, project-based practice. The two programs served different learning needs.

Shared practice

Making the work understandable

In the community of practice I lead, I demonstrated my AI approaches, then facilitated a structured conversation about how the team was using AI and what people were learning.

We aligned on a specific integration to develop further and scale across the team. This connects individual experimentation with shared learning and a concrete direction for team practice.

Decisions that support adoption

Choose the approach. Set the standards. Learn from use.

Solution design

Making guidance easier to use

For a federal client, I designed an AI-supported approach to learning and using a service design procedure. The design treats the authoritative procedure as the source and works through the client's approved AI tools.

The client adopted the approach and approved exploring opportunities to scale it, building on its existing adoption roadmap.

Evaluation and judgment

Use evidence to guide the next investment

I have used participant feedback and pilot assessments to evaluate learning programs and guide further investment. That means examining the learning need, the experience, the conditions for delivery, and what the evidence can actually support.

The same discipline informs my workflows: preserve sources, define review responsibilities, flag missing evidence, and revise the process when use exposes a weakness.

ADAPT framework

A practical framework for adoption.

I developed ADAPT to connect opportunity assessment, governance, workflow design, learning, and evaluation. I apply its components internally where they are useful and where time and access allow.

The five phases describe a full adoption cycle. They can also be used as modules, with clear outputs and responsibilities. A documented workflow or learning plan can be useful before the full cycle is complete; broader adoption needs its own evidence.

Leadership support, communication through change, and equitable access to tools and learning run throughout.

Assess
Map demanding, recurring tasks and listen to staff. Prioritize opportunities by usefulness and feasibility.
Define
Set data boundaries, approve tools, establish quality standards, and agree on how to judge progress.
Architect
Document the workflow, including human review. Create prompts, templates, and a learning plan that help people use it.
Pilot
Test with teams doing real work. Compare results with the agreed measures and refine the approach around what happens.
Transform
Build effective practices into procedures, onboarding, and project routines. Keep a forum for sharing lessons and improving the work.

Working on AI adoption or team capability?

I'm interested in helping government and nonprofit teams connect useful systems, staff development, and sound judgment.

Get in touch