AI Enablement & Transformation

Turning AI capability into usable systems.

I help organizations define where AI can contribute, design the workflows and governance around it, and equip people to use it with judgment. My work connects product development, human-centered evaluation, and organizational adoption.

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AAgentic AI product development · 2026

Designing an AI research coach with clear human boundaries

Agentic Educational Research Coach (AERC). Developed through Product Faculty’s AI Product Management certificate work. Certificate earned July 2026.

Context: Faculty beginning educational research often need help turning a teaching observation into a focused research opportunity. AERC supports that early assessment and prepares a structured brief for a human mentor.
My contribution: I designed and built a working agentic AI prototype, serving as product lead, agent architect, evaluator, and prototype developer. The work included product requirements, workflow architecture, prompt and context design, governance controls, and behavioral evaluation.
Product and governance decisions: Ground recommendations in a supplied synthetic evidence library, evaluation rubric, and decision criteria.
Produce a reviewable brief with a recommendation, rationale, supporting references, uncertainty, and mentor discussion points.
Define when the agent should recommend proceeding, revising, or pausing—and when it should refuse or escalate a request.
Keep consequential scholarly, methodological, and ethical decisions with a human mentor.
End the workflow before study design or research approval.
Evaluation and status: The working prototype passed behavioral testing using synthetic scenarios, including checks on evidence use, uncertainty, and human decision boundaries. This demonstrates prototype behavior; real-world workflow benefits have not yet been established.

BA.T. Still University

Connecting AI governance to organizational practice

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Context: An enterprise needs a coherent approach to using AI across differing roles, workflows, and responsibilities.
My contribution: I helped create the university’s AI framework and chair its AI Governance Committee, connecting policy, responsible use, capability building, and adoption.
Leadership focus: Bringing organizational priorities, human needs, and responsible-use considerations into a shared approach to AI.

CHuman–AI decision support · Ongoing project

Evaluating AI-supported instructional decisions

Feedback Fusion

Research question: Can instructional users interpret, validate, and act on AI-generated feedback summaries with appropriate confidence?
My contribution: I led an initial formative phase and designed the research protocol and instruments to examine how users assess AI-generated themes and priorities, check them against source feedback, and decide what action to take.
Evaluation focus: Interpretability, calibrated trust, source validation, decision confidence, instructional actionability, and barriers such as over-reliance or validation fatigue.
Status: Initial phase conducted; analysis pending. Additional phases planned. No evaluation findings reported yet.

Supporting experience · Organizational adoption

Helping people expand how they use technology

At A.T. Still University, my enterprise learning and enablement work included helping faculty use Canvas beyond the gradebook. The share of courses using additional features increased from approximately 11% to 59% between Fall 2021 and August 2026.

Tracked features included pages, assignment submissions, assignments, discussions, announcements, and modules.

From Platform Availability to Platform Adoption

How I turn AI opportunities into workable systems

Diagnose readiness

Map workflow friction, stakeholder needs, and organizational readiness. Use maturity models and workflow audits to identify where AI could contribute.

Define the operating model

Specify the agent’s work, evidence needs, human decision rights, review points, and escalation paths.

Evaluate and enable

Test behavior, examine how people interpret and use outputs, and design playbooks, learning support, and adoption measures for continued improvement.

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