DCDorian Coleman, EdDUX Researcher & AI Strategist

AI Strategy · User Research · Complex Systems

Interested in solving complex problems? 
So am I.

Hi, I'm Dorian. I help organizations inform and implement AI strategies though user research. I do this by uncovering how people actually work with AI-enabled systems, translate those insights into product and system strategy, and build the evaluation frameworks that determine whether the resulting experience is useful, trustworthy, and operationally effective.

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Portrait of Dorian Coleman, researcher and AI strategist
AI strategyAI evaluationAgent workflowsHuman-in-the-loop systemsSystems researchProduct strategy

Selected work

Research case studies

Deep research programs where the evidence resolved a genuine strategic question.

AI systems & builds

Agents, research automation, evaluation harnesses, prototypes, and workflow experiments.

Strategy & frameworks

Adoption frameworks, governance thinking, evaluation approaches, and system models.

Printed workflow diagram with fine black lines and connected nodes on off-white paper

Research Operations · AI Strategy · Agent Design · Human-AI Interaction

Insights have a shelf life

An employee-facing system of an orchestrator root agent and purpose-built subagents, with humans in the loop, applied across nearly every stage of the research lifecycle. The goal was research velocity, not novelty: move the throughput to the system and keep the judgment with the researcher.

Role
Research and AI strategy lead — agent architecture, orchestration design, evaluation
Impact
$8,000 less per year in tooling cost; AI applied at nearly every lifecycle stage
View case study
Laptop displaying a grayscale data dashboard on a light warm-gray desk

Foundational Research · AI Strategy · Product Strategy · Human-AI Interaction

Finding where AI belongs in the service portal

The instruction came down as one word: agentify. No problem attached, no user in the sentence. Pioneer research turned an open directive into a prioritized, evidence-backed portfolio of use cases — and, just as usefully, a map of the places users wanted left alone. When money or access is on the line, nobody wants a conversation.

Role
Research lead — foundational research, opportunity mapping, use case prioritization
Impact
11 use cases identified; 3 already moving into build
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Three stacks of research transcripts and surveys arranged around a hand-drawn synthesis diagram showing three perspectives converging into five shared theme clusters

Enterprise Research · Research Strategy · Mixed Methods · Product Strategy

Reading across the studies, not within them

Three populations, four instruments, six months of collection. Each group arrived with what sounded like its own set of complaints. Grouped thematically rather than counted by channel, they converged: one system failing in the same places, experienced from three different seats.

Role
Research lead — mixed-method synthesis, thematic mapping, roadmap alignment
Impact
Top five pain points mapped to roadmap initiatives and used in quarterly planning
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AI problems are rarely just AI problems.

The quality of an intelligent system depends on more than the model. I look at the surrounding ecosystem—users, workflows, information architecture, incentives, data, interfaces, organizational constraints, and human decision points—to understand what actually determines whether an AI experience succeeds.

Research

Understand people, behaviour, needs, context, and the points where the work actually fails.

Systems

Map dependencies, workflows, tools, ownership, data, and the organizational constraints around them.

AI

Evaluate where intelligent systems create value, where they introduce risk, and how humans should interact with them.

Strategy

Translate evidence into priorities, product direction, experience principles, and decisions.

AI strategy

How I approach AI

Where research, evaluation, and product judgement meet the question of what intelligence should actually do inside a workflow.

  • AI opportunity identificationFinding where intelligence meaningfully improves an outcome, and where it only adds surface.
  • Human-AI interaction researchHow people supervise, verify, correct, and abandon intelligent systems.
  • AI experience strategyPrinciples for how a product should behave when it is uncertain.
  • Evaluation and test-set developmentScenario sets and measures that make quality arguable with evidence.
  • Agent and workflow evaluationAssessing multi-step systems against the work they are supposed to carry.
  • Human-in-the-loop designPlacing judgement where it changes the outcome, not where it merely signs off.

About

I've spent a decade inside products that are difficult on purpose: enterprise platforms, learning systems, and now the workflows forming around intelligent agents. The common thread is that the complexity is usually legitimate—what fails is how it is expressed, and who is left accountable for it.

My work sits between research and strategy. I map systems, run the studies that resolve genuine uncertainty, evaluate how intelligent systems behave in real work, and stay close enough to the roadmap that the evidence changes what ships.

Increasingly that means helping organizations decide where AI belongs at all: which problems it improves, which it obscures, and how people should be positioned to supervise it.

More about me
Dorian Coleman reviewing a service blueprint on a whiteboard

Interested in complex problems? So am I.

dncole01@gmail.comLinkedInDownload CV