AI Strategy · User Research · Complex Systems

I help organizations decide where AI creates value—and where it doesn’t.

Hi, I’m Dorian. I use research to help teams identify where AI belongs, design how people and intelligent systems should work together, and evaluate whether those systems are useful, trustworthy, and effective.

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Portrait of Dorian Coleman, researcher and AI strategist

Impact & scope

11: 4 advanced toward roadmap and implementation.
~2 weeks → ~1 day: Through AI-assisted orchestration.
$8K annually: By replacing a licensed repository with an AI-enabled approach.
AI governance: Strengthening internal AI applications as they move through governance.
150+: Across team showcases, AI tool demonstrations, and enterprise audiences.
Enterprise-wide: Employee, operational, and customer-facing systems.

Selected work

Research Operations · AI Strategy · Agent Design

Agent-assisted research operations

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: move the throughput to the system and keep the judgment with the researcher.

Impact: $8,000 less per year in tooling cost; AI applied to compress early lifecycle stages

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Enterprise Research · Research Strategy · Mixed Methods · Product Strategy

Enterprise-wide experience gaps

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.

Impact: Top five pain points mapped to roadmap initiatives and used in quarterly planning for prioritization

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Foundational Research · AI Strategy · Product Strategy

Finding where AI belongs — and where it doesn't

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.

Impact: 11 use cases identified; 4 already moving into build

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How I create value

I work across the AI lifecycle—from finding the right opportunity to designing the system around it and determining whether it actually works.

Identify opportunities

Find where AI creates meaningful value.

I use research, workflow analysis, and systems thinking to identify where AI can improve an outcome, where human effort is disproportionate, and where automation would introduce unnecessary risk or complexity.

Opportunity discovery
Workflow and pain-point analysis
AI suitability and sensitivity
Prioritization and roadmap input

Design human-AI workflows

Turn opportunities into usable systems.

I design how people, intelligent agents, data, tools, and decision points should work together—keeping human judgment where it materially improves the outcome.

Agent and workflow design
AI orchestration
Human-in-the-loop systems
Experience strategy
Guardrails and recoverability

Evaluate what works

Make AI quality measurable.

I build evaluation approaches that help teams determine whether intelligent systems are useful, trustworthy, grounded, consistent, and operationally effective.

Evaluation test sets
LLM and agent evaluation
Human review and cross-validation
Guardrail testing
Quality criteria and iteration

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.