E.K. JORDAN
AI Experience Architect · Conversational AI Strategy

Designing how AI earns trust.

I turn complex services, knowledge, and operational requirements into AI experiences people can understand and use—from agent behavior and prompt systems to recovery, governance, analytics, and adoption.

Explore selected workEJEPsAS@gmail.com
7+ yearsConversational AI and AI experience
Public + enterpriseGovernment, Microsoft, Airbnb, Fox
End-to-endDiscovery through launch and iteration
Human-centeredTrust, accessibility, recovery, control
Selected work

Three problems. One systems mindset.

These cases show how I approach public-facing agents, enterprise support experiences, and operational enablement. Sensitive implementation details are intentionally generalized.

01

Phoebe: A public-facing AI assistant for the City of Phoenix

Designing a citywide service experience around resident needs—not government structure.

Copilot StudioAgent architectureGovernance

Design response

I reframed city services as human goals, then created governed interaction patterns that could scale across departments. Ambiguous requests triggered focused clarification; low-confidence or unsupported requests moved into designed recovery rather than dead ends.

Resident languageStart with the problem as the person describes it.
Interpret + clarifyDetermine intent and ask only what is needed.
Route + respondUse approved knowledge and service logic.
Recover + learnRedirect, escalate, and improve from evidence.
Product ownershipFrom discovery and architecture through testing and post-launch improvement.
Trust by designConfidence-aware behavior, guardrails, accessibility, and human handoff.
Visible outcomePhoebe was elevated to the Phoenix.gov homepage as a flagship digital service.
02

Enterprise support: Designing repeatable conversation systems

Making complex support interactions clear, recoverable, measurable, and platform-aware.

MicrosoftWashington DOLAnalytics

Design response

I created reusable conversation patterns instead of treating every response as a one-off. Transcript review and conversational diagnostics surfaced friction, inconsistent responses, failed intents, and weak handoffs; those signals drove redesign and testing.

ObserveReview language, transcripts, failures, and drop-off.
DiagnoseSeparate intent, content, routing, and platform issues.
RedesignRefine dialogue, prompts, knowledge, and recovery.
EvaluateTest successful paths, edge cases, and failure behavior.
Platform fluencyCopilot Studio, QnA Maker, dialogue logic, APIs, and routing systems.
Quality systemsReusable frameworks for tone, accuracy, recoverability, and consistency.
Cross-functional deliveryProduct, engineering, analytics, UX, and operations partnership.
03

UltraCare: Turning workflow chaos into an operating system

Building the structured knowledge, controls, and guidance required for consistent execution and future AI enablement.

Workflow designEnablementKnowledge systems

Design response

I treated the internal operation as a product experience: identify the next decision, put the guardrail where it is needed, make completion observable, and reduce dependence on memory or tribal knowledge.

MapExpose steps, owners, systems, decisions, and gaps.
StandardizeDefine expected actions and verification criteria.
OperationalizeCreate forms, queues, checklists, and escalation paths.
EnableTurn the system into teachable, reusable knowledge.
Program thinkingConnected procedures, tools, roles, controls, and adoption.
UsabilityDesigned guidance around the moment of action and the next required step.
AI readinessMade expected behavior explicit enough to support automation and reliable retrieval.
How I work

From ambiguity to a shippable experience.

I connect human needs, product behavior, technical reality, and operational adoption so the experience holds together beyond the demo.

01

Discover

Understand the user, desired outcome, workflow, constraints, risk, and failure points.

02

Architect

Define journeys, intents, states, knowledge, decisions, clarification, recovery, and escalation.

03

Evaluate

Test expected paths, ambiguity, edge cases, unsupported requests, and handoffs.

04

Operationalize

Ship with standards, documentation, governance, analytics, and reusable patterns.

Capabilities

Strategy with production depth.

I work across the experience layer and the systems that make it reliable, measurable, and maintainable.

AI experience architecture

Journeys, agent behavior, state, context, task flow, and interaction models.

Conversation design

Intent, utterances, dialogue, clarification, recovery, escalation, tone, and accessibility.

Prompt and grounding systems

Prompt structure, response standards, approved knowledge, guardrails, and consistency.

Evaluation and analytics

Transcript audits, failed-intent analysis, edge cases, UAT, drop-off, and quality review.

AI governance

Responsible behavior, limits, uncertainty, human oversight, documentation, and reuse.

Enablement and adoption

Workflow standards, training, operational guidance, stakeholder alignment, and change support.

AI changes what a product can do. It does not change what people need: clarity, control, recovery, and a useful next step.

Contact

Let’s make complex AI usable.

Available for senior individual-contributor and manager-level opportunities in conversational AI, AI experience, implementation, enablement, and digital product strategy.

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