Pillar 4: AI Experience Design

Overview

Pillar 4 answers the question: How will people actually use this?

This pillar designs the interaction models, interfaces, and workflows that determine whether AI actually gets adopted into daily work—through genuine improvement, not mandates.

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Why Pillar 4 Matters

The Problem We're Solving

Most AI projects fail not because the technology does not work, but because people do not use it.

Organizations have seen this pattern repeatedly: workflow automation tools that sit unused, collaboration platforms that employees route around, analytics dashboards that no one checks. The technology worked; the experience did not.

Common adoption failures:

• AI tools feel foreign to existing workflows
• Users don't trust AI recommendations
• Interaction requires too much effort
• Value is not visible to users
• Senior experts feel threatened, not empowered

What Success Looks Like

By the end of Pillar 4, the client organization has:

• Detailed interaction designs for each prioritized agent
• Trust and transparency specifications built into the architecture
• Integration requirements mapped to existing systems
• Adoption metrics defined and baseline-ready
• User-validated designs that pass "the Experience Test"

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Leveraging Superintelligent Insights

The Superintelligent survey from Pillar 1 captured rich data about how employees actually work—not how they are supposed to work, but how they really operate day-to-day. This data becomes the foundation for experience design.

What Superintelligent Reveals:

Insight Type	What It Shows	Design Implication
Work Rhythm	When and how people work (bursts, disconnected, etc.)	Design for actual patterns, not ideal patterns
Context Switching	How often people jump between tasks	AI should bring context, not require users to find it
Expert Concerns	What senior practitioners worry about	Position AI as amplifier, not replacement
Informal Workarounds	Personal systems employees have built	Integrate with existing patterns, don't disrupt them

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The OAIO Experience Design Methodology

Pillar 4 delivers through agent-specific design sessions—one session per agent, each bringing together the right stakeholders for that specific use case.

Session Participants

Unlike governance sessions (which included Legal and Compliance), experience design sessions bring together:

Participant	Role	Why They're Essential
End Users	Field workers, reviewers, analysts	Know the daily reality of the work
Workflow Owners	Process owners	Understand how work actually flows
Senior Practitioners	Veterans who've "seen everything"	Know edge cases and institutional context
UX Facilitator	Orion experience designer	Translates insights into design

Session Structure (3-4 hours per agent)

Each session follows a consistent three-part structure:

Part 1: Current State Mapping (60-90 minutes)

Map the current workflow in detail:

• Where does work originate?
• What triggers human action?
• Where are the friction points?
• What happens when something goes wrong?

Part 2: Interaction Model Design (60-90 minutes)

Design how human-agent interaction will work:

Element	Question to Answer
Entry Point	How does work reach the agent?
Handoff Protocol	How does the agent communicate what it has done?
Uncertainty Handling	How does the agent surface doubt or ambiguity?
Escalation Trigger	When does the agent ask for human help?
Exit Point	How does the human confirm completion?

Part 3: Transparency and Trust Design (45-60 minutes)

Define what the agent shows users:

Element	Purpose	Example
Confidence Indicators	Visual signals of certainty	"High confidence" badge, percentage scores
Reasoning Traces	Explain why agent made recommendations	"Flagged because: [specific rule]"
Source Attribution	Links to data agent relied on	Direct links to source documents
Correction Mechanisms	Easy ways to fix agent mistakes	One-click "dismiss" or "modify"

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Agent Interaction Model Templates

Pattern 1: Pre-Processing Agent (QA Validation)

The Challenge: Human reviewers spend hours on mechanical checks before applying judgment.

Stage	Human Action	Agent Action
Entry	Upload item to queue	Automatic validation begins
Processing	Continue other work	Check against validation rules
Handoff	Receive notification	Present validation summary with issues flagged
Review	Examine flagged items	Provide context for each flag (source, rule, confidence)
Resolution	Accept, modify, or dismiss flags	Learn from corrections
Exit	Mark validation complete	Log decision for audit

Trust Design: The agent never says "this is good." Instead: "I checked 47 items. Here are 3 that need your attention."

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Pattern 2: Normalization Agent (Field Note Processing)

The Challenge: Inconsistent inputs (terminology, abbreviations, formats) require tedious standardization.

Stage	Human Action	Agent Action
Entry	Submit raw content (text, photos)	Parse and interpret content
Processing	See real-time progress indicator	Normalize terminology, structure data
Handoff	Review normalized output	Present side-by-side comparison
Review	Accept or edit suggestions	Highlight changes for easy scanning
Exit	Confirm final version	Store normalized version with audit trail

Trust Design: Show every change, color-coded:

• Blue = Terminology normalization
• Yellow = Inferred data (with confidence score)
• Normal = Unchanged content

One-click options: Accept All, Review Individually, Revert to Original.

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Pattern 3: Routing Agent (Exception Handler)

The Challenge: Experts waste time on routine cases while genuinely complex cases sometimes get inadequate attention.

Stage	Human Action	Agent Action
Entry	None (automatic monitoring)	Continuously analyze incoming work
Detection	Receive priority alert	Identify exception based on risk patterns
Context	Review exception summary	Provide case context, similar precedents, risk factors
Decision	Assign routing or handling	Learn from routing decisions
Exit	Confirm action taken	Log decision and outcome

Trust Design: The agent surfaces information, not decisions. Each alert includes:

• Why this was flagged (specific risk indicators)
• Similar past cases (how they were handled)
• Recommended expert (based on expertise matching)
• Urgency assessment (time-sensitive or routine)

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Confidence Design

Confidence indicators require careful calibration. Users initially ignore vague indicators ("high confidence") because they don't know what the threshold means.

Effective Confidence Communication:

Approach	Example	When to Use
Comparative	"Similar to 94% of cases I've seen"	When historical context helps
Range	"Between 75-85% confident"	When precision matters
Categorical	"High / Medium / Low" with definitions	For quick scanning
Contextual	"Lower than usual for this document type"	When deviation matters

Progressive Disclosure: Power users want to move fast; new users want to understand. Solution: Collapsed explanations with "Why?" links.

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Design Validation

Before finalizing designs, Orion conducts lightweight validation with representative users.

Method: Walk-Through Sessions with Prototypes

What We Test:

• Can users understand what the agent did?
• Do they trust the confidence indicators?
• Can they efficiently review and correct?
• Does it feel faster than current approach?

Common Validation Findings:

Finding	Solution
Confidence indicators ignored	Show confidence as comparative range with context
Explanations slow power users	Make explanations optional (collapsed by default)
Mobile experience critical	Mobile-first design for field-facing features
Expert matching sometimes wrong	Easy reassignment with feedback mechanism

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Design Artifacts

Pillar 4 produces detailed specifications for implementation:

Interaction Specifications

Artifact	Content	Audience
Screen-by-Screen Wireframes	Visual designs for each agent interface	Development team
State Diagrams	Workflow transitions and decision points	Development, QA
Error Handling Designs	Edge cases and exception flows	Development team

Trust and Transparency Specifications

Artifact	Content	Audience
Confidence Display Standards	How to show certainty levels	Development, UX
Explanation Templates	Standard formats for agent reasoning	Development, UX
Correction Mechanisms	How users fix mistakes	Development, QA

Integration Requirements

Artifact	Content	Audience
Entry Point Mapping	Where agents connect to existing systems	Architecture, Development
Notification Design	Alerting and messaging specs	Development, UX
Mobile/Offline Requirements	Disconnected operation needs	Development, Architecture

Adoption Metrics

Metric	Definition	Target
Activation	% of users who try the agent	80%+ within 30 days
Engagement	Frequency of agent use	Daily for target users
Satisfaction	User perception of value	NPS > 30
Efficiency	Time savings achieved	50%+ reduction in task time

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Session Agenda

Per-Agent Design Session (3-4 hours):

Time	Focus	Purpose
0:00–0:15	Context Setting	Review agent purpose, Superintelligent insights
0:15–1:15	Current State Mapping	Map workflow, identify friction, document workarounds
1:15–1:30	Break
1:30–2:30	Interaction Model Design	Define entry, handoff, escalation, exit
2:30–3:15	Transparency and Trust Design	Confidence, explanations, corrections
3:15–3:45	Validation Planning	Identify test users, define success criteria

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Design Principles

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Outputs and Handoffs

Pillar 4 Deliverables

Deliverable	Description	Example
Interaction Specifications	Screen flows, state diagrams, wireframes	View Example
Trust Specifications	Confidence, explanation, correction designs	—
Integration Requirements	System touchpoints, API needs, offline requirements	—
Adoption Metrics	KPIs with targets and measurement plan	View Example
Validation Report	User feedback and design refinements	—

Handoff to Pillar 5

Pillar 4 outputs flow directly into Pillar 5 (FinOps):

• Usage projections inform cost forecasting
• Interaction complexity affects token consumption estimates
• Training requirements impact operational cost model
• Adoption targets inform business case validation

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Delivery Timeline

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Common Pitfalls

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Facilitator Guidance

Mission & Charter

The Pillar 4 Design Sessions exist to create AI experiences that earn adoption. These are not UI polish exercises or technology demonstrations. The mission is to:

• Design interaction models that fit how people actually work
• Build transparency and trust into every agent touchpoint
• Create experiences that are genuinely better than current workflows
• Validate designs with real users before development begins

What these sessions are NOT:

• UI mockup reviews divorced from workflow context
• Technology demonstrations to impress stakeholders
• Manager-driven design without actual end users in the room
• Abstract interaction patterns without specific agent grounding

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Session Inputs

Participants should have reviewed:

• Superintelligent survey insights relevant to their agent
• Pillar 3 permission boundaries (what the agent can and cannot do)
• Current workflow documentation (if it exists)

Orion enters with prepared artifacts:

• Superintelligent insights summary for this user population
• Current state mapping templates
• Interaction model canvas templates
• Trust design checklist

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Preparation Checklist

1. Review Superintelligent data — Understand how this user population actually works (rhythms, pain points, workarounds)
2. Study Pillar 3 outputs — Know the permission boundaries and trust requirements that constrain design
3. Recruit the right participants — End users, not managers describing what users do; include skeptics
4. Prepare current state templates — Have workflow mapping tools ready (whiteboard, digital, or paper)
5. Identify senior practitioners — Veterans who can surface edge cases and institutional knowledge
6. Review mobile/offline needs — Understand connectivity constraints for field workers

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Delivery Tips

Opening:

• Start with Superintelligent insight: "Your colleagues told us X about how this work actually happens"
• Frame the goal: "We're designing something you'll actually want to use—not something you're told to use"
• Acknowledge past failed tools—show you understand their skepticism

Managing the Room:

• Draw out the quiet users—they often know the real workflows
• Capture workarounds without judgment—they're design opportunities, not compliance failures
• When experts express concern, validate and design for amplification: "How do we make this help you do more of what you're good at?"
• Watch for managers answering for users—redirect to actual practitioners
• Keep the interaction model grounded in the permission boundaries from Pillar 3

Part Transitions:

• Current State → Interaction Model: "Now that we see how work flows today, let's design where the agent fits"
• Interaction Model → Trust Design: "The interaction is clear. Now let's make sure you can trust what the agent tells you"

Closing:

• Walk through the interaction model end-to-end with a realistic scenario
• Confirm the "Experience Test" answer: Why would someone use this instead of their current approach?
• If the answer isn't compelling, the design isn't ready—iterate before leaving
• Schedule validation session with representative users (different from design participants)

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Output Artifacts Checklist

By session end, confirm you have:

• Current state workflow map with pain points and workarounds identified
• Interaction model for the agent (entry, handoff, escalation, exit)
• Transparency specifications (confidence display, explanations, corrections)
• Integration requirements (where agent connects to existing systems)
• Mobile/offline requirements (if applicable)
• Validation session scheduled with representative users
• Compelling answer to "The Experience Test"

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When Variations Are Required

Session adjustments:

• If users work in disconnected environments: prioritize mobile-first, offline-capable design
• If senior experts are highly skeptical: dedicate extra time to "amplification not replacement" framing
• If workflows vary significantly by region/team: may need multiple sessions with different user groups

Validation considerations:

• Always validate with users different from those who designed (avoid confirmation bias)
• For high-stakes agents: consider clickable prototypes, not just walk-throughs
• If validation reveals fundamental issues: pause for redesign rather than force-fitting

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Pricing and Positioning

Scope Options

Scope	Duration	Description
Experience Workshop	1-2 weeks	Design sessions for 2-3 agents with basic validation
Comprehensive Design	3-4 weeks	Full design package with extensive validation
Design + Prototyping	4-6 weeks	Interactive prototypes for user testing

Integration with UX Programs

Pillar 4 designs integrate with existing UX practices:

• Design system alignment
• Accessibility requirements
• Brand guidelines
• Existing interaction patterns

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Required Collateral

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Reference Materials

Related Content

• NorthRidge Case Study: Pillar 4 — Story-based walkthrough
• Pillar 3: AI Protection & Operational Trust — Prerequisites for Pillar 4
• Pillar 5: AI FinOps & Operational Economics — Where Pillar 4 outputs flow

External Resources

Human-AI Interaction:

• Google People + AI Guidebook — Design patterns for human-AI collaboration
• Microsoft HAX Toolkit — Human-AI experience design guidelines
• IBM Design for AI — Enterprise AI design principles

Accessibility:

• WCAG 2.1 Guidelines — Web content accessibility standards
• Inclusive Design at Microsoft — Inclusive design methodology

AI Transparency:

• Anthropic Constitutional AI — Approach to AI alignment and transparency
• Google Model Cards — Documenting AI model capabilities and limitations