AI Experience Design Framework
A unified, intelligent, self-service entry point for all data & AI platform interactions, discover services, request access, provision resources, read documentation, monitor requests, and get AI-assisted guidance from a single experience. This board is the design process behind it.
Empathize
Human
Who is the user?
People who need to discover, request, access, and consume data platform services to accomplish technical or analytical work.
People who need visibility into platform capabilities, adoption, roadmap, and the broader data ecosystem to support planning, investment, and business decisions.
What are they trying to accomplish?
Discover the right data/platform service, understand whether it meets their need, request access or resources, and get to a usable state with minimal dependency on platform teams.
Understand what the platform provides, how it is being used, where demand exists, and how the data ecosystem is evolving so they can make informed business and investment decisions.
User journey — where AI actually touches it
The same Intent → Discover → Decide → Request → Consume → Feedback flow named under “Where do they need assistance?”, walked stage by stage against what AI actually does at each one — and what stays human.
User does
States what they want to do, in their own words — not a form field, a sentence.
Human-only moment
None yet. This moment stays entirely human — AI is listening, not deciding.
User does
Sees the catalogue narrowed to what's actually relevant, instead of searching it alone.
AI touchpoint
Interprets the stated intent, searches the catalogue, cross-references usage, and surfaces options worth considering.
User does
Chooses a path, with the reasoning behind each option visible.
AI touchpoint
Explains the recommendation and guides the next step — the decision itself stays human.
User does
Reviews an already-assembled request and sends it in.
AI touchpoint
Prepares the request from what's already established and validates it against policy before a human submits and approves it.
User does
Gets what they asked for, and can see it's actually working.
AI touchpoint
Provisions the approved change and monitors it afterward.
User does
Finds out quickly if something's gone wrong, and who's handling it.
AI touchpoint
Detects the deviation and raises it — a human still decides what happens next.
What are their pain points?
Discovery
Difficult to find the right service within a dense catalogue.
Service names and technical terminology don't always map to the user's mental model.
Users may know what they want to accomplish, but not which platform service enables it.
Decision-making
Users need help determining which service is appropriate for their use case.
Comparing services requires understanding technical capabilities, prerequisites, limitations, ownership and policies.
Business visibility
Platform capabilities, adoption and ecosystem information may be fragmented.
Business stakeholders need a higher-level view rather than navigating individual technical services.
Requesting
Users have to translate their intent into technical request details.
Users may not understand why certain information or approvals are required.
Request forms can require information the user doesn't naturally think in terms of.
Progress & visibility
Once a request is submitted, users may not understand what is happening.
When something fails, users may not know what failed, why, or what they can do next.
Dependencies, approvals and provisioning steps can be opaque.
What decisions do they make? (Who & What?)
- What data/platform capability they need.
- Which service best fits their use case.
- Whether a service meets their requirements.
- What resources/access they need.
- Whether they are eligible to use a service.
- What configuration is appropriate.
- Whether to submit a request.
- Whether to accept an AI recommendation.
- What to do when a request fails or requires intervention.
- Which capabilities are available and relevant.
- Where platform adoption/demand is growing.
- Where gaps exist.
- What capabilities may need investment.
- Which initiatives or roadmap priorities matter to the business.
What do they need to delegate?
Data Practitioners need to delegate
information discovery and service selection to platform experts, while request creation, submission and follow-through remain largely manual.
Business Stakeholders need to delegate
ecosystem discovery, platform insights while synthesizing that information for planning and decision-making remains largely manual.
Where do they need assistance?
Data Practitioners
Business Stakeholder
Context
CO-CREATING/COLLABORATION WITH OTHER STAKEHOLDERS
What environment are they working in?
A standalone enterprise self-service application that acts as a single entry point to the organization's Data & AI platform ecosystem.
What systems do they use?
What data do they have?
User context
- Role
- Team
- Permissions
- Existing access
- Previous requests
Service knowledge
- Capabilities
- Requirements
- Availability
- Ownership
- Documentation
- Policies
System state
- Request status
- Provisioning status
- Errors
Organizational context
- Platform roadmap
- Adoption data
What constraints exist?
Access and permission boundaries
Data sensitivity
Organizational policies
AI capability limitations
Incomplete or conflicting information
Human approval requirements
Potentially irreversible actions
Intent
What outcome are we trying to create?
Move users from knowing what they want to achieve to successfully achieving it with less searching, less technical translation, less manual coordination and greater visibility throughout the journey.
Why AI?
Because users often know the outcome they want, but not the platform capability, service or technical configuration required to achieve it. AI can bridge that gap by understanding intent, synthesizing platform knowledge, recommending appropriate services, preparing actions, and helping users recover when things go wrong.
Why now?
As the Data & AI ecosystem grows, the number of services, dependencies, policies and configuration choices increases. A traditional catalogue and form-based self-service model increasingly places the burden of understanding the platform on the user. AI creates an opportunity to shift from users navigating the platform to the platform helping users navigate it.
Define
What are the actions? Write them as verbs only.
How does the work actually move between human, AI and the platform services?
Enterprise Data & AI Platform | Self Service Portal
Experience information model
1. Intent layer
What the user is trying to do
2. Entity layer
The platform as connected entities
Platform Core
- Data & AI Workspaces
- Compute Scaling
- Storage Integration
- Pipeline Monitor
- AI Compute Limit Increase
- Cost Intelligence
Data Fabric
- Federated Teams
- Technical Catalog
- Access Intelligence Hub
- Data Quality Hub
- Repository
My Requests
- Track / Manage request
Release Notes
- Platform Core
- Data Fabric
Home
- What is the platform?
- Platform Metrics
- Adoption · Usage Overview
- Platform Health · Key Insights
Help & Support
- Contact Support
- FAQ
- Provide Feedback
- About the AI Analytics Platform
Relationship layer
How the systems work together
3. State and action layer
States belong to an entity type
My Requests — lifecycle
Enablement Services — lifecycle
Available actions
4. Knowledge layer
What answers are grounded in
Technical Catalog
Service and asset detail
Release Notes
What changed and when
Platform Metrics
Adoption, health, insights
FAQ
Answered questions
About the AI Analytics Platform
Scope and boundaries
AI layer
Two surfaces, one model
Platform AI (Global Assistant)
Traverses the whole graph. Answers across Platform Core, Data Fabric and My Requests.
Service-Specific AI (Embedded AI)
Scoped to one entity and its edges. Same model, narrower traversal.
Terminology unchanged from the base information architecture.
Design
Medium
What is the right fit for this experience? UI, Conversational, Embedded AI, Multimodal, Agents, invisible…
Delegation
How intent gets in, and how precisely it can be expressed. Conversational, structured, or both.
Transparency
What it's doing, why, and how sure it is. Uncertainty and explanation live here.
Visibility
Can the user check the result faster than doing it themselves? If not, the automation is decorative.
Control
Interrupt, redirect mid-run, override, undo, recover.
Uncertainty
What happens when AI isn't sure? Ask, flag, or defer to a human.
Recovery
What happens when something goes wrong?
Feedback
How does AI communicate progress, status and outcome?
Prototype
Experience prototype
The interface design itself: screens, states, and the moments that steer a live interface.
Behaviour prototype
How does the actual model actually act?
Co-creating/collaboration with User and Dev Stakeholders.