01COMMAND CENTER↗
Queue / 24 activeReview priority
High confidence
Queue / 24 active
01AI
AI / ENTERPRISEF2 — Making enterprise AI understandable
A product-design narrative for turning powerful AI and operational workflows into a clear, trustworthy experience.
●●●PRODUCT / EXPERIENCE
RECOMMENDATION
Resolve duplicate vendor records
EVIDENCE4 matching signalsCompany · address · invoice · domain
NEXT ACTIONMerge records2 records will be archived
01Approved product mockup / presentation visualScroll to explore ↓
INTERFACE STUDY
Designed as a system.
Supporting interface moments turn the hero screen into a product story: hierarchy, states and interaction patterns are shown as connected parts of one experience.
02EVIDENCE LAYER↗
Signal strength4 matching signals
Source trace ↗
Signal strength
03APPROVAL FLOW↗
Before → AfterHuman review
Confirm action
Before → After
THE CONTEXT
Start with the why.
F2 is framed around the kind of AI and digital-transformation work represented in Tayyaba’s professional experience: complex capabilities, multiple stakeholders and a need to make automation legible.
01 / DISCOVERY
What the experience revealed.
Before shaping the interface, the work is framed around the user's mental model, moments of friction and the opportunities that can change the journey.
KEY OBSERVATIONS
01
Users need to distinguish what the AI decided from what they still control.
02
Evidence and system status need to be visible at the moment of review.
03
Exceptions are more important than the happy path for trust.
PAIN POINTS
×
Unclear ownership between automation and people
×
Too much technical context at once
×
OPPORTUNITIESLow visibility into why a recommendation was made
↗
Make recommendation → evidence → action a repeatable pattern
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Expose confidence and status without overwhelming the user
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Design review and recovery states before edge cases arrive
02 / INFORMATION ARCHITECTURE
Structure before surface.
The interface follows a clear information hierarchy so visual polish supports the product model rather than hiding it.
01Workspace→
02Task / queue→
03Recommendation→
04Evidence→
05Review / approval→
06Outcome / history
THE PROBLEM
The challenge was not simply to make an AI interface look modern. Users need to understand what the system knows, what it is doing, what requires their decision and what happens next. Without that mental model, even capable automation can feel risky.
DESIGN HYPOTHESIS
If the experience separates intent, system context, automation and human approval into recognizable layers, users will move through complex workflows with greater confidence and less cognitive friction.
WHO IT SERVESOperational teams, decision makers and internal product stakeholders who need to review, approve and act on AI-assisted work.
03 / USER JOURNEYFrom first intent
From first intent
to useful outcome.
The journey is intentionally expressed as a sequence of decisions, not a list of screens. This keeps the case study about behavior and product thinking.
01Arrive with a task or exceptionentry
02Understand context and system recommendationdecision
03Inspect supporting evidencedecision
04Approve, edit or route the actiondecision
05Monitor outcome and return to the next priorityoutcome
04 / PROCESS
How the work moved forward.
01
01 — Discover
Start from user intent rather than the AI feature list. Identify the moments where people need explanation, control or reassurance.
Task framingUser questionsRisk moments
02
02 — Structure
Create a hierarchy that makes status, recommendation, evidence and next action visually distinct.
Information architectureDecision hierarchyFlow map
03
03 — Prototype
Prototype the highest-risk states first: uncertainty, exceptions, approvals and handoffs between people and automation.
Interactive flowsEdge statesApproval patterns
04
04 — Systemize
Translate successful patterns into reusable components so future AI workflows feel like one coherent product.
Component modelTokensInteraction rules
05 / DESIGN DECISIONS
Small choices create the experience.
These are the decisions that make the final interface feel coherent rather than merely polished.
01
Show the recommendation before secondary detail, but keep evidence one step away.
02
Make human approval explicit instead of visually blending it into automation.
03
Use progressive disclosure for technical context.
04
Treat empty, loading, error and confidence states as first-class product design.
06 / UI SYSTEM
A visual language that scales.
Instead of designing each screen independently, repeated behavior becomes a system. That gives future screens the same rhythm and makes implementation more predictable.
COMPONENTS
01Command surfaces↗
02AI recommendation cards↗
03Evidence drawers↗
04Approval / edit states↗
05Status and confidence language↗
Designed around the decision.
07 / OUTCOME
The portfolio story positions F2 around clarity, trust and scalable product language — a stronger proof of systems thinking than a collection of isolated screens.
Portfolio synthesis based on Tayyaba’s professional experience and available project context. No unverified business metrics are presented.