Case Study · Enterprise AI Product
60%
10+
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23min
13+
2.5hrs
"I spend my first hour every morning just figuring out what to work on. By the time I'm ready to actually work, I'm already exhausted."
Senior Product Manager, Series B SaaS Company
1
The Forgotten Achievements Gap
Professionals struggled to recall and articulate their work during performance reviews. Key contributions were scattered across Jira, meetings, and emails, forcing people to reconstruct months of work manually—often incompletely.
2
Meeting Preparation Paralysis
Preparing for meetings required manually scanning Slack threads, Jira tickets, and email chains. This often took 30+ minutes per meeting, leading many to join underprepared—or skip preparation entirely.
3
Priority Blindness in Communication
With 100+ daily emails, important messages—especially from leadership or cross-functional teams—were buried among newsletters, notifications, and automated updates.
4
Morning Decision Fatigue
At the start of the day, professionals lacked a clear sense of what required immediate attention. Tasks, meetings, and messages lived in different tools, leaving people unsure where to begin.
Persona 1 — Primary
Goals
→ Start each day knowing what actually matters
→ Communicate impact clearly during reviews
I need a single source of truth that tells me what deserves my attention today.
Persona 2 — Core Contributor
Goals
→ Work with full task context without leaving the IDE
→ Capture contributions without extra reporting
My work lives in code and tickets — reviews shouldn’t depend on my memory.
Persona 3 — Manager View
Goals
→ Spot blockers before they affect delivery
→ Spend less time preparing for status updates
I want to understand my team’s reality without scheduling another meeting.
Persona 4 — Cross-Functional Lead
Goals
→ Track campaign progress across tools
→ Generate stakeholder-ready decks faster
The work is happening — it just isn’t visible in one place.
Morning Kickoff
8:00 AM
Before
Too many unread messages
No clear top priorities
After
Daily AI Brief gives top 3
Combines emails, Slack, Jira
Midday Execution
12:00 PM
Before
Constant context switching
Manual reporting & updates
After
Unified workspace view
1-tap status updates
Afternoon Meetings
2:00 PM
Before
No time to prep meeting context
Action items get lost afterward
After
Auto meeting summaries
Auto task extraction → Jira
Evening Wrap-Up
6:00 PM
Before
Hard to track what was done
Forgetting achievements
After
Auto daily logs
Continuous achievement tracking
Morning Kickoff
8:00 AM
Before
Too many unread messages
No clear top priorities
After
Daily AI Brief gives top 3
Combines emails, Slack, Jira
1
Fragmented Context
Work signals are distributed across tools and domains.
2
Opaque Automation
AI systems often act without explaining intent or confidence.
3
Cognitive Overload
Professionals manage parallel priorities with limited mental bandwidth.
Decision 1
Attempt 1 — Rejected
“Surface work as real-time events so nothing is missed.”
→ Time-critical alerts need immediate attention
→ Monitoring event-driven systems
→ Single-threaded workflows with clear next actions
→ Multiple priorities compete simultaneously
→ Context is required to make informed decisions
→ Notifications create anxiety rather than clarity
Attempt 2 — Explored
“Guide users through decisions in priority sequence based on context and urgency.”
→ Work follows a clear sequence with explicit dependencies
→ Users need guided flow through complex processes
→ Temporal urgency is the primary prioritization factor
→ Work is parallel across multiple domains
→ Users need to see the full landscape and context
→ Priorities shift dynamically throughout the day
→ Forced sequencing reduces user autonomy
Final Direction — Selected
“Provide full situational awareness while preserving flexible decision-making.”
→ See the entire work landscape at a glance
→ Act in any order based on context and judgment
→ Understand relationships between tasks, meetings, and emails
→ Switch between parallel responsibilities without losing context
→ Requires intelligent prioritization to prevent information overload
→ More complex system orchestration across multiple domains
→ Users must interpret context rather than follow guided sequence
→ AI must continuously adapt to evolving work patterns
Why This Model Was Chosen
After testing multiple delivery models, the structured workspace provided the best balance of:
→ Overview and depth — See everything without losing detail
→ Guidance and autonomy — AI prioritizes, users decide
→ Structure and flexibility — organized but not restrictive
It supports real-world work complexity without forcing behavior. The core principle: Aira should structure work—not dictate it.
Decision 2
Trust Building
Cognitive Load
Decision Speed
High (>90%)
Medium (70-90%)
Low (<70%)
Decision 3
Rejected
Each platform (web, mobile, extension) runs its own AI model locally.
→ Ultra-low latency, works offline
→ No cross-platform learning, inconsistent experience, higher resource cost
Rejected
All intelligence lives in the cloud, platforms are thin clients.
→ Perfect consistency, continuous learning
→ Network latency kills UX, no offline mode, single point of failure
Selected
Central intelligence with platform-specific inference caching and adaptive sync.
→ Core intelligence and learning loop in cloud (context graph, user models)
→ Platforms cache recent inferences locally (instant response for common patterns)
→ Adaptive sync: aggressive when online, graceful degradation offline
→ Conflict resolution: server wins, client updates reconcile on reconnect
The central hub where professionals start their day and access deep workflows
Intelligent Daily Brief
Every morning, Aira generates a focused daily brief
by analyzing your work context across tools.
How priorities are ranked
Deadline proximity
Business impact
Task dependencies
Recent mentions
⏱️ Reduced morning orientation from 45 minutes to under 2 minutes.
Meeting Intelligence Lifecycle
Before:
• Agenda & prior discussion
• Open action items
During:
• Live transcription
• AI-tagged decisions & blockers
After:
• Smart summary
• Jira tasks auto-created
📉 Reduced status meetings from 5 → 2 per week through continuous visibility.
Context-Aware Email Intelligence
Aira understands email context, not just content.
When you open a project email, it instantly surfaces related Jira tickets, recent meetings, and Slack discussions—so replies are grounded in real work context.
Career Timeline & Review Decks
Aira continuously logs achievements—completed tickets, meetings led, recognition received, problems solved. When performance review season arrives, one click generates a complete deck with quantified impact, peer quotes, and growth areas.
Designed for on-the-go professionals who need hands-free intelligence
Solution 1
The Problem
Users need clarity in the first 30 seconds of their day—often while commuting or away from their desk.
The Solution
A voice-first daily brief, auto-generated on open and ranked by urgency and meeting impact.
→ Hands-free orientation while commuting
→ Priorities ranked by urgency & impact
Impact: Morning orientation reduced from ~45 minutes → under 2 minutes
Solution 2
The Problem
Small but critical decisions stall until users return to their desk—blocking dependent work.
The Solution
A mobile-first way to resolve decisions and follow-ups instantly, with full context attached.
→ Surfaces pending approvals & acknowledgements
→ One-tap actions without context switching
→ Linked to meeting, ticket, or email
Impact: Enabled faster resolution of dependent work by removing approval delays from desktop-bound workflows.
1
AI Trust is Earned Through Incremental Exposure
Early prototypes with full automation terrified users. The breakthrough came when I designed the “confidence escalation ladder”—start with suggestions, graduate to drafts, finally enable auto-execution. Users who experienced this progression developed trust 3x faster than those dropped into full automation.
Application for future projects: When designing AI products, build trust architectures that start conservative and become more autonomous as the user gains confidence.
2
Cross-Platform Consistency ≠ Identical Design
My initial instinct was making all platforms visually identical. User testing revealed this felt unnatural—mobile users want voice and gestures, IDE users want keyboard-first interactions, Slack users expect conversational interfaces.
I pivoted to “consistent intelligence, native interactions”—same brain, different bodies. Each platform feels native to its context while maintaining the same underlying intelligence.
3
Empty States Are Onboarding Opportunities
When users first connected Jira but had no synced tasks, our initial empty state said “No tasks found”—which created anxiety. I redesigned it to show “Setting up your workspace…” with a progress indicator and contextual tips.
This transformed anxiety-inducing empty states into reassuring moments that built confidence. First-time users’ completion rates improved by 34%.
4
Context is the Killer Feature
In early user testing, participants loved individual features (meeting summaries, smart email replies) but the “wow moment” came when they saw everything connected. Opening an email and seeing the related Jira ticket, last meeting discussion, and Slack threads automatically—that’s when they understood Aira’s value.
Insight: The real innovation wasn’t any single feature—it was the context graph connecting everything. Future AI products should prioritize relationship mapping over isolated capabilities.
5
Design for the 80%, Configure for the 20%
I initially tried to design for every workflow variation, which led to overwhelming complexity. The breakthrough came from identifying the 80% use case (knowledge workers in mid-size tech companies) and designing the core experience around them, while making the system configurable for edge cases.
This resulted in a simpler initial experience with powerful customization for advanced users—serving both populations without compromising either.