[Case 01]
Accenture Gen-Ai Navigator Platform
Enterprise Internal Application
[ Impact ]
[Project Overview]
Accenture's internal knowledge ecosystem is vast accelerators, solution frameworks, case studies, benchmarks, SME insights accumulated over decades. But that knowledge was fragmented across hundreds of tools and repositories, locked behind manual search, and dependent on individual tribal knowledge.
For every new client pitch, consultants were effectively starting from scratch: searching the same repositories, relying on the same overbooked SMEs, and rebuilding context that already existed somewhere in the system. World-class expertise was being lost to logistics.
[My Role]
Product Designer
[Platforms]
Internal Enterprise Platform
[Problem Statement]
How might we design an AI-powered navigator that helps Accenture teams rapidly curate, contextualise, and present solutions for clients using both the internal knowledge base and generative AI
Knowledge spread across hundreds of tools with no unified entry point
Multiple clicks, duplicated workflows, inconsistent outputs per team
No way to translate internal knowledge into client-ready narratives in real time
Heavy dependency on SMEs for every discovery cycle
[ Users & Context ]
The primary users were senior ( Subject Matter Experts ) SMEs and executives responsible for leading client conversations, identifying pain points, and proposing tailored solutions. These users typically operate in high-pressure environments such as live meetings and workshops, often while managing multiple tools, documents, and inputs simultaneously.
In this context, the platform was not a primary workspace but a supporting system used alongside presentations, notes, and real-time discussion. Speed, clarity, and trust were critical, while complex interactions or dense visual elements risked becoming distractions.
[ Research ]
The most challenging constraint on this project was access.
SMEs and senior leaders — The primary users were impossible to schedule. Calendars were blocked, meetings were back-to-back, and structured interview sessions simply weren't happening.
Rather than wait, I proposed a different approach to my manager: let me go to them.
This led to CONTEXTUAL INQUIRY
I spent a full day embedded with the SME team sitting in their offices, sharing working spaces, observing from the corner of conference rooms during live sessions. No script, no interview guide. Just observation.
What I saw changed the product direction entirely.
Every single SME was running two screens. A primary screen for the meeting, slides, video calls, notes. And a secondary screen, constantly, where they were searching for data, pulling up case studies, cross-referencing solutions all while trying to stay present in the conversation.
The secondary screen was their lifeline, and it was completely undesigned for that purpose.
[Research Insights ]
They didn't need a better dashboard.
They needed a glanceable secondary interface information available at a glance, requiring zero navigation while in flow. This observation shaped every major design decision that followed:
Large, scannable typography over dense data
Minimal interaction depth look, don't click
[Process]
This project coincided with the earliest commercial wave of large language models, and the ambiguity was real. Parallel workstreams from legal, tech, and compliance teams were running simultaneously with unanswered questions:
How much internal client data could be shared with an LLM?
Could we access models via API or only via local servers?
What guardrails were needed to reduce hallucination risk?
A traditional double diamond process assumes you can define the problem, then solve it. Here, the problem kept shifting as technical constraints evolved. So we shifted too working with explicit assumptions, fast iterations, and progressive commitment rather than waiting for certainty that wasn't coming.
Trust requires transparency, users need to see where information comes from
AI value must be expressed in business outcomes, not technical specs
Conversational entry points reduce cognitive load
Users need clarity, not volume
[ Design Goals ]
The design focused on enabling experts to stay present in conversations while accessing relevant information seamlessly.
Reduce cognitive load during live usage
Surface insights and assets instantly
Support confident, real-time decision-making
Maintain a high level of clarity and trust
Fit naturally into existing consultant workflows
[ Approach ]
Given the executive nature of the users and limited availability for structured research, the design process prioritised contextual understanding and iterative validation. Inputs from stakeholders, usage patterns, and real working environments informed design decisions.
The approach emphasised practicality over novelty focusing on how the platform would be used in real moments rather than how it appeared in isolation.
[ Solution ]
The final solution was a streamlined, lightweight GenAI interface designed to function as a glanceable assistant rather than an immersive application. Key elements included:
A minimal, distraction-free layout
Large, readable typography for at-a-glance consumption
“Battlecard” style summaries with key talking points and assets
Simplified navigation and reduced interaction depth
Voice-first and hands-free interaction where appropriate
The design intentionally removed unnecessary visual complexity to prioritise speed and usability in live settings.
[ Solution ]
Designing for expert users requires a deep understanding of context, not just tasks. In high-stakes environments, the most effective design is often the one that stays out of the way supporting thinking, reducing friction, and enabling confidence without demanding attention.
This project reinforced the importance of clarity, restraint, and workflow-first thinking when building enterprise AI products.
[Persona]
Ryan Fransis
Subject matter Expert
Age: 29
Location: New York City
Tech Proficiency: Moderate
Gender: Male
[Goal]
Quickly complete purchases without interruptions.
Access accurate product details and a seamless payment process.
Trust the platform with his payment and personal information.
[Frustrations]
Quickly complete purchases without interruptions.
Access accurate product details and a seamless payment process.
Trust the platform with his payment and personal information.
[Process]
[01] User Research
Conducted interviews with 15 frequent users to uncover frustrations and preferences.
Analyzed behavioral data to identify bottlenecks in the current flow.
Benchmarked against competitors to identify best practices for checkout flows.
[01] User Research
Conducted interviews with 15 frequent users to uncover frustrations and preferences.
Analyzed behavioral data to identify bottlenecks in the current flow.
Benchmarked against competitors to identify best practices for checkout flows.
[01] User Research
Conducted interviews with 15 frequent users to uncover frustrations and preferences.
Analyzed behavioral data to identify bottlenecks in the current flow.
Benchmarked against competitors to identify best practices for checkout flows.
[Outcome]
25% increase in checkout completion rates.
30% reduction in cart abandonment on mobile devices.
30% reduction in cart abandonment on mobile devices.
[Key Learnings]
Human-Centered Approach
Designing experiences that prioritize user needs and behaviors.
Human-Centered Approach
Designing experiences that prioritize user needs and behaviors.
Human-Centered Approach
Designing experiences that prioritize user needs and behaviors.
[Key Learnings]
Human-Centered Approach
Designing experiences that prioritize user needs and behaviors.
Human-Centered Approach
Designing experiences that prioritize user needs and behaviors.
Human-Centered Approach
Designing experiences that prioritize user needs and behaviors.