Dynamic Case Details (DCD)

Dynamic Case Details (DCD) is a reporting and investigation experience used by compliance teams to analyze, monitor, and extract insights from large-scale case data for audits, examinations, and business requests.

Originally positioned as a Cognos migration initiative, the project evolved into an opportunity to rethink how users interact with reporting data and make decisions.

As the sole UX owner, I led the project end-to-end using a Double Diamond approach, combining research, AI-assisted analysis, product thinking, and design execution to uncover opportunities beyond feature parity.

The business objective was straightforward:

“Replace Cognos reporting capabilities within a modern internal platform.”

Before exploring solutions, I partnered with Product and Business stakeholders to understand the broader context surrounding the initiative.

This included:

• Product background and business objectives
• Existing assumptions and known challenges
• Success metrics and measurements
• Technical constraints
• Existing solution ideas
• User groups and workflows
• Data complexity and reporting volumes

What initially appeared to be a migration project quickly revealed a much larger opportunity space.

To understand how reporting was being used in practice, I conducted user interviews and group study sessions with compliance and business users.

Research revealed that users regularly worked with reports containing more than 500,000 records.

To answer audit requests, examinations, and business queries, they frequently exported data into Excel, manually filtered records, created pivot tables, validated outputs, and shared findings across teams.

While the reporting system generated data efficiently, transforming that data into actionable insights remained largely manual.

This resulted in:

• Heavy reliance on manual analysis
• Repetitive reporting activities
• Dependency on technology teams for ad-hoc requests
• Increased risk of reporting errors
• Slow and inconsistent workflows

Most importantly, users were spending more time preparing information than making decisions.

Following research, I introduced AI into the process to help manage the volume and complexity of information gathered during discovery.

Rather than using AI to make decisions, I used it to accelerate exploration, identify patterns, and support analysis throughout the UX process.

Research Synthesis

AI helped me:

• Consolidate interview findings
• Surface recurring themes and behavioral patterns
• Highlight user pain points and needs
• Generate empathy maps

Opportunity Identification

AI supported:

• Opportunity matrix creation
• Feature prioritization discussions
• Identification of workflow improvement areas
• Discovery of recurring reporting behaviors

Experience Definition

AI assisted in:

• Current-state process flows
• User journey development
• Information architecture exploration
• Proposed workflow structures

Solution Exploration

AI accelerated:

• Design ideation and brainstorming
• Exploration of alternative solution directions
• Edge-case analysis
• Concept evaluation before moving into design

A critical part of this process was validation.

While AI significantly accelerated synthesis and exploration, maintaining accuracy required continuous validation. Outputs often needed refinement and cross-checking against research findings, business context, and stakeholder feedback before being used in subsequent stages.

Every stage required careful review to ensure that patterns, opportunities, and recommendations remained grounded in real user behavior rather than assumptions generated by the tool.

The business initially viewed the initiative as a migration effort focused on replicating existing reporting capabilities.

Research revealed a different challenge.

Users had adapted to inefficient reporting workflows for so long that many viewed manual effort as a normal part of the process.

Original Question

“How do we replicate Cognos?”

Reframed Opportunity

“How do we reduce the effort required to get from data to decisions?”

This shift transformed the project from a migration exercise into an opportunity to redesign how users interact with reporting data and generate insights.

It became the guiding principle for the remainder of the project.

After validating research findings through stakeholder readouts and collaborative discussions, I translated insights into a solution strategy focused on reducing recurring effort and improving reporting efficiency.

Automating Recurring Workflows

Introduced report scheduling to automate recurring reporting activities through scheduled execution, notifications, and delivery.

Reducing Repetitive Configuration

Enabled reusable report configurations, allowing users to quickly rerun frequently used reports while maintaining flexibility.

Improving Visibility & Recovery

Introduced report history, execution status, and recovery actions to improve transparency and reduce rework caused by failed report runs.

Once strategic directions were aligned, I translated concepts into Figma designs, conducted design walkthroughs with stakeholders, incorporated feedback, and prepared the solution for engineering delivery.

Due to project constraints and timelines, design walkthroughs and stakeholder validation served as the primary mechanism for feedback and refinement.

UX Impact

• Reduced manual effort spent preparing and analyzing report data
• Faster execution of recurring reporting workflows
• Improved visibility into report execution and recovery
• Increased reporting consistency and accuracy

Business Impact

• Reduced dependency on technology teams for ad-hoc reporting needs
• Scalable foundation for future enhancements
• Improved efficiency across reporting and investigation workflows

Process Impact

• ~60–70% reduction in research synthesis effort
• ~50% reduction in design exploration and delivery cycles
• Faster stakeholder review and feedback loops
• More time focused on validation and decision-making

This project fundamentally changed how I think about integrating AI into UX practice.

AI is Best for Divergence, Not Decisions

AI helped generate possibilities, uncover patterns, and accelerate exploration, but final decisions still required business context, research evidence, and design judgment.

AI Accelerates Synthesis, Not Understanding

While AI significantly reduced the effort required to organize and analyze information, meaningful insights still came from direct conversations with users.

Human Judgment Remains Critical

The greatest challenge was not generating outputs—it was validating them.

Every stage required continuous review and refinement to ensure accuracy before progressing to the next phase.

One of the biggest risks in enterprise transformation projects is assuming that users want the current experience reproduced.

This project demonstrated the value of challenging that assumption.

By combining research, AI-assisted analysis, product thinking, and continuous validation, I was able to uncover opportunities that users had adapted to over time but never expected the system itself to solve.

More importantly, it reinforced a belief that continues to shape my work today:

“Technology can accelerate the design process, but meaningful outcomes still come from understanding people, questioning assumptions, and applying sound design judgment.”