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 initiated as a Cognos migration effort, the project evolved into an opportunity to redesign how users interact with reporting data and make decisions.

Users regularly worked with reports containing 500,000+ records.

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

While the reporting system generated data efficiently, the effort required to transform 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

Through interviews and group sessions with 10+ users, I discovered that users were spending more time preparing information than making decisions.

Three recurring behaviors emerged:

• The same reports were repeatedly generated for recurring business needs.
• Similar report configurations were recreated with minor parameter changes.
• Report failures often required users to repeat the entire setup process.

The business initially viewed the initiative as a migration project focused on feature parity.

However, research revealed that replicating the existing experience would also preserve many of the inefficiencies users had adapted to over time.

This shifted the conversation from:

“How do we replicate Cognos?”

to

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

AI was integrated throughout the design process to accelerate both research and solution development.

This included:

• Synthesizing qualitative research findings
• Identifying behavioral patterns and opportunities
• Exploring solution directions
• Evaluating edge cases
• Rapid prototyping and iteration

Impact

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

Rather than recreating existing workflows, the solution 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.

• Reduced manual effort spent preparing and analyzing report data
• Faster execution of recurring reporting workflows
• Reduced dependency on technology teams for ad-hoc reporting needs
• Improved visibility into report execution and recovery
• Increased reporting consistency and accuracy
• Scalable foundation for future enhancements

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 synthesis, and product thinking, we identified opportunities that users had adapted to over time but never expected the system itself to solve.