CASE STUDY · ENTERPRISE WORK
Intelligent Document Processing Framework
AI Workflow Framework for Human Review & Exception Handling
Creating reusable UX patterns for AI-assisted document validation, comparison, classification, exception handling, and human-in-the-loop review across complex enterprise workflows.
ROLE
Principal UX Designer
USERS
Operations analysts, compliance officers, document review teams
PLATFORM
Enterprise web platform
FOCUS
AI trust, human-in-the-loop verification, confidence scoring, exception handling
TOOLS
Figma, prototyping, user research, workflow mapping
CONFIDENTIALITY NOTE
Some enterprise work is recreated, abstracted, or generalized to protect confidential information. Case studies focus on the design challenge, workflow complexity, decision-making, and product outcomes rather than proprietary implementation details.
Situation
Business context
Manual document processing was a major bottleneck across compliance workflows, prone to human error and carrying high operational costs.
User context
Operations analysts needed to review large volumes of documents daily — verifying data extraction accuracy while meeting strict processing SLAs.
System context
AI extraction models were available but untrusted. Users had no visibility into confidence levels or the reasoning behind automated decisions, so they defaulted to full manual review.
Design Challenge
Design an interface where humans can verify AI-extracted data with high confidence and minimal friction — building trust in automation while preserving the control and accuracy required in financial compliance.
Low trust in automation
Users defaulted to full manual review because the AI provided no evidence for its conclusions — the system felt like a black box.
High error cost
Incorrect data extraction in financial documents carries regulatory and financial risk. Users could not afford to blindly accept automated results.
Processing bottleneck
Manual review speeds could not keep pace with increasing document volume and tightening compliance requirements.
My Role
What I led
The human-in-the-loop verification interface design and the AI feedback mechanism that allows analyst corrections to improve the model over time.
What I designed
Side-by-side document comparison views, field-level confidence scoring UI, inline editing patterns, exception handling flows, and the audit trail interface.
Who I partnered with
ML engineers, compliance officers, operations managers, front-line document analysts, and the engineering team building the extraction pipeline.
Making the Workflow Visible
Mapped the transition points between automated extraction and manual verification to identify the high-risk handoffs where human judgment is most critical — and where trust breaks down.
WORKFLOW RECREATED FOR PUBLIC PORTFOLIO
The flow is generalized to show design approach and decision-making without exposing internal systems or data. Detailed workflow maps can be discussed during interviews, subject to confidentiality constraints.
Exploring the Experience
Confidence visualization
Explored visual treatments for AI confidence scores at both the field level and the full document level.
Side-by-side comparison
Tested layouts for comparing the source document directly against extracted data fields.
Exception handling flows
Designed the flows for flagged fields, escalation paths, manual override, and supervisor review.
Batch processing patterns
Explored patterns for high-volume document review across priority-sorted queues.
Feedback loop mechanism
Prototyped the mechanism for analyst corrections to feed back into the AI model as training data.
Audit trail design
Designed the verification history view for compliance documentation and regulatory reporting.
The Solution
A unified workspace where AI suggestions are integrated directly into the existing document review flow — with transparent confidence scoring, inline correction capabilities, and a feedback loop that continuously improves the model.
ABSTRACTED AI REVIEW WORKSPACE
Visuals represent the validation model and review flow without exposing internal documents, data, or operational systems. Detailed walkthrough available during interviews, subject to confidentiality constraints.
Document queue
Priority-sorted queue with AI confidence indicators, SLA tracking, and batch processing controls.
Extraction view
Side-by-side comparison of the source document and extracted fields with field-level confidence scores.
Confidence scoring
Visual indicators showing AI certainty at both field and document level, guiding reviewer attention to uncertain areas.
Inline correction
Direct editing of extracted values with automatic AI model feedback — every correction improves future accuracy.
Exception dashboard
Centralized view of flagged documents requiring manual review, supervisor escalation, or compliance attention.
Audit history
Complete trail of all verifications, corrections, and approvals for regulatory compliance and reporting.
Key Design Decisions
Highlight-and-verify model
Transparent confidence scores
Integrated feedback loop
Progressive automation tiers
Impact
USER IMPACT
Analysts shifted from full manual review to targeted, confidence-guided verification — focusing attention where it matters most and reducing cognitive burden.
BUSINESS IMPACT
Embedded IDP workflow experiences contributed to a 75% reduction in rejected client onboarding documents and a 55% reduction in rejected home loan documents.
OPERATIONAL IMPACT
Reduced the manual processing bottleneck, improved SLA adherence, and created a path for increasing automation over time.
FRAMEWORK IMPACT
Established a reusable pattern for transparent, human-centered AI integration that influenced the approach for other enterprise AI workflow initiatives.
Reflection
What I learned
Designing for trust in AI is less about perfect automation and more about providing clear, transparent evidence for the AI's conclusions — users trust what they can understand.
What I would improve
I would explore more granular control over automation levels, allowing experienced analysts to customize their own review thresholds based on their confidence and expertise.
How this shaped my approach
This project established my framework for human-AI collaboration: transparency first, automation second, user control always. The human must always be able to understand why the AI made its decision.