AI-Powered Anomaly Detection for Workiva
An AI assist for financial analysts who hunt for anomalies in collaborative spreadsheets. Cuts down the manual scanning so review moves faster.
The Challenge
Workiva users were spending 73% more time than necessary reviewing collaborative reports for data anomalies and formatting inconsistencies. Datasets kept growing, compliance deadlines kept tightening, and manual review couldn't keep up.
Research & Discovery
Understanding the Problem Space
I spoke with Business Analysts and MIS professionals about how they actually do anomaly review today. Most relied on domain knowledge plus manual scanning, which produced inconsistent results and missed outliers.
User Interviews
6 participants across Business Analysts and MIS students using structured 18-question framework.
Thematic Analysis
Identified 3 core themes: automatic flagging, learnability, and visualization needs.
Competitive Analysis
Evaluated existing solutions and identified gaps in enterprise-grade anomaly detection.
Key User Insights
"Ideally it would tell me what the problem could be as well. So let's say there's an increase in acquisitions this month. Maybe we can tell me that it's possibly because this variable seems to have too many missing values."
"An alert system basically — like the moment it finds out an alert and it should be real time. It should actually tell you where exactly is the error instead of just telling that there is something wrong."
Flagging
Users need automated systems that can identify anomalies and provide contextual explanations for why data points are flagged as unusual.
Real-time Alerts
Immediate notification systems that pinpoint exact locations of errors rather than general warnings about data quality issues.
Visual Hierarchy
Clear color-coding and visual indicators that make anomalies immediately recognizable within large datasets.
User Personas
Sarah Mitchell
Novice User
"I need tools that guide me through the process and explain what's happening at each step."
- Learn anomaly detection best practices quickly
- Gain confidence in identifying data issues
- Understand why certain data points are flagged
- Overwhelmed by complex datasets
- Unsure which anomalies are critical
- Needs contextual help and guidance
David Chen
Experienced User
"I need speed and accuracy. Show me the anomalies, let me decide what to do with them."
- Process large datasets efficiently
- Customize detection parameters for specific use cases
- Integrate findings into automated workflows
- Manual detection is time-consuming
- Inconsistent results across team members
- Difficulty prioritizing high-impact anomalies
Wireframes
Design Solution
AI-Powered Anomaly Detection Interface
The design pairs a machine learning model with an interface that flags anomalies in real time inside the Workiva spreadsheet, then explains what triggered the flag and what to do next.
Impact & Results
The design goes after the workflow bottlenecks users described and gives Workiva a stronger position in AI-assisted financial data analysis.
Key Learnings
- For enterprise adoption, AI has to be explainable before it can be accurate.
- Real-time feedback keeps users trusting an automated system. Without it, trust falls apart fast.
- Visual hierarchy and progressive disclosure help people move through dense data without getting lost.
