Gathering and Documenting AI Business Requirements
A structured approach for product managers to effectively gather, document, and implement AI-specific business requirements.
Veronica Moss
Introduction to AI Requirements Gathering
As a Product Manager with extensive experience in AI product development, I'm excited to walk you through my process for gathering and documenting AI-related business requirements.
The unique challenges of AI projects require specialized approaches to requirements gathering that account for data dependencies, model performance expectations, and ethical considerations that traditional software projects might not address.
Specialized Approach
AI projects demand unique requirements gathering methods beyond traditional software development.
Data Dependencies
Understanding and documenting data quality and availability is fundamental to AI success.
Ethical Considerations
AI requirements must address fairness, bias, and transparency from the beginning.
The AI Requirements Gathering Framework
I use a structured framework that addresses the unique aspects of AI product development across seven key areas.
Business Problem Definition
Clearly articulate the business challenge AI will solve
Data Assessment
Evaluate data availability, quality, and gaps
Model Capability Exploration
Determine feasible AI approaches and performance targets
User Experience Integration
Define how AI will enhance user workflows
Ethical & Compliance Considerations
Establish guardrails for responsible AI use
Technical Implementation Planning
Translate capabilities into technical requirements
Success Metrics Definition
Determine how to measure AI implementation success
Step 1: Business Problem Definition
I start by clearly defining the business problem through collaborative workshops with stakeholders.
"5 Whys" Technique
Drill down to root causes through iterative questioning
Opportunity Assessment
Create canvases to evaluate potential value and feasibility
Value Propositions
Define clear benefits for each user segment
BUSINESS PROBLEM STATEMENT: Finance team members spend 15+ hours weekly manually reviewing expense reports for policy violations and fraud, causing delayed reimbursements and employee frustration. AI-powered automatic expense validation could reduce review time by 70% while increasing detection accuracy.
Step 2: Data Assessment
For AI projects, understanding data availability and quality is fundamental to success.
Data Inventory Sessions
Work with data engineers and subject matter experts to catalog available data
Data Quality Scorecards
Create visual representations of data completeness and quality
Gap Analysis
Identify missing data and develop mitigation plans
DATA ASSESSMENT SUMMARY:
  • Available: 3 years of labeled expense reports (120,000 records)
  • Quality: 92% completeness score, requires cleansing of duplicate entries
  • Access: Compliance approval needed for PII-related fields - Gaps: Limited examples of certain fraud categories
Step 3: Model Capability Exploration
Next, I document what AI capabilities will address the business problem by collaborating with data scientists to determine feasible approaches.

AI Capabilities
Determine which AI approaches can solve the business problem

Performance Targets
Define precision, recall, and accuracy requirements

Feasibility Mapping
Assess technical viability of different approaches

Innovation Potential
Identify opportunities for novel applications
MODEL CAPABILITY REQUIREMENTS:
  • Primary: Classification of expenses into compliant/non-compliant categories
  • Secondary: Anomaly detection for unusual spending patterns
  • Performance: Minimum 95% precision, 90% recall for compliance violations
  • Transparency: Ability to explain reasoning for flagged expenses
Step 4: User Experience Integration
I ensure requirements clearly define how AI will integrate with user workflows through dual-track user stories and confidence threshold guidelines.
AI Processing
System analyzes expense with confidence score
Confidence Routing
Expenses sorted based on confidence thresholds
Human Interaction
Appropriate human oversight based on confidence
Feedback Loop
Human decisions improve future AI performance
USER EXPERIENCE REQUIREMENTS:
  • High confidence violations (>90%): Auto-reject with explanation
  • Medium confidence (70-90%): Flag for human review with highlighted concerns
  • Low confidence (<70%): Process normally but log for pattern analysis
  • All decisions: Provide explanation factors for reviewers
Step 5: Ethical & Compliance Considerations
AI projects require explicit ethical guardrails and compliance checks to ensure responsible implementation.
Ethical Impact Assessments
Structured evaluation of potential ethical implications across diverse user groups and scenarios
Bias Mitigation Requirements
Specific documentation of potential bias sources and required mitigation approaches
Compliance Documentation
Clear articulation of regulatory requirements and how they'll be addressed in the AI system
Transparency Guidelines
Requirements for explainability and user understanding of AI decisions
ETHICAL REQUIREMENTS:
  • Model must maintain consistent accuracy across all employee seniority levels
  • System must not create disparities in processing time based on department or location
  • Privacy: Personal details must be obscured from expense reviewers
  • Transparency: All automated decisions must include supporting evidence
Step 6: Technical Implementation Planning
I translate AI capabilities into technical implementation requirements by defining model deployment strategies and integration requirements.

Deployment Strategy
Cloud environment specifications
Integration Requirements
API endpoints and system connections
Monitoring Systems
Drift detection and performance tracking
Fallback Mechanisms
Safety systems when AI confidence is low
TECHNICAL IMPLEMENTATION REQUIREMENTS:
  • Deployment: Model to be contained and deployed in existing cloud environment
  • Latency: Maximum 2-second response time for expense classification
  • Integration: API endpoints for ERP system and expense management platform
  • Monitoring: Drift detection system with weekly retraining evaluation
  • Fallbacks: Rule-based system activation if model confidence drops below 60%
Step 7: Success Metrics Definition
Finally, define how to measure success of the AI implementation through business KPIs and technical performance metrics.
70%
Review Time Reduction
Target decrease in manual review hours
25%
Detection Improvement
Increase in policy violation identification
95%
Precision Target
Minimum accuracy for production model
3 days
Reimbursement Time
Reduced from current 9-day average
Success metrics must tie directly back to the original business problem while also tracking technical performance to ensure the AI system maintains quality in production.
Documentation Organization
I organize AI requirements documentation in a structured format that serves different stakeholders, from executives to technical teams.

Executive Summary
Business problem, approach, outcomes, timeline
Business Requirements
Detailed problem statement, user needs, KPIs
Data Requirements
Sources, quality needs, privacy considerations
Model Requirements
Performance specs, explainability, fairness
Technical Requirements
Integration, deployment, monitoring plans
This structured approach ensures all stakeholders can quickly find the information most relevant to their role while maintaining a comprehensive view of the entire AI initiative.
Example: AI Requirements Document Excerpt
Below is a sample excerpt from an AI requirements document showing how model performance specifications should be documented.
2.3 MODEL PERFORMANCE REQUIREMENTS
This section defines the quantitative performance targets the AI model must achieve to be considered successful in production.
2.3.1 Accuracy Specifications
The expense classification model must achieve:
  • Minimum 95% precision for policy violation detection
  • Minimum 90% recall for fraud detection
2.3.2 Performance Stability
The model must maintain consistent performance across:
  • All expense categories and amounts
  • All employee departments and seniority levels
  • Peak processing periods (month-end, quarter-end)
Tools of the Trade
I leverage several specialized tools for AI requirements gathering across collaborative workshops, documentation, and AI-specific frameworks.
These specialized tools help structure the requirements gathering process and ensure all unique aspects of AI development are properly addressed from the beginning.
Practical Tips for Success
Based on my experience, here are some practical tips for effective AI requirements gathering:
Early Data Science Involvement
Include data scientists from the very beginning of requirements gathering to ensure technical feasibility and identify data challenges early.
Visual Decision Trees
Use visual decision trees to document confidence thresholds and clarify how the system should behave under different certainty levels.
Dual Performance Targets
Document both ideal and minimum viable performance metrics to create clear success criteria while allowing for practical implementation.
Conclusion
AI requirements gathering combines traditional product management with specialized considerations for data, ethics, and model characteristics.
The key to success is translating business needs into clear, measurable, and comprehensive requirements that account for the unique aspects of AI systems.
Through structured frameworks and collaboration across disciplines, teams can create requirements documentation that guide successful AI implementations that deliver real business value. Driving repeatability and agility in project execution.
Thank You!
Questions?