Requirements Engineering for AI Products

Requirements Engineering for AI Products: Navigating Complexity and Uncertainty

When Mark Chen joined FinTech Global as their new AI Product Lead, his first project seemed straightforward: build an AI system to detect suspicious transactions. The business requirements appeared clear: “Catch the bad guys, don’t block legitimate transactions.” Six months and several failed iterations later, Mark learned a crucial lesson about AI requirements engineering: what seems simple in traditional software becomes remarkably complex when AI is involved.

“In traditional software, you’re engineering certainty,” Mark reflects. “In AI, you’re engineering probability. That fundamentally changes how you approach requirements.”

Gathering and Documenting AI-Specific Requirements

The Unique Challenge of AI Requirements

Let’s examine how successful organizations approach AI requirements through a real-world lens:

Case Study: Healthcare Diagnosis Assistant

Traditional Requirement (Failed Approach):

System shall correctly diagnose patient conditions based on symptoms.

AI-Appropriate Requirement (Successful Approach):

System shall:

– Achieve minimum 95% accuracy for primary diagnosis suggestions

– Provide confidence scores for each suggestion

– List alternative diagnoses when confidence is below 90%

– Flag cases requiring immediate human review

– Maintain false negative rate below 0.1% for critical conditions

– Update recommendations based on new medical research within 30 days

The AI Requirements Framework

A systematic approach developed through multiple successful AI implementations:

1. Performance Requirements

Quantitative Metrics:

Accuracy thresholds

Speed requirements

Resource constraints

Scale expectations

Example from Fraud Detection:

Base Requirements:

– Minimum 98% precision in fraud detection

– Maximum 0.1% false positive rate

– Real-time processing (< 100ms)

– Handle 10,000 transactions per second

– Scale to 100M daily transactions

Edge Cases:

– Maintain 95% accuracy during traffic spikes

– Degrade gracefully under data quality issues

– Handle new fraud patterns within 24 hours

2. Data Requirements

Quality Specifications:

Minimum data volumes

Quality thresholds

Update frequencies

Coverage requirements

Sample Implementation:

Training Data Requirements:

– Minimum 1M labeled transactions

– Maximum 1% missing values

– 95% confidence in labels

– Balanced class representation

– Monthly data refresh cycle

Operational Data Requirements:

– Real-time data validation

– Maximum 0.1% error rate

– 99.9% data availability

– Complete audit trail

Documentation Patterns

A major insurance company’s successful AI requirements documentation structure:

1. Hierarchical Requirements Organization

Level 1: Business Objectives

Strategic goals

Success criteria

ROI targets

Timeline constraints

Level 2: Functional Requirements

Core capabilities

User interactions

Integration points

Output formats

Level 3: Technical Requirements

Performance metrics

Data specifications

Infrastructure needs

Security requirements

2. Uncertainty Documentation

Novel Approach: Confidence Levels

Managing Stakeholder Expectations

The Expectation Management Framework

Developed from successful AI implementations across industries:

1. Education Component

2. Communication Strategy

Regular Updates:

Weekly metrics dashboards

Monthly performance reviews

Quarterly strategy alignments

Annual capability assessments

Case Study: Retail Recommendation Engine

Challenge: Managing expectations for personalization accuracy

Solution: Tiered communication approach

Tier 1: Executive Updates

– Revenue impact

– Customer engagement

– Strategic alignment

– Future capabilities

Tier 2: Technical Reviews

– Model performance

– Data quality metrics

– System health

– Improvement roadmap

Tier 3: User Communications

– Feature releases

– Usage guidelines

– Success stories

– Feedback channels

Defining Success Metrics

The Comprehensive Metrics Framework

A successful approach from a major e-commerce platform:

1. Technical Metrics

Performance Indicators:

Model accuracy

Processing speed

Resource utilization

Error rates

Implementation Example:

Primary Metrics:

– Prediction accuracy: 95%+

– Response time: <100ms

– System uptime: 99.9%

– Error rate: <0.1%

Secondary Metrics:

– Data freshness

– Model drift

– Resource efficiency

– Recovery time

2. Business Metrics

Value Indicators:

Revenue impact

Cost reduction

Customer satisfaction

Operational efficiency

Real-World Example:

Direct Impact:

– 15% revenue increase

– 30% cost reduction

– 25% efficiency gain

– 40% faster processing

Indirect Impact:

– Customer retention

– Market share

– Brand perception

– Innovation index

Handling Uncertainty and Probabilistic Outcomes

The Uncertainty Management Framework

A systematic approach to handling AI’s inherent uncertainty:

1. Probability Thresholds

Decision Framework:

2. Error Handling

Structured Approach:

Error categorization

Impact assessment

Recovery procedures

Learning mechanisms

Case Study: Financial Advisory AI

Challenge: Managing uncertainty in investment recommendations

Solution: Multi-layered confidence framework

Layer 1: Recommendation Confidence

– High (>90%): Direct recommendation

– Medium (70-90%): Multiple options

– Low (<70%): General guidance only

Layer 2: Risk Assessment

– Market volatility impact

– Data quality influence

– Model confidence variance

– External factor analysis

Layer 3: Communication Strategy

– Confidence level disclosure

– Risk factor explanation

– Alternative suggestions

– Decision support tools

Best Practices and Implementation Guide

1. Requirements Gathering

Start with business objectives

Include technical constraints

Consider data availability

Plan for uncertainty

2. Documentation

Use clear metrics

Document assumptions

Include confidence levels

Plan for updates

3. Stakeholder Management

Educate on AI capabilities

Set realistic expectations

Provide regular updates

Manage uncertainty

4. Success Measurement

Define clear metrics

Include multiple dimensions

Plan for evolution

Monitor continuously

Building for Success

As Mark from our opening story learned, successful AI requirements engineering requires a fundamental shift in thinking.

Key takeaways:

Embrace Uncertainty

Plan for probabilistic outcomes

Set realistic expectations

Build in flexibility

Monitor and adjust

Focus on Metrics:

Define clear success criteria

Use multiple dimensions

Monitor continuously

Adjust as needed

Manage Stakeholders

Educate and inform

Set realistic expectations

Communicate regularly

Build trust

“Success in AI,” Mark reflects, “comes not from eliminating uncertainty, but from learning to manage it effectively through clear requirements, realistic expectations, and robust metrics.”

Want to learn more about AI Product Management? Visit https://www.kognition.info/ai-product-management/ for in-depth and comprehensive coverage of Product Management of AI Products.