Why AI Requirements Are Different
Traditional software requirements specify deterministic behavior: given input X, produce output Y. AI systems are probabilistic. They produce correct results most of the time, handle edge cases unpredictably, and improve with more data. Requirements engineering for AI must account for accuracy thresholds, confidence scoring, graceful degradation, feedback loops, and evolving performance over time. We translate business objectives into specifications that AI engineers can build against and stakeholders can evaluate.
Functional Specifications
We define what the AI system must do in terms of inputs, outputs, decision boundaries, and edge case handling. Each function includes accuracy targets, latency constraints, and example inputs with expected outputs.
Compliance Constraints
Regulatory requirements shape AI design. We document GDPR data minimization, CCPA disclosure, HIPAA de-identification, Fair Credit Act explainability, and industry-specific constraints that must be built into the system.
Budget Parameters
We define cost envelopes for development, infrastructure, and ongoing operations. Monthly API costs, compute spend, storage, and human review labor are budgeted separately so trade-offs are visible.
Integration Requirements
We specify every system the AI must connect to, including data formats, authentication methods, error handling expectations, and SLA requirements for each integration point.
Requirements Process
Discover
Elicit needs from stakeholders
Specify
Write structured requirements
Validate
Review with business and engineering
Baseline
Lock scope and acceptance criteria
Discover
Elicit needs from stakeholders
Specify
Write structured requirements
Validate
Review with business and engineering
Baseline
Lock scope and acceptance criteria
Requirements Decision Tree
Defining Success Metrics
Every AI requirement includes measurable acceptance criteria. Vague goals like "improve customer experience" become specificmetrics like "reduce average response time from 4 hours to 15 minutes for tier-1 support tickets while maintaining 92% customer satisfaction rating." These metrics become the criteria for evaluating whether the AI system is performing as specified.
Accuracy thresholds. We define minimum acceptable accuracy for each AI function, informed by the cost of errors. A medical triage system needs different accuracy than a product recommendation engine. Thresholds are set per category, not as a single aggregate number.
Performance baselines. Before AI can improve something, you need to know how it performs today. We establish current-state measurements for every metric the AI system is expected to improve, creating a clear before-and-after comparisonframework.
Monitoring requirements. We specify what must be measured in production: input distributions,output quality, drift detection, fairness metrics, and user feedback capture. These monitoring requirements are part of the initial spec, not an afterthought.
Who This Is For
Requirements engineering is essential before any significant AI development investment. It is particularly valuable when multiple stakeholders have different expectations, when compliance requirements constrain design, or when the development will be performed by an external vendor who needs a clear contract specification.
Contact us at ben@oakenai.tech to define your AI requirements.