Requirements Definition establishes what you actually need from an AI solution before evaluating options. This includes understanding the specific business problem you’re solving, the use cases you’re enabling, performance expectations, integration requirements, compliance obligations, and non-negotiable constraints. Without clear requirements, organisations choose solutions based on features or marketing rather than fit-for-purpose capability.
Starting with requirements prevents costly mistakes—selecting overpowered (expensive) solutions for simple needs, or underpowered solutions that can’t meet critical performance or compliance requirements. It also provides objective criteria for evaluating competing options.
Key Questions to Answer
Business Context
What business problem are you solving?
- What is the current state and pain point?
- What does success look like?
- How will you measure ROI?
- Who are the primary users and stakeholders?
What are your use cases?
- Content generation (marketing, documentation, code)?
- Analysis and insights (data analysis, research, decision support)?
- Customer interaction (chatbots, support automation)?
- Process automation (document processing, workflow)?
- Specialized tasks (translation, summarization, classification)?
Technical Requirements
Performance expectations:
- Response time/latency requirements
- Accuracy thresholds
- Throughput/volume requirements
- Availability expectations (SLA)
Integration needs:
- Which systems must AI integrate with?
- Data sources AI will access
- Authentication mechanisms (SSO, SAML, OAuth)
- API requirements
Data characteristics:
- Volume and velocity of data
- Data sensitivity classification
- Languages and formats
- Structured vs unstructured data
Compliance and Risk
Regulatory obligations:
- GDPR, AI Act, sector-specific regulations
- Data residency requirements (which jurisdictions?)
- Audit trail requirements
- Right to explanation obligations
Security requirements:
- Data encryption (in transit, at rest)
- Network isolation needs
- Access controls
- Penetration testing requirements
Risk tolerance:
- Acceptable error rates
- Consequences of AI failure
- Human oversight requirements
- Brand and reputational risk considerations
Organizational Constraints
Budget parameters:
- Capital vs operational expenditure
- Total cost ceiling
- Cost-per-transaction limits
- Multi-year budget availability
Timeline:
- Speed to deployment required
- Proof-of-concept vs production timeline
- Change management timeframes
Skills and resources:
- Internal technical capability
- Training requirements
- Ongoing support model
Requirements Documentation Template
Create a structured requirements document covering:
- Executive Summary — Problem, objectives, success criteria
- Use Cases — Detailed scenarios with examples
- Functional Requirements — What the solution must do
- Non-Functional Requirements — Performance, security, scalability
- Integration Requirements — Systems, data sources, protocols
- Compliance Requirements — Regulatory obligations, data handling
- Constraints — Budget, timeline, technical limitations
- Evaluation Criteria — Weighted scoring framework
Common Pitfalls
Solution-first thinking — Starting with “we need ChatGPT” rather than “we need to solve X problem”
Underspecifying compliance requirements — Discovering data residency or audit requirements after vendor selection
Ignoring integration complexity — Assuming “it’s just an API” without understanding authentication, data transformation, or error handling
Vague performance requirements — “Fast” or “accurate” without quantified thresholds
Overlooking change management — Focusing on technology while ignoring user adoption and training needs
Next Steps
With clear requirements documented, you can now evaluate the solution landscape systematically. The next section maps the types of AI solutions available and their typical fit-for-purpose scenarios.