AI in Requirements Engineering
This dimension assesses how AI assists in capturing, refining, and prioritising requirements.
Sample assessment questions for each level:
- Level -1: “Is the use of AI tools for requirements analysis explicitly prohibited or discouraged?”
- Level 0: “Are AI requirements tools used inconsistently by individual team members without standards?”
- Level 1: “Has the organisation identified specific use cases for AI in requirements gathering?”
- Level 2: “Are large language models used to draft requirement documentation?”
- Level 3: “Is AI used to analyse customer feedback or behaviour to derive requirements?”
- Level 4: “Does AI detect ambiguity or contradictions in requirements?”
- Level 5: “Does AI proactively suggest backlog grooming or feature reprioritisation?”
Key metrics to track:
- Requirements quality improvement: Percentage reduction in ambiguities after AI analysis
- Customer feedback processing efficiency: Time saved using AI for analysis vs. manual methods
- Predictive accuracy: Percentage of AI-predicted user needs that become actual requirements
- Requirements clarity score: Measured through structured evaluation of AI-refined requirements
- Feature prioritisation alignment: Correlation between AI-suggested priorities and actual user value