Measuring Utility in Generative AI for Requirements Management: How Much Should You Invest?
By: Simone Bernardi, Celeris AB
Imagine you're a captain navigating a complex sea of product development. Requirements management is your compass, ensuring you stay on course. Now, with Generative AI, you have an advanced navigation system capable of real-time corrections, optimizing your route, and predicting potential obstacles ahead. The question is: how do you measure its utility, and how much should you invest in it?
Understanding Utility in Generative AI
Utility in Generative AI isn’t just about output quality; it’s about tangible value. In the context of requirements management, this translates into:
- Clarity Gains: Does AI help refine vague or ambiguous requirements into precise, actionable statements?
- Consistency Boost: Can AI enforce uniformity in terminology and structure across teams?
- Efficiency Gains: How much time does AI save compared to manual refinement and validation?
- Risk Reduction: Does AI help identify missing or conflicting requirements before costly development phases?
A Simple Framework to Measure Utility
Organizations can assess Generative AI’s impact using a simple formula:
Utility = (Time Saved + Quality Improved + Risks Mitigated) - (Implementation Cost + Training + Oversight Effort)
Each term in this equation needs measurable data:
- Time Saved: Compare the time spent refining requirements manually vs. with AI assistance.
- Quality Improved: Analyse defect reduction, stakeholder approvals, or rework reductions.
- Risks Mitigated: Track instances of early risk detection in AI-assisted workflows.
- Implementation Cost: Include AI platform fees, integration efforts, and compliance considerations.
- Training & Oversight Effort: Factor in onboarding and governance costs to ensure responsible AI use.
Management’s Dilemma: How Much to Invest?
Investing in AI-driven requirements management is like choosing between an old map and a cutting-edge GPS for a transatlantic voyage. The more uncertain the waters (complex projects, regulatory constraints, multi-stakeholder environments), the higher the return on investment from a robust AI solution.
However, blindly adopting AI without measuring its utility is akin to overloading a ship with expensive but unused navigation equipment. Leaders must balance cost and benefits by starting with small pilots, defining measurable KPIs, and scaling based on proven utility.
Final Thought: AI as Your Co-Pilot, Not a Replacement
Generative AI should be seen as a co-pilot—augmenting human expertise, not replacing it. The true utility lies in its ability to amplify the skills of engineers, analysts, and managers, making requirements management a strategic advantage rather than an administrative burden.
For readers interested in practical applications of AI in requirements management, the Requirement AI Analyzer by Celeris offers a compelling example. This tool integrates with IBM DOORS Next to automatically assess and score requirements against INCOSE standards, providing real-time feedback and enhancing requirement quality.
