What is AI Use Case Discovery?
AI Use Case Discovery is the systematic process of identifying and validating problems where AI can deliver significant value. It involves analyzing user workflows, identifying repetitive or data-intensive tasks, evaluating AI feasibility, and prioritizing opportunities based on impact and implementation complexity.
This glossary term is currently being developed. Detailed content covering implementation approaches, best practices, common challenges, and business applications will be added soon. For immediate assistance with AI product management, please contact Pertama Partners for advisory services.
Systematic use case discovery prevents the common failure pattern of pursuing technically impressive AI projects that solve problems nobody actually has, wasting $50,000-300,000 per misguided initiative. Companies with structured discovery processes select use cases that deliver 3-5x higher ROI than ad hoc project selection methods. Discovery workshops also build organizational AI literacy by educating department leaders about realistic capabilities and constraints.
- Should focus on high-frequency, high-impact tasks where AI can provide 10x improvement
- Must assess data availability and quality required to build effective AI solutions
- Requires evaluating whether simpler non-AI solutions could solve the problem adequately
- Should consider ethical implications and potential for bias in each use case
- Must validate that users would trust AI to perform the identified task
- Conduct structured discovery workshops with frontline operators rather than relying solely on executive assumptions about where AI creates the most operational value.
- Score discovered use cases across data availability, technical feasibility, business impact, and organizational readiness using weighted evaluation matrices.
- Validate top-ranked use cases through rapid prototyping sprints lasting 2-4 weeks before committing to full development investment on any single opportunity.
- Conduct structured discovery workshops with frontline operators rather than relying solely on executive assumptions about where AI creates the most operational value.
- Score discovered use cases across data availability, technical feasibility, business impact, and organizational readiness using weighted evaluation matrices.
- Validate top-ranked use cases through rapid prototyping sprints lasting 2-4 weeks before committing to full development investment on any single opportunity.
Common Questions
How does this apply to AI products specifically?
AI products have unique characteristics including model uncertainty, data dependencies, and evolving capabilities that require adapted product management approaches.
What skills do product managers need for AI products?
AI product managers need technical literacy in ML concepts, data strategy skills, the ability to set realistic expectations, and expertise in iterative product development.
More Questions
Success metrics for AI features include model performance metrics (accuracy, precision, recall), user experience metrics (task completion, satisfaction), and business impact metrics (efficiency gains, cost reduction).
References
- NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology (NIST) (2023). View source
- Stanford HAI AI Index Report 2025. Stanford Institute for Human-Centered AI (2025). View source
AI Product Management is the discipline of defining, building, and launching AI-powered products requiring unique skills in balancing probabilistic behavior, managing model performance, handling bias and fairness, and designing for continuous learning.
AI Product Strategy is a comprehensive plan defining how artificial intelligence capabilities will deliver user value and business outcomes. It identifies which problems AI can uniquely solve, target user segments, competitive positioning, and a roadmap for AI feature development aligned with organizational goals.
AI Product Vision is an inspirational description of the future state where AI-powered capabilities transform how users accomplish their goals. It articulates the unique value proposition of AI features, the user problems being solved, and the long-term impact on customer experience and business value.
AI-First Product Design is an approach where artificial intelligence capabilities are fundamental to the product experience, not add-on features. Products are designed around what AI can uniquely enable, with user interfaces, workflows, and value propositions built specifically to leverage machine learning capabilities.
AI Value Proposition is a clear statement of the specific benefits users gain from AI-powered features, articulated in terms of time saved, quality improved, insights gained, or new capabilities unlocked. It explains why AI is the right solution for the user's problem and what makes it better than alternatives.
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