What is AI Use Case Identification?
AI Use Case Identification workshop-based process that generates, evaluates, and prioritizes potential AI applications aligned with business strategy. Structured identification ensures organizations focus on highest-value opportunities rather than technology-led initiatives without clear ROI.
This AI consulting and delivery term is currently being developed. Detailed content covering service models, engagement approaches, deliverables, and selection criteria will be added soon. For immediate guidance on AI consulting services, contact Pertama Partners for advisory services.
Structured use case identification prevents the most expensive AI mistake mid-market companies make: building technically impressive solutions for low-impact problems. Companies using prioritization frameworks deploy their first AI project on the highest-ROI opportunity rather than the most technically interesting one. A 2-day identification workshop costing $5K-15K typically surfaces 3-5 viable use cases worth $50K-500K annually in efficiency gains, paying for itself within the first implemented project.
- Cross-functional stakeholder participation.
- Value estimation framework (cost savings, revenue, risk reduction).
- Feasibility assessment (data, complexity, timeline).
- Prioritization balancing impact and effort.
- Business case development for top use cases.
- Roadmap sequencing considering dependencies.
- Score potential use cases across three dimensions: business impact, data readiness, and implementation complexity to create a prioritized portfolio rather than pursuing scattered experiments.
- Conduct 90-minute workshops with each department head to surface repetitive, data-rich tasks that consume more than 10 hours weekly and follow predictable decision patterns.
- Validate top-ranked use cases against a feasibility checklist confirming available data, measurable success criteria, and executive sponsorship before allocating any development resources.
- Score potential use cases across three dimensions: business impact, data readiness, and implementation complexity to create a prioritized portfolio rather than pursuing scattered experiments.
- Conduct 90-minute workshops with each department head to surface repetitive, data-rich tasks that consume more than 10 hours weekly and follow predictable decision patterns.
- Validate top-ranked use cases against a feasibility checklist confirming available data, measurable success criteria, and executive sponsorship before allocating any development resources.
Common Questions
When should we use consultants vs. build in-house?
Use consultants for strategy, specialized expertise, accelerating initial implementations, and filling temporary capability gaps. Build in-house for long-term competitive differentiation, core capabilities, and maintaining institutional knowledge.
How do we select the right AI consultant?
Evaluate industry expertise, technical depth, implementation track record, cultural fit, and knowledge transfer approach. Request references, review case studies, and assess team composition and engagement model.
More Questions
Strategy engagements: 4-8 weeks. Proof of concept: 6-12 weeks. Full implementation: 3-9 months. Timelines vary based on scope, complexity, and organizational readiness.
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
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Need help implementing AI Use Case Identification?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how ai use case identification fits into your AI roadmap.