How to Identify High-Value AI Use Cases in Your Business
Deliberate Academy Editorial Team
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- Apply the four-dimension use case evaluation framework to score and rank candidate AI use cases in your organization
- Explain why multiplication produces a more strategically honest ranking than addition when scoring use cases across dimensions
- Identify which use case categories typically score highest across all four dimensions for most organizations
- Recognize the three types of use cases to avoid as a first pilot and articulate the reason each is unsuitable
Your AI strategy working group has been meeting for two months. You have reviewed vendor demos, read the case studies, and agreed that AI is an important priority. You also have a list of 27 potential AI use cases that various teams have submitted. Some are genuinely exciting. Some are solutions looking for problems. Some are technically straightforward but strategically irrelevant. And nobody has a clear basis for deciding which to pursue first. A use case identification and prioritization framework turns that list from a source of disagreement into a prioritized roadmap.
Why Use Case Selection Is the Most Important Strategic Decision
The AI use case you select first sets several consequential precedents. It determines which team's workflows are disrupted and thus which relationships you need to manage. It determines the technical infrastructure you invest in initially, which creates path dependencies for subsequent use cases. It determines the first AI success story — or failure story — that the organization will form its assumptions around.
Leaders who select first use cases based on technical novelty, vendor recommendation, or whichever team was most vocal about wanting AI frequently find that their pilot either fails or succeeds in a way that does not transfer meaningful learning to the rest of the organization. Use case selection should be strategic, not opportunistic.
The Use Case Evaluation Framework
Rate each potential use case across four dimensions.
1. Business impact potential
How much value does solving this problem create? Measure in terms that matter to your organization: cost reduction (annual hours saved or cost eliminated), revenue impact (conversion improvement, churn reduction, upsell rate), quality improvement (error rate reduction, output consistency), or strategic positioning (capability that creates competitive differentiation). Be specific. "Reduces analyst time on report preparation" is less useful than "reduces an estimated 15 hours per week of analyst time at a cost of approximately $50,500 per year."
2. Feasibility
Can current AI technology actually solve this problem reliably enough to be useful? This requires knowing the type of task: document summarisation, text classification, and structured data extraction are well-proven. Real-time physical world interaction, highly regulated advice, and tasks requiring perfect accuracy are poorly suited to current AI. Feasibility also includes data feasibility — does the data the AI would need exist, is it accessible, and is it of sufficient quality?
3. Organizational readiness
Does the team that would use this solution have the skills and process maturity to adopt it? A technically feasible use case deployed into a team without AI literacy or clearly documented processes will underperform. Include in readiness assessment: the target team's openness to change, their current process documentation quality, and whether they have a champion who will drive adoption.
4. Strategic alignment
Does this use case help the organization deliver on its strategic priorities? AI use cases that solve real problems but are strategically peripheral are less valuable than use cases that directly support what the organization is trying to achieve. A use case that helps a strategic growth initiative is worth more than an equally good use case that improves a non-strategic function.
Score each use case on these four dimensions using a simple 1-5 scale. Multiply the scores together rather than adding them. Multiplication means that a use case scoring 5 on impact but 1 on feasibility ranks lower than one scoring 3 on all four dimensions — which correctly reflects the reality that a great idea that cannot be executed is worth less than a good idea that can be.
A use case scores 5 on business impact, 5 on strategic alignment, 1 on feasibility, and 3 on organizational readiness. A second use case scores 3 on all four dimensions. Which ranks higher under the evaluation framework, and why?
Select one answer.
Use Case Categories Worth Prioritizing
Different types of use cases tend to cluster at different levels on the evaluation framework. Some categories almost always score highly across all four dimensions for most organizations.
Document processing and information extraction: Converting unstructured documents (contracts, reports, correspondence) into structured data or summaries. High impact (saves significant analyst and administrative time), high feasibility (well-proven AI capability), and deployable without significant process redesign.
Internal knowledge search and Q&A: Building AI-assisted search over your internal documentation, policies, and knowledge bases. Allows employees to find answers to questions that currently require colleague time. Relatively low data risk (internal documents rather than customer data) and high adoption potential.
Content drafting and editing support: Supporting any team that produces significant volumes of written content with AI drafting assistance. Marketing, HR, communications, and operations teams typically qualify. Impact depends on volume and the current quality gap between output and standard.
Customer communication triage and routing: Using AI to classify incoming customer communications by type and urgency, routing them appropriately before human response. High impact on response times and resource allocation; moderate complexity depending on your communication channels.
Data analysis and commentary generation: Using AI to generate narrative commentary on quantitative data — performance reports, financial summaries, operational metrics. High impact for teams that spend significant time writing around numbers; manageable data risk when the AI is working with aggregated rather than individual-level data.
What to Avoid in First Use Case Selection
Avoid use cases that require perfect accuracy. No current AI system has a zero error rate. Use cases where errors are catastrophic — legal advice, medical recommendations, safety-critical decisions — are poor first choices. Choose use cases where errors are reviewable and the cost of a mistake is manageable.
Avoid use cases with unresolved data privacy issues. If the use case requires AI processing of personal data, patient data, or confidential client information, and your legal and compliance teams have not yet reviewed the implications, this is not your first use case. The legal review takes time. Start with use cases where the data questions are already resolved.
Avoid use cases that depend on a vendor's roadmap rather than current capability. A vendor demonstrating a capability that is "coming soon" or "in beta" is showing you a use case for the future, not the present. Your first use case should be based on proven, current capability.
The most expensive AI investments are pilots that are technically successful but deliver no measurable business value. Define your value metrics before you begin, not after. If you cannot articulate how you will measure whether this use case was worth pursuing within six months, it is not a well-enough-defined use case to fund.
Building the Prioritized Use Case List
Score your candidate use cases across the four dimensions, multiply the scores, and rank the results. The top three to five use cases on the ranked list are your initial candidates for the first pilot.
From those candidates, select one or two for a first pilot based on: which has the clearest success metrics, which has the most willing and capable team to implement with, and which has the fewest unresolved blockers.
The goal is a first pilot that succeeds. A well-executed pilot on a moderately high-ranked use case is more strategically valuable than a struggling pilot on a perfectly scored one.
Scoring Out a 27-Item Use Case List — Professional Services
Context
A Head of Digital Transformation had inherited a sprawling list of 27 AI use case suggestions gathered from business unit leads over four months. The list ranged from genuinely compelling opportunities to technically infeasible ideas, with no shared basis for prioritization. Leadership expected a credible recommendation within six weeks.
Action
She ran a structured scoring workshop with representatives from IT, operations, and three business units. Each use case was scored across the four dimensions — business impact, feasibility, organizational readiness, and strategic alignment — on a 1-5 scale, with scores multiplied. The scoring immediately collapsed the list: 11 use cases scored below 40, almost all due to a feasibility score of 1 or 2. A second pass identified three use cases that scored consistently across all four dimensions, all in the document processing and content drafting categories.
Outcome
The firm entered its first AI pilot with a clear rationale for the two use cases selected, which could be explained to any stakeholder in under a minute. Both pilots delivered measurable results within the projected timeframe, in part because the feasibility dimension had ensured no pilot depended on unproven AI capability.
Why should you multiply use case evaluation scores across the four dimensions rather than adding them?
Select one answer.
Exercise
Your Task
Take the list of AI use cases your organization or team has informally discussed — if you do not have one yet, generate five plausible candidates for your function. Score each on the four dimensions (business impact, feasibility, organizational readiness, strategic alignment) using a 1-5 scale and multiply the scores. Write down which use case ranks first and whether that result surprises you. If it does not match the informal consensus in your organization, identify which dimension is causing the discrepancy.
Your reflection
Did you complete this exercise? What did you find? (Saved locally in your browser)
- First use case selection sets consequential precedents for technology investment, team relationships, and organizational learning — it should be strategic, not opportunistic or driven by whichever team was most vocal.
- Evaluate use cases on four dimensions: business impact potential, feasibility, organizational readiness, and strategic alignment — multiply scores rather than adding them so that near-zero feasibility correctly tanks the overall ranking.
- High-value categories for most organizations include document processing, internal knowledge search, content drafting support, customer communication triage, and data commentary generation.
- Avoid first use cases requiring perfect accuracy, those with unresolved data privacy issues, and those dependent on vendor capabilities that are not yet proven in production.
- Define value metrics before the pilot begins — if you cannot articulate how you will measure success within six months, the use case is not well enough defined to fund.