What is AI Use Case?
An AI Use Case is a specific, well-defined business scenario where artificial intelligence can be applied to solve a problem or create value, describing the target process, the AI technique involved, the expected outcomes, and the measurable business impact it aims to deliver.
What Is an AI Use Case?
An AI Use Case is a specific application of artificial intelligence designed to address a particular business problem or opportunity. It is more than a vague idea like "use AI for marketing." A well-defined AI use case specifies exactly what problem is being solved, what data is needed, what AI technique will be applied, and what measurable business outcome is expected.
For example, "use AI for marketing" is not a use case. "Deploy a machine learning model that predicts which existing customers are most likely to purchase a premium upgrade in the next 90 days, increasing upsell conversion rates by 15 percent" — that is an AI use case.
Anatomy of a Well-Defined AI Use Case
A robust AI use case document should include:
Business Context
- Problem statement — What specific business challenge does this address?
- Current process — How is this handled today, and what are the pain points?
- Stakeholders — Who benefits from this solution, and who needs to be involved?
Technical Definition
- Data requirements — What data is needed, where does it come from, and is it available?
- AI approach — What type of AI technique is appropriate (e.g., classification, prediction, NLP)?
- Integration points — How will the AI system connect with existing tools and workflows?
Expected Outcomes
- Key metrics — What specific KPIs will improve, and by how much?
- Timeline — When will results be measurable?
- Success criteria — What defines success versus failure for this use case?
Feasibility Assessment
- Data readiness — Is the required data available and of sufficient quality?
- Technical complexity — How difficult is this to build and deploy?
- Organizational readiness — Is the team prepared to adopt this solution?
How to Identify AI Use Cases
Finding the right use cases is one of the most critical steps in AI strategy. Here are proven approaches:
Process Mining
Walk through your major business processes and look for:
- Repetitive tasks that follow consistent patterns
- Decisions based on data that humans currently make manually
- Bottlenecks where processing speed limits business performance
- Error-prone steps where mistakes are common and costly
- Prediction needs where knowing the future would create significant value
Pain Point Analysis
Interview stakeholders across the organization and ask:
- What tasks consume the most time with the least strategic value?
- Where do you wish you had better predictions or insights?
- What customer complaints could be reduced with faster or smarter processes?
- What data do you have that nobody is using?
Competitive Benchmarking
Research what competitors and industry leaders are doing with AI:
- What AI capabilities do competitors advertise or discuss?
- What AI applications are common in your industry globally?
- Where could AI give you an advantage that competitors have not yet pursued?
AI Use Case Prioritization
Not all use cases deserve immediate investment. Prioritize based on:
- Business impact — How much revenue, cost savings, or competitive advantage will this deliver?
- Feasibility — How complex is the technical implementation, and is the data ready?
- Strategic alignment — Does this support your company's broader goals?
- Time to value — How quickly will this deliver measurable results?
- Risk — What could go wrong, and what is the downside?
A common framework is the 2x2 matrix plotting business impact against implementation feasibility. Start with use cases in the "high impact, high feasibility" quadrant.
High-Impact AI Use Cases for Southeast Asian SMBs
Based on the common needs of businesses operating in ASEAN markets:
- Multilingual customer support chatbots — Handle inquiries in local languages 24/7
- Demand forecasting — Predict product demand across diverse and seasonal markets
- Invoice and document processing — Automate data extraction from invoices, receipts, and forms
- Customer churn prediction — Identify at-risk customers before they leave
- Dynamic pricing — Adjust prices based on demand, competition, and market conditions
- Quality inspection — Use computer vision for manufacturing quality control
- Recruitment screening — Automate initial resume filtering for high-volume hiring
- Fraud detection — Identify suspicious transactions in payment and lending operations
Common Mistakes in Use Case Selection
- Choosing use cases based on technology appeal rather than business impact
- Selecting use cases where data does not exist or would be too expensive to collect
- Starting with the hardest, most ambitious use case instead of building momentum with achievable wins
- Ignoring the human element by choosing use cases that require significant behavioral change without planning for it
- Failing to define success metrics upfront, making it impossible to evaluate whether the project delivered value
Selecting the right AI use cases is the most consequential decision in your AI journey. CEOs who invest in the wrong use cases waste money and organizational patience, making future AI initiatives harder to fund and support. Conversely, starting with the right use case can create a virtuous cycle of success that builds momentum, confidence, and capability for increasingly ambitious AI applications.
For CTOs, use case selection determines whether the technical team builds something the business actually values. The most technically sophisticated AI system is worthless if it solves a problem nobody cares about. The CTO's role is to translate business needs into technically feasible use cases and to honestly assess whether the organization has the data and infrastructure to succeed.
In Southeast Asian markets, where AI adoption is still relatively early among SMBs, choosing the right first use case is especially important. A successful initial project can position your company as an AI leader in your segment, attract talent, and create competitive advantages that compound over time. A failed first project, on the other hand, can set your AI ambitions back by years.
- Always start with the business problem, not the AI technology — ask what needs to improve, not what AI can do
- Prioritize use cases using a structured framework that balances business impact, feasibility, and data readiness
- Define clear, measurable success criteria before beginning any use case development
- Choose your first use case carefully — it should be achievable, visible, and clearly valuable to build organizational confidence
- Involve both business stakeholders and technical teams in use case identification and prioritization
- Maintain a backlog of potential use cases ranked by priority so you always know what to work on next
- Revisit and reprioritize your use case backlog quarterly as business needs and capabilities evolve
Frequently Asked Questions
How many AI use cases should we pursue at once?
For most SMBs, start with one to two use cases simultaneously. This allows you to focus resources, learn effectively, and build capability before scaling. Larger organizations with dedicated AI teams might handle three to five concurrent use cases. The key principle is that it is better to succeed with one use case than to half-complete five.
What makes a good first AI use case?
The ideal first use case has four characteristics: high business impact that stakeholders care about, sufficient available data to train a model, relatively low technical complexity, and a clearly measurable outcome. Common strong first use cases include customer service chatbots, document processing automation, and demand forecasting. Avoid use cases that require massive data collection efforts or significant organizational change as your first project.
More Questions
For most SMBs, start with off-the-shelf or pre-built AI solutions wherever possible. These are faster to deploy, less expensive, and lower risk. Custom AI development makes sense when your use case is truly unique, when off-the-shelf solutions do not meet your specific requirements, or when the use case involves proprietary data that gives you a competitive edge. Many successful companies use a hybrid approach — off-the-shelf tools for common needs and custom models for differentiated capabilities.
Need help implementing AI Use Case?
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