How to to Identify AI Use Cases

Many organizations are rushing to adopt the latest AI technology trends, only to struggle with unclear outcomes, wasted resources, or poor adoption. The key to success lies in identifying the right AI use cases; those that deliver measurable business value, align with strategy, and can be supported by the right data.

Figure Out the Problem You’re Trying to Solve

One of the most common mistakes is starting with AI as the goal rather than the solution. Instead, organizations should ask themselves:

• What are our biggest challenges?
• Where do inefficiencies or risks exist today?
• Which business outcomes do we want to improve?

By starting with clearly defined business goals, you can align AI opportunities with measurable impact rather than chasing hype.

Prioritize Use Cases by Feasibility and Impact

Not every AI use case is equally achievable. Evaluate each opportunity based on:

• Business impact: Will this improve revenue, reduce costs, or minimize risks?
• Data readiness: Do you have clean, accessible, and sufficient data to train AI models?
• Technical feasibility: Is the process measurable and digital and trainable, or does it rely heavily on human judgment?
• Adoption potential: Will employees and customers embrace the solution?

An impact matrix is a helpful tool for visualizing and prioritizing potential projects.

Engage Stakeholders Across the Organization

AI adoption is more than an IT project; it requires input and buy-in from across the business. To successfully deploy an AI project, organizations will want to foster collaboration between:

• Executives to ensure alignment with strategic objectives
• Line-of-business leaders to identify pain points and opportunities
• End users to understand workflows and adoption challenges

This cross-functional approach ensures that AI use cases are relevant, actionable, and supported at every level.

Don’t Overlook Compliance and Risk Management

AI use cases that handle sensitive data (e.g., healthcare, financial services, or legal) must be evaluated through the lens of compliance and governance, such as:

• Data privacy regulations (GDPR)
• Industry-specific security requirements (HIPAA, PCI-DSS)
• Explainability and transparency to maintain trust

Keeping compliance top-of-mind early in the process prevents rework and builds confidence in AI systems.

Pilot, Measure, and Scale

Once high-potential AI use cases are identified for your organization:

• Start with a pilot project to test feasibility.
• Measure success with clear KPIs such as cost savings, reduced downtime, increased productivity or improved response times.
• Scale successful projects across departments or geographies.

This approach balances innovation with risk management, ensuring that AI investments pay off.

How Thrive Can Help

At Thrive, we help mid-market organizations support their AI-based business goals. Our NextGen managed AI, security services and advisory teams guide clients through the process of evaluating, prioritizing, and implementing AI use cases, ensuring these decisions are grounded in security, compliance, and business outcomes. With a strong information architecture and data governance foundation, we help ensure your organization’s AI initiatives are scalable, cost-effective, and future-ready. Contact Thrive today to learn more about identifying use cases for your AI initiatives.