In the GenAI journey, identifying the right use cases is where strategy meets execution. It’s the moment when “what’s possible” becomes “what’s valuable.” But not all use cases are created equal—and not all are worth pursuing.
In this post, we’ll walk through a structured approach to identifying, prioritizing, and designing GenAI use cases that deliver real business impact. We’ll also highlight the tangible benefits of doing this well.
Too often, GenAI projects begin with excitement but lack direction. Teams experiment with chatbots, document summarization, or code generation—without a clear link to business goals. The result? Cool demos that never scale.
A well-designed use case answers five critical questions:
Without this clarity, even the most advanced models will underperform.
Start by aligning GenAI opportunities with your organization’s strategic priorities. InCycle’s GenAI Accelerator helps teams filter and generate use cases using three lenses:
A pharmaceutical company might prioritize “automated research reporting” using RAG-Lite to generate narratives from clinical trial data. A bank might focus on “risk assessment and credit scoring” using fine-tuned models to improve lending decisions.
Once you’ve identified potential use cases, evaluate them using a structured scoring model that considers:
InCycle’s accelerator includes a quadrant-based prioritization tool that visually maps use cases by business value and technical feasibility. This helps teams focus on the “sweet spot” where innovation meets impact.
Every use case should be backed by a business case that includes:
A GenAI-powered customer support assistant might cost $223K to develop and $127K per year to operate—but deliver $560K in annual business value, resulting in a 3-year ROI of over $1 million.
Once a use case is prioritized, define the architecture that will support it. Consider:
InCycle’s accelerator includes prebuilt architecture templates for each use case type, including infrastructure-as-code, CI/CD pipelines, and monitoring dashboards.
Before scaling, pilot the use case in a controlled environment. Validate:
Use this feedback to refine the solution before full deployment. GenAI is not a one-and-done project—it’s a continuous learning loop.
In the final post of this series, we’ll show how InCycle’s GenAI Accelerators bring all of this together—helping organizations go from idea to impact with speed, safety, and scale.
NEXT:
Part 5: Accelerating GenAI Adoption with InCycle’s Accelerator Platform
BACK:
Part 3: Platform Engineering: Enabling Scalable and Secure GenAI Innovation