AI Use Case Discovery Tool
A GTM enablement project bridging the disconnect between partner AI use case marketing claims and real-world field execution through a NotebookLM discovery tool.
01
The Challenge
There is a significant discrepancy between the exhaustive AI capabilities our partners market on their websites and the use cases they are actively investing and selling in the field.
Red Hat's partners (Cisco, Dell, Lenovo, etc.) market broad AI capabilities on paper, but sellers had no reliable way to know which use cases actually had real field investment, dedicated resources, and repeatable GTM motions behind them. This gap led to wasted sales effort, misaligned product assumptions, and missed opportunities to position Red Hat's AI portfolio credibly in customer conversations.
02
Discovery & Insights
Going Straight to the Field
Interviews with PAMs and partner leaders confirmed a real disconnect. I mapped it into a formal inventory: Partner Hierarchy, NVIDIA Focus Areas, Target Vertical, Certified Hardware, Deployment Architectures (RHAIE), and Pre/Post-Sale GTM strategy, turning scattered claims into something sellers could query.
Financial Services Deepdive
To stress-test the framework, I applied it to a regulated, high-stakes vertical: fraud detection. I traced the full path from partner hardware (Cisco, Lenovo, Dell AI Factory) through MLOps platforms (OpenShift AI, vLLM) to the customer-facing application, confirming the inventory held up against real deployment complexity, not just a hypothetical.
An Unmaintainable Database is Worthless
The inventory only mattered if sellers actually used it, before a call or as active research, not buried in a shared drive. That reframed the goal: not a database, but a tool designed for seller adoption.
03
Design Process
04
Reflections
Stepping outside my Computer Science & Design Comfort Zone.
Coming from a CS and Design background, I had to learn how enterprise go-to-market strategy actually operates: how partners structure sales motions, how hardware and platform decisions ripple into deployment, and how sellers make decisions in real time. That shift in perspective shaped every part of the project. NotebookLM was also new territory, and getting it to turn messy field notes into clean, structured data took real iteration on prompt design. I learned to treat prompting as its own design problem, not just a technical shortcut. Overall, I enjoyed putting on a new hat (pun intended) and learning how buisness needs operate in the the product cycle