So often, a single product manager becomes the linchpin for whether a trajectory-changing GenAI-powered feature gets shipped at a company—or not.
In our quest to research and launch Verta's GenAI Workbench platform, I've engaged with dozens of product managers exploring or deeply entrenched in the AI space. The role of the product manager is swiftly evolving, especially with the widespread adoption of generative AI features.
Many product managers are transitioning into generative AI prototypers and prompt engineers—a trend observed not only in machine learning companies but also across diverse organizations seeking a competitive edge through AI integration.
Here’s how product managers shape the destiny of GenAI-powered features:
Rapid Ideation
Most PMs listen to customer feedback, envisioning how a model might solve their problem. They leverage Chat-GPT's webUI to swiftly qualify or dismiss their ideas. Product managers validate AI concepts by experimenting, capitalizing on their understanding of customer pain and knowledge of emerging technology applicable to their business.
Prototype Hardening
No company ships a GenAI feature without trust in its scalability. Proving the solution's robustness demands focused efforts, resources, and time.
PMs immerse themselves in spreadsheets and data, iterating on prompts and QAing results until a solid solution is found. This meticulous process, often involving hundreds of iterations, is led by the PM, ensuring the solution is refined while the team implements the UI and APIs. Especially in a company with limited data science resources it is the PM who is best positioned to iterate on refine prompts.
Internal Advocacy
PMs play a crucial role in persuading technical and leadership teams of the viability of generative AI solutions. Stakeholders may be cautious due to operational risks, but PMs understand the risks competitors are willing to take.
The GenAI prototyper, typically a PM, knows where to apply GenAI successfully, meeting KPIs and business goals, swiftly proving or disproving hypotheses.
Model/Vendor Selection
Proving GenAI's suitability involves meeting quality requirements and choosing models/vendors to fulfill company needs. From trust and safety to legal and regulatory requirements, PMs tackle challenges, researching open source alternatives, conducting pricing comparisons, and analyzing build-vs-buy scenarios.
User-Centric Development
Generative model outputs can be unpredictable, requiring validation for real customer value. PMs uniquely validate model outputs and set release criteria for GenAI-powered features, collaborating with customer success and QA teams. They monitor user reactions, iterating and refining features. Ultimately they will say “go” or “no go” as to whether a feature is shipped or benched.
The Verta Workbench, born from product managers immersed in debates over model outputs, is the tool I wished for to ideate, iterate, and ship swiftly.
Product managers, if you're contemplating or using generative AI, explore the GenAI Workbench. It can transform your ideas into real-data prototypes in just a few hours. Try it out, share your thoughts—your journey to leveraging generative AI is closer than you think!
It won’t happen without you.
Learn more
For an in-depth look at our capabilities, you can read our full launch blog post and check out the platform here.