An AI research lab had world-class model training talent but no dedicated product function, leaving decisions about which capabilities to prioritize, how to price API access, and what to optimize for made ad hoc by whichever research team was loudest.
Standing Up a Foundation Model Product Practice from Scratch
Service Provided
Capabilities Utilized
Foundation Model Product Management, Operating Model Design, Metrics Strategy
Client challenge
Model releases were being scoped by research priorities alone, with no systematic input on what capabilities customers actually needed, how competitors were pricing comparable access, or what latency and cost tradeoffs mattered to different customer segments. Two recent releases had shipped capabilities customers didn’t ask for while missing ones they had been requesting for months.
Our solution
We stood up a foundation model product management practice with clear ownership over the roadmap prioritization process, customer feedback intake, and the cost-latency-quality tradeoff decisions that research teams had previously made in isolation. Product managers were embedded directly alongside research leads, rather than sitting above them, to keep decisions grounded in technical reality.
We introduced a lightweight customer advisory input process — recurring structured conversations with a rotating panel of API customers — to make sure roadmap prioritization reflected actual customer demand rather than internal assumptions about what customers wanted.
Results
The next model release, shaped by the new product practice’s prioritization process, addressed the top three most-requested capabilities from the customer advisory panel and saw a 3.1x increase in API adoption among existing customers compared to the prior release. Internal research teams reported the embedded product management model reduced re-work from misaligned priorities by an estimated 25%.
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