PP&A drafted the interview guide, refined it with the client's research team, and completed ten hour-long interviews between June 25 and June 28, 2024. Six interviewees were technology decision-makers, from a chief data and analytics officer to an AI researcher and an engineering founder. Four were legal leaders, including a deputy general counsel, a chief legal and compliance officer and a privacy attorney. Their organizations spanned AI start-ups, developer software, semiconductors, digital health and electronics manufacturing.

Each conversation moved from current model use to how organizations evaluate and approve models, then to license terms. Two screen-share exercises anchored the discussion. In the first, interviewees picked the two most and two least important of nine selection criteria, from cost and model quality to parent-company reputation and licensing terms. In the second, they compared three anonymized model licenses, one of them the client's, across five terms: indemnity in each direction, commercial restrictions, attribution and acceptable use, then reviewed a sample acceptable use policy. Probes covered pre-approved licenses, limits on commercial use and whether a license that mirrored a standard open-source license would make a model more attractive.

PP&A delivered the results one week after the first draft of the guide as a structured notes workbook rather than a slide deck, with one column per interview and one row per section of the guide, so the client's research team could compare technology and legal views clause by clause.

The client received a notes workbook that set technology and legal views side by side for every section of the interview guide and both exercises. It showed how decision-makers weigh license terms against model quality, cost and reputation, how legal review and pre-approved licenses shape which models teams may use, and how long approval takes. It also showed which clauses create friction for which function, across indemnity, attribution, limits on commercial use and acceptable use, and how interviewees expected the weight of licensing to change as AI regulation and litigation mature.

In general terms, licensing mattered more as a gate in the approval process than as a reason to choose a model. The workbook gave the client's team a clause-by-clause view of how its terms read to technology and legal buyers as it worked to raise adoption of the model.