The Top 3 outcomes cited across respondents from Propel about model context protocol (MCP) adoption are improved employee productivity (35%); faster decision-making (34%); and faster product development (31%).
"This research shows many manufacturers have already identified what they want AI to do," says Ross Meyercord, CEO of Propel. "Organizations that start from a connected product, quality, and content foundation remove the first and most time-consuming obstacle of making their data usable for AI. Propel and Salesforce take care of the rest with a unified data structure and AI being an integral part of our platform."
Key takeaways:
· Product management's top use cases center on a consolidated view that explicitly includes quality status alongside requirements, bills of materials, open changes, and ownership.
· When asked to rank the product lifecycle stages where connected AI would have the greatest impact, respondents placed quality management second, behind only product design and engineering. 69% of product management respondents also rated the ability to have an AI agent reach across PLM, CRM, ERP, and market data to surface product, customer, and competitive information in one place as extremely or somewhat valuable.
· 95% rated the ability to securely connect approved AI tools to PLM and QMS, with access controls and audit logging, as extremely or somewhat valuable. 86% said the same about replacing point-to-point integrations with a single, standardized MCP interface, and 84% valued the ability to govern what AI can access and act on from one place.
· For IT teams being asked to enable use cases across an organization, a standardized integration layer is what makes that scale achievable without multiplying complexity.
· 85% of marketing teams ranked AI assistance pulling engineering, quality, and product details together for launch materials as extremely or somewhat valuable.
