A Propel Briefing Note
Axendia was briefed by Erin Keefe, Director of Marketing and Communications, Tom Shoemaker, VP Product Marketing, and Chuck Serrin, VP Industry Marketing at Propel Software. The company is a cloud-native, Product Lifecycle Management (PLM) and QMS provider built on the Salesforce platform, focused on discrete manufacturers, with a strong presence in the medical device industry. Propel has over 200 customers and continues to expand its footprint as organizations modernize legacy PLM and QMS environments.
The Propel team outlined how they are advancing PLM beyond traditional system boundaries by leveraging a unified product thread, agentic AI, and a cloud-native architecture to support increasingly complex, cross-functional product development environments in life sciences. As Keefe noted, “Propel is a disruptor in the space… we’re focused on fast-paced manufacturing cycles where customers are really concerned about speed to market and quality.”
Addressing the Design Data Dilemma
Propel’s Design Hub was introduced to address the challenges in managing engineering data across distributed teams and toolsets.
Propel highlighted a “design data dilemma” characterized by version confusion, disconnected MCAD and ECAD environments, and limited visibility across the enterprise. “These issues often lead to rework, delays, and increased risk, particularly in regulated environments where traceability and change control are essential,” explained Serrin. Design Hub introduces a hub-and-connector model that integrates data from multiple design systems into a unified product thread. This enables stakeholders to review changes in the context of the entire product before committing them.
Serrin described the approach as “a really elegant solution… customers are loving this,” particularly due to its ability to support multiple CAD and PDM environments without requiring organizations to standardize on a single tool.
Propel is not attempting to replace engineering or simulation platforms. As Shoemaker explained, “our philosophy is the right tool for the right job… ours is not the system to conduct the analysis.” Modeling and simulation remain in specialized systems, while Propel focuses on orchestrating lifecycle processes and managing product data across domains.
Announcing Propel One
Propel’s second major innovation, Propel One, introduces agentic AI everywhere into this enterprise platform. While many vendors remain focused on generative AI for content creation and insights, Propel is emphasizing execution within operational workflows.
As Keefe noted, “everyone is talking about AI,” but the focus here is on applying it to drive tangible business value. The platform is designed to “turn reasoning into execution through agents,” enabling users “to do things, not just answer questions.” This reflects a shift toward embedding AI directly into enterprise processes where it can support and automate work across the product lifecycle, rather than treating it as a standalone capability.
The team demonstrated an AI-driven, training automation use case. Propel One can generate structured training quizzes directly from product documentation by automatically identifying key concepts, creating multiple-choice questions, and providing validated answers grounded in enterprise data. This addresses a common challenge in life sciences: maintaining consistent, up-to-date training aligned with evolving product and quality documentation.
By automating quiz generation and linking it to controlled content, organizations can reduce manual effort while improving training accuracy and compliance. Additional use cases span quality and product processes, including supporting change management workflows and summarizing quality events, generating document descriptions, and assisting with item and revision management across the lifecycle.
From Axendia’s perspective, this shift ultimately enables Product Lifecycle Intelligence (PLI) where connected product, quality, and process data support better, faster decision-making across the lifecycle. Disconnected systems remain a key bottleneck for many organizations. Companies that establish a unified data foundation are better positioned to leverage AI and turn data into intelligence.
The Importance of the Product Thread
A central theme of the briefing was Propel’s effort to redefine PLM as a broader “product value management” platform and extending beyond engineering into quality, regulatory, commercial, and service domains.
Traditional PLM and QMS systems often operate in silos, creating gaps in visibility and coordination. As Keefe explained, “We’re not your parents’ PLM or QMS solution,” underscoring the limitations of legacy approaches that fail to span the full product lifecycle.
Propel’s approach centers on a unified product thread that connects product data, processes, and stakeholders. This foundation not only improves cross-functional collaboration but also enables new capabilities through AI.
This is particularly critical for AI since these systems depend not just on access to data, but on context, structure, and meaning. Without a coherent product data model, AI outputs risk being incomplete, inconsistent, or unreliable.
Propel’s architecture is built on the Salesforce platform, enabling AI to operate on enterprise data with appropriate governance, auditability, and security controls. For life sciences organizations, this supports compliant use of AI, improves traceability, and helps ensure that outputs align with regulatory expectations.
In Brief
The briefing highlighted several broader trends shaping the future of PLM in life sciences.
The convergence of PLM, QMS, and related systems is accelerating, driven by the need for integrated workflows and end-to-end visibility. At the same time, PLM is evolving into a system of coordination that connects data, processes, and stakeholders across the lifecycle.
Agentic AI is emerging as a key enabler, particularly in areas where automation can reduce manual effort and improve consistency. As discussed during the briefing, this creates opportunities for AI agents to help ensure that processes are completed correctly and that critical steps are not missed.
These advances also introduce new challenges. In regulated environments, organizations must ensure that AI-driven processes remain transparent, auditable, and aligned with validation and compliance requirements.
As life sciences organizations continue to navigate increasing product complexity, regulatory scrutiny, and digital transformation pressures, platforms like Propel are positioning themselves at the intersection of product data, process orchestration, and AI.
The opportunity lies in translating these capabilities into measurable business value while maintaining the trust, control, and governance of AI technologies as required in regulated environments.
We will continue to provide updates on Propel as they become available.
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Axendia’s Related Content
- Axendia 2026 Life Sciences Radar
- Does MedTech Need Product Value Management?
- eBook: Are You on the Right Side of the Digital Divide?
The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.