How AI and PLM can help close the gap between product and commercial teams [Q&A]

The promise of AI is undeniable. The technology offers transformative potential to increase productivity, accelerate time to market and optimize customer experiences — all critically important for product manufacturers.

Agentic AI in particular can be a powerful asset. These systems don’t just assist; they act autonomously, streamlining workflows, detecting anomalies, and proactively solving problems in real time.

When manufacturers use AI along with product lifecycle management (PLM), which breaks down traditional silos and enables cross-functional engagement, they can maximize the lifetime value of their products with insights, collaboration, and engagement across all stakeholders.

We spoke with with Propel Software CEO, Ross Meyercord, to discuss how AI and PLM can be used in product manufacturing to close the gap between product and commercial teams to drive positive business impact

BN: How are product manufacturers currently using AI, and what are the risks for those manufacturers not leveraging the technology?

RM: The AI window has recently opened and companies are beginning to see real results. Manufacturers who seize the AI opportunity today are positioning themselves to greatly impact nearly every business function, prompting new growth, reduced overhead, and sharper competitiveness.

Our recent survey of 800 US employees across industrial equipment, medical device, high tech, and consumer goods manufacturers found 65 percent are using AI in their product operations and seeing success. Specifically, 51 percent in high tech, 32 percent in industrial equipment, 26 percent in consumer goods, and 24 percent in medical devices are actively using AI and expanding investment. Another 34 percent of consumer goods, 33 percent of medical devices, 32 percent of industrial equipment and 31 percent of high tech are piloting or experimenting with AI in limited areas.

Those respondents actively using AI reported the following results:

• Productivity gains — 52 percent
• Achieving a competitive advantage — 50 percent
• Technology consolidation — 35 percent
• Ability to reallocate resources — 32 percent
• Direct expense reduction — 28 percent and
• Reduced headcount — 23 percent

The companies still sitting on the sidelines are not just missing opportunities, they’re ceding market position to competitors who moved first.

BN: How can these businesses use AI in conjunction with product lifecycle management (PLM), and what are the benefits of doing so across business functions?

RM: AI’s effectiveness depends on having digitized, connected product data. In many product companies, critical information remains siloed, trapped in spreadsheets, disconnected CAD systems, or isolated business applications, making it invisible to AI and limited cross-functional decision making.

These data domains shouldn’t exist in isolation. Information needs to be digitized and connected allowing AI to link insights from supply chain, quality, marketing, field service, and other functions into a single, intelligent product thread.

Cloud-native, single-platform PLM solutions change this equation entirely. They create that product thread that connects upstream requirements and engineering with downstream sales and service data. When engineering needs field service feedback to eliminate faulty components, it’s available. This connected, digitized data set is the key to unlocking powerful AI results across product companies.

Together, AI and PLM provide broader access to product data. AI-driven PLM like Propel Software built on Salesforce seamlessly connects every stage of the product lifecycle. With connections between engineering through to sales and service, barriers are removed to provide real-time, role-specific insights that impact the bottom line, enabling teams to design, source, and deliver products faster and more efficiently.

Non-technical users can ask questions and instantly receive tailored, task-specific insights. This empowers more team members to use product data effectively, leading to better decisions across the organization and stronger business outcomes.

BN: What role can agentic AI play in this?

RM: Think of agentic AI like having an engineer who never sleeps. It’s continuously monitoring your supply chain data, flagging component issues before they become recalls, and automatically triggering change orders. They’re intelligent by design, capable of anticipating problems and continuously adapting to new conditions. The result is greater operational resilience, faster decision-making, and enhanced business agility.

By embedding sophisticated AI directly within everyday workflows, manufacturers can transform decision-making processes into something more dynamic, informed, and precise. Instead of spending valuable time sifting through data or managing tedious tasks, teams gain the ability to focus on strategic objectives, innovation, and oversight. AI agents serve as trusted collaborators anticipating needs, streamlining tasks, and delivering results swiftly, reliably, and without friction.

Looking ahead, the real breakthrough lies in orchestrating agentic AI agents across multiple applications. This is where major application platform providers like Salesforce are positioned to lead. The ability to build, deploy, and coordinate agents across a suite of applications within a unified platform will be a game changer.

BN: To deploy agentic AI effectively, what foundation do manufacturers need?

RM: Successful AI deployment isn’t about technology, it’s about architecture and governance. Organizations that get this right follow these five steps, built on a foundation of trust:

• Define clear roles and ‘jobs to be done’ for each agent.
• Provide secure access to the right data streams, this access should mirror the application level access human users have.
• Establish specific actions the agent can take with measurable outcomes.
• Leverage built-in controls and instructions that tune actions to business needs to maintain the desired outcomes while also maintaining compliance, security, and ethical standards.
• Integrate agents into existing workflows to work alongside the human employees.

The companies that skip these fundamentals end up with AI projects that never leave a prototype stage or deliver minimal impact. The ones that get it right will see immediate productivity gains and can scale with confidence.

BN: How can manufacturers ensure data security when leveraging AI and PLM?

RM: Data security isn’t negotiable. Extending that protection into AI environments begins with strong data governance. This involves establishing robust access controls, role-based permissions, and clear policies that define who can view, modify, and act on specific data sets. This governance is critical not only for protecting sensitive information, but for ensuring traceability, regulatory compliance, and auditability as AI systems interact with enterprise data.

For example, if an employee isn’t authorized to access sensitive data in a system, they shouldn’t be able to retrieve it via an AI-powered query or action. When governance protocols are in place, companies can scale AI with confidence, without compromising IP protection or regulatory integrity.

A fragmented approach is ill advised. Every disconnected system, every bolt-on AI tool, every data transfer between platforms creates new vulnerabilities and weakens your control. Smart manufacturers should prioritize cloud-native, SaaS-based platforms, where AI is embedded into their existing data architecture. The most effective approaches embed AI directly into the systems where business data already resides and governance is already enforced such as Salesforce, Microsoft, and Google platforms. This isn’t just about reducing risk, it’s about scaling AI without compromising the IP and compliance standards that keep you in business.

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