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AI agents grant product designers faster data access and decision support to reduce rework and enable smarter, more efficient product development. 

 

TAKEAWAYS:
● AI agents speed product development by automating routine tasks and surfacing cross-system insights, helping teams avoid errors and make more optimized decisions.
● AI agents elevate human judgement rather than replacing it by surfacing relevant options and historical insights for designers to evaluate.
● Organizations that focus on targeted reuse scenarios, strong governance and clear KPIs will be best positioned to capture measurable impact from AI agents.  

 

Manufacturers face a familiar but intensifying challenge: competitive pressure demands greater speed, lower cost and continuous innovation—yet product design cycles aren’t keeping pace. Compounding this, every product iteration now generates exponentially more data, and instead of streamlining decisions, that complexity too often creates more noise than clarity. AI is changing that—not by replacing engineering judgment, but by giving teams the tools to move faster and decide smarter.

The real opportunity for decision enhancement lies not in any single system, but in the connections across all of them. Manufacturers already sit on vast reservoirs of institutional knowledge—embedded in product lifecycle management (PLM) systems, enterprise resource planning (ERP) data, quality records, supplier histories and engineering documentation accumulated over decades. The problem is that this knowledge has historically lived in silos, forcing engineers and product teams to make critical decisions with only a fraction of the relevant context. When AI can surface patterns and relationships across these enterprise systems simultaneously, the nature of the decision itself changes—teams stop reacting to incomplete information and start anticipating outcomes before they occur.

This is where AI agents are beginning to redefine what’s possible in product development. Unlike traditional search or analytics tools, agents can traverse both structured data and unstructured content—think engineering specifications, change orders, inspection reports and supplier emails—inferring connections and similarities that no individual engineer could reasonably track across a complex product portfolio. In practice, this means teams can identify potential quality issues before they propagate, assess manufacturability earlier in the design cycle when changes are least costly, and automate time-consuming but critical tasks like compiling regulatory documentation. The result is less time spent hunting for answers and more time spent acting on them.

AI Agents for Connecting Data to Better Decisions

AI agents are not simply better search tools; they fundamentally change how designers leverage enterprise knowledge and make decisions. They can surface relevant prior designs and approved components, predict quality risks, highlight manufacturability constraints, summarize regulatory requirements and automate routine data compilation.

A few high‑value use cases illustrate the potential:

• Finding previously approved formulations or components that match new design requirements
• Surfacing similar products or variants to accelerate early design concepts
• Predicting quality or manufacturability risks based on historical patterns and design attributes
• Summarizing regulatory requirements and automating administrative, repetitive or retrieval-heavy portions of the design process
• Synthesizing cross-system insights to support faster, more informed design decisions

By reducing time spent navigating systems and reconstructing prior decisions, AI agents shift engineering effort from information retrieval to design evaluation. Rework declines as teams confidently build on proven designs and previous guidance. With faster access to the insights they need, designers are free to explore faster, more broadly, and with more context. As the final judges on design decisions, humans remain in the driver’s seat.

Evolving the Product Development Ecosystem with AI Agents

AI agents don’t replace existing systems or enterprise architecture—they complement PLM, ERP, quality management systems (QMS) and other repositories by connecting information and uncovering insights. Most companies are only beginning to experiment with AI in design workflows. Understanding the path from early pilots to full AI adoption can help companies capture value faster while mitigating risk.

This shift to an AI-assisted design workflow represents a new evolution in PLM maturity:

Figure 1: A new PLM Maturity Model

Advancement along this model hinges on both technological and cultural preparedness. Teams must trust the data, understand how AI agents derive conclusions, and provide proper oversight. Transparency and traceability are crucial, especially in regulated sectors. Progressive organizations will view AI agents as collaborative tools that augment human ability, rather than substitute it.

How Manufacturers Can Start the Journey

The most successful manufacturers will follow these steps:

1. Diagnose pain points and process inefficiencies
   Identify where mistakes are made, where reinvention or repetition is prolific or where you stand the most to gain from more optimized decisions. Define the personas impacted and map information needs from across the enterprise, not just PLM – ERP, MES, QMS, and RIM systems.
2. Match the right AI capabilities to the right problems
   Different challenges require different tools:
   a.  Generative AI for summarization, interpretation and chat interaction
   b.  Predictive models to catch errors and forecast the outcome of a decision
   c.  Prescriptive models to optimize design, such as for cost or manufacturability
   d.  Retrieval-augmented generation (RAG) for intelligent search across document stores and databases
   The goal is not broad AI adoption but targeted solutions that reduce cycle time and rework.
3. Build a prioritized use case pipeline
   Start with narrow, high-value scenarios to demonstrate measurable improvements in speed or rates of reuse. Use early wins as momentum to refine the operating model.
4. Establish the operating model early
   AI agents require continuous improvement, not a one-time implementation. Leaders should build an AI-ready PLM architecture that accounts for governance, data structures and a federated model connecting central AI teams with product design functions. This ensures that AI agents remain accurate, trusted and aligned with business needs.

Future-Looking Product Designers Have AI Assistants

AI agents will not replace product designers—they will amplify their expertise and creativity. But capturing this value requires discipline and cultural readiness; many organizations fall into the trap of “doing AI” without clarity on business value or underestimating the complexity of PLM and adjacent systems. Clear KPIs, strong governance and a focus on real problems—reuse, visibility, cycle time—keep teams grounded in outcomes rather than hype.

Equally important, leaders must help culture evolve alongside new technologies. Job loss fears and uncertainty can slow adoption unless teams understand that AI agents relieve administrative burden rather than eliminate design roles. When designers see AI as a partner that expands their creative capacity, adoption accelerates.

The future of product development belongs to teams that treat AI agents as true collaborators—tools that help them move faster, design smarter and innovate with confidence.  M

About the Authors:

 

Chelsea Barnes is a Data Science Director at Rockwell Automation Digital

 

 

 

David Miracle is Global Lead Principal of Consumer Packaged Goods at Rockwell Automation