In manufacturing plants and design centers worldwide, engineers lose nearly a quarter of their week chasing information scattered across systems, departments, and outdated files. The result: slower onboarding, design errors, missed supplier opportunities, and longer equipment downtime.
According to a recent study by Tech-Clarity, about one-half of companies surveyed identified sharing information with others as a top design challenge. A similar number report that simply finding the right information hampers design efforts. Over one-third say they work on the wrong or outdated data, almost half of engineers said simply sharing information across teams was their biggest design challenge.
This knowledge gap is costing manufacturers speed, productivity, and innovation. And it’s exactly where AI agents and generative AI can make the biggest impact.
Why Manufacturers Hesitate
The promise of agentic AI is compelling: agents that can analyze, design, simulate, and forecast faster than ever before. But most manufacturers are approaching deployments with caution. The risks are real:
- Hallucinated answers or wrong recommendations.
- Incomplete or biased data.
- Lack of skilled users and adoption barriers.
- Compliance and governance concerns.
- For leaders, the key is not to avoid GenAI. It’s to start in the right place.
Knowledge Agents: A Practical First Step
For manufacturers, the fastest ROI from GenAI doesn’t come from futuristic design copilots, it comes from solving the daily grind of finding information. Engineers, planners, and technicians routinely lose eight to sometimes 15 hours each week searching PLM systems, supplier portals, and shared drives. That’s lost productivity multiplied across the workforce, and millions in hidden cost.
Knowledge agents solve this immediately. With enterprise AI search as a universal platform and by combining GenAI with retrieval-augmented generation (RAG) – an AI framework that allows gen-AI infused applications to answer questions using enterprise knowledge, they deliver answers grounded in your company’s own manuals, contracts, and databases—not guesswork. The result is speed you can trust:
- Maintenance: Instead of hunting through PDFs, a technician asks, “What’s the failure protocol for this compressor?” and receives the exact, cited procedure in seconds with references to the document sources.
- R&D: Teams instantly surface prior test results and simulations, cutting duplication and accelerating time-to-innovation.
- Supply Chain: Planners integrate supplier performance data and contracts with market trends to make smarter, faster sourcing decisions.
- Knowledge agents close the gap between scattered data and usable insight, giving engineers and designers back hours each week to focus on higher-value work.
The Digital Thread in Action
The opportunity becomes even bigger when knowledge agents are connected through the digital thread, which is the continuous flow of data across the product lifecycle.
When CAD models, PLM systems, and MES logs are tied together, agents can provide context-aware answers for everyone from designers to field technicians. Airbus Helicopters uses a GenAI search application to boost self-service for over 20 percent of customer queries, resolves simpler issues faster, and reduces support response times by up to 10 percent.
At TotalEnergies, an AI assistant helped prevent three major equipment failures in just six months, avoiding millions of dollars in downtime losses.
The lesson is clear: When AI is connected to the digital thread, it doesn’t just speed up tasks and save time. It prevents costly errors, improves safety, and fuels innovation across the entire manufacturing value chain.
Key Considerations for Adoption
To capture the upside without the risk, decision makers should focus on four priorities:
- Start narrow, scale with confidence. Begin with a targeted use case like knowledge search or maintenance support before expanding to design, simulation, or supply chain.
- Invest in data pipelines. RAG only works if the underlying data is clean, connected, and accessible.
- Pair AI with human oversight. AI should augment, not replace, human expertise—especially in high-stakes environments.
- Prepare the workforce. Reskilling and change management are as critical as the technology itself.
The Strategic Role of RAG
Retrieval-augmented generation could be the differentiator that makes AI agents viable in manufacturing. Without it, you could be gambling on hallucinations. With it, you get:
- Answers tied directly to your own documentation.
- Explainability by citing sources, critical for compliance and safety.
- Faster innovation by reusing knowledge that would otherwise stay hidden.
AI agents and generative AI aren’t just shiny new tools in a loud and prolific hype cycle. They are strategic enablers of manufacturing competitiveness. The path forward is clear: start with knowledge agents, ground them with RAG, connect them through the digital thread, and scale responsibly.
Manufacturers who do will transform the knowledge gap from today’s biggest bottleneck into tomorrow’s biggest advantage.
