Introduction

|Agentic AI is reshaping manufacturing in 2026. But without a connected PLM foundation, AI cannot automate workflows, optimize BOMs, or make real-time decisions. This blog explains why moving from static repositories to integrated digital threads is the key to AI readiness. 

Most manufacturing enterprises today are sitting on "static repositories"—massive, disconnected lakes of data in Enovia, Teamcenter, SAP, or legacy drives that don't talk to each other. If you want your organization to be AI-ready by 2026, your strategy shouldn't start with buying more AI tools. It must start with building a connected foundation. 

At BJIT, we have spent over 17 years bridging these gaps for top-tier global clients in the Automation and Automotive sectors. We know that the road to Agentic AI is paved with Integration, Migration, and Automation—and here is how successful leaders are building that road today. 

Start building your AI-ready PLM foundation today. Partner with BJIT to integrate, migrate, and automate your enterprise data for 2026 and beyond. 

What Is Agentic AI and Why It Matters in 2026 

We have moved past the phase of generative AI simply summarizing reports. The next generation of AI in manufacturing—Agentic AI—is defined by its ability to take independent action. Unlike traditional automation, which follows rigid rules, Agentic AI possesses autonomy and multi-step reasoning. It can analyze a supply chain disruption, formulate a plan, and execute changes across multiple systems without human intervention. 

Imagine an AI agent that doesn't just alert you to a material shortage but autonomously: 

• Re-routes orders in your ERP based on inventory levels. 
• Updates the Bill of Materials (BOM) in your PLM to reflect substitute parts. 
• Notifies the shop floor via the MES to adjust production schedules. 

The Industry Shift: 

• 80% of manufacturers plan to increase investments in smart and AI-driven automation by 2026 (Deloitte, 2025). 
• Gartner predicts that AI-augmented workflows will be embedded in 40% of enterprise applications by 2026. 
• Over 20% of manufacturers plan to expand robotics and cobot automation in the next two years. 

To unlock this value, your data cannot be static. It must be dynamic, structured, and accessible across the entire manufacturing digital thread. 

The Problem: Static Repositories Slow Down AI Adoption 

In the traditional model, your Engineering team might live in Enovia or Teamcenter, Procurement in Infor LN, SAP, or Salesforce, and the shop floor relies on a separate MES. As a technology-agnostic partner, we often see data moving between these systems via emails, spreadsheets, or manual entry. 

Data Silos are the Enemy of AI According to recent industry reports, 95% of IT leaders cite integration issues as the primary barrier to AI implementation. When data is trapped in silos, AI models hallucinate or fail because they lack the full context of the product lifecycle. 

Table: The Cost of Stagnation vs. The ROI of Connectivity 

Why AI Fails Without a Connected PLM System 

For a human, disjointed systems are cumbersome. For an AI agent, they are fatal. An AI cannot optimize a production schedule if it can’t "see" the design constraints in the PLM or the inventory levels in the ERP. To unlock the value of 2026, integration is no longer optional—it is the nervous system of your enterprise. 

The Connected Workflow 

A connected ecosystem allows data to flow seamlessly across the digital thread, enabling AI to act across departments. 

Data Flow in an AI-Ready Ecosystem: 

1. Engineering (PLM): Engineers release a new design revision. 
2. Data: CAD Models, EBOM, Specs. 
3. Automatic Sync via BJIT Integration Layer 
4. Middleware/Integration Layer: 
5. Validates data structure. 
6. Translates EBOM to MBOM. 
7. API Calls 
8. Procurement (ERP): Updates cost and checks inventory. 
9. Action: AI Agent triggers parts order. 
10. Manufacturing (MES): Receives new work instructions. 
11. Feedback: Quality data loops back to Engineering. 

Manufacturers preparing for AI in 2026 are already modernizing their PLM–ERP integrations. BJIT helps global teams achieve this with secure, scalable APIs. 

Case Studies: How Enterprises Are Building AI-Ready Systems 

1. Bridging the PLM-ERP Divide (Automation Industry) 

For a major automation business line, BJIT tackled a critical disconnection between Enovia PLM and Infor LN ERP. The lack of synchronization meant manual data entry was slowing down production and introducing errors. 

• The Solution: Drawing on our extensive background in PLM/ERP Integration Services, we developed a custom service using middleware to automate the transfer of Item Data and Manufacturing Structures between the two systems. Now, when an engineer updates a model in Enovia, the cost and structure updates are instantly reflected in the ERP. 
• The "AI-Ready" Result: By creating this real-time link, we established a PLM–ERP synchronization pipeline. This unified data stream allows future predictive models to forecast costs and timeline risks accurately without manual data gathering. 

2. The 1.6 Million Part Challenge (Industrial Manufacturing) 

A global industrial client faced a massive hurdle: their legacy AIX platform support was ending, and they had 1.6 million parts trapped in an older VPM system. The standard DBDI (Design Base Data Import) process was too slow. 

• The Solution: Leveraging our global team of 750+ engineers, we engineered a solution to filter, replicate, and fix data errors before they hit the new 3DExperience environment. Our capacity allows us to handle complex mapping of Engineering BOMs (EBOM) to Manufacturing BOMs (MBOM) at scale. 
• The "AI-Ready" Result: The client now operates on a modern, cloud-ready platform with clean, structured data—the exact environment required to deploy machine learning algorithms for engineering data migration and design optimization. 

3. The Document Delivery Tool (Engineering Documentation) 

For a leading engineering firm, the manual generation of technical documentation was a bottleneck. 

• The Solution: Whether working with CAD automation services for CATIA/SolidWorks or documentation platforms, our teams specialize in connecting disparate tools. In this case, BJIT developed a Document Delivery Tool integrated directly into M-Files that automates generation based on pre-set templates. 
• The "AI-Ready" Result: Standardized output. Structured data is readable data, making it effortless for future AI agents to index, search, and summarize technical documentation. 

If you're planning a PLM modernization program, this is the best time to create an enterprise-wide integration map. Reach out to BJIT and tap into our 17+ years of integration and engineering data expertise. 

Reliability in a Hyper-Connected World 

As you connect your PLM, ERP, and CAD systems, your infrastructure becomes more complex. If one link in the chain breaks, the whole operation can stall. In an AI-driven future, "downtime" means your intelligent agents stop working. 

This is why Application Management Services (AMS) are critical. You cannot "set and forget" a modern PLM environment. It requires proactive monitoring, security patching, and continuous improvement. 

With offices in Japan, the USA, Finland, Sweden, Singapore, Thailand, and Bangladesh, BJIT’s AMS teams provide 18/5 and 24/7 support for major platforms. We don't just fix bugs; we perform root cause analysis and regular "health checks" to ensure your digital thread never snaps. 

Conclusion: Build a Future-Proof PLM Strategy for 2026 

The hype around AI often obscures the hard work required to make it useful. The companies that will dominate in 2026 aren't just the ones buying the flashiest AI tools; they are the ones doing the heavy lifting today. 

They are integrating their silos using robust APIs. They are cleaning their legacy data through intelligent migration. They are automating the mundane to prepare for the autonomous. 

Is your data ready for the AI era? 

If you’re planning a PLM upgrade or data migration in 2025–2026, this is the ideal time to build your AI-ready foundation. Whether you need to migrate millions of legacy items, build a custom integration between Enovia and your ERP, or develop productivity macros for CATIA, BJIT has the expertise to get you there. 

Contact BJIT Today to discuss your PLM strategy for 2026. 

References

1. MuleSoft. (2024). 2024 Connectivity Benchmark Report. Salesforce. 
2. Accenture. (2025). The Intelligent Service Center. Accenture Federal Services. 
3. Integrate.io. (2025). Data Transformation Challenge Statistics — 50 Statistics Every Technology Leader Should Know in 2025. 
4. Saphyte. (2025, August 28). Practical Ways AI Cuts Operational Costs in 2025. Medium.