Bringing AI to the Factory Floor: It’s Not as Simple as Plug-and-Play

The manufacturing industry has long embraced world-changing technological innovations—just consider the mechanization of the Industrial Revolution and subsequent automation of assembly lines.

Even so, the industry tends to lag behind others when it comes to the modern adoption and utilization of advanced digital technology solutions. Many facilities are still working to reach a critical threshold of digitalization, where connected systems unite all machinery through the industrial internet of things (IIoT) and centralize insights for shop floor workers.

For the solution providers who design these connected systems, it’s been especially challenging to accommodate the rapid growth and sudden adoption of artificial intelligence (AI) solutions. The introduction of these solutions has created what feels like an AI land grab, where new models and tools are launching constantly, each with its own interface, data requirements and context assumptions.

Naturally, that creates friction when trying to design tools and embed them into real workflows. Solution providers and manufacturers both have roles to play in driving further digitalization by facilitating effective, interoperable AI solutions.

What AI Can Do for Manufacturers

If implemented correctly, AI solutions help manufacturers make better, smarter, faster decisions. Consider popular applications such as:

• Predictive maintenance, which uses IIoT sensors on shop-floor equipment to help manufacturers anticipate and preempt machinery breakdowns.
• Statistical process control, which samples finished products to gauge overall quality and identify areas for improvement.
• Smart systems, which identify bottlenecks and inefficiencies, facilitating overall process improvement.

These are just a few use cases for AI on the factory floor. More arise every day—notably, generative AI (GenAI) has tremendous promise as a vehicle for shop-floor data proficiency. Conversational interfaces will let manufacturing workers ask questions in plain language and receive informed, contextualized responses that help them understand the bigger picture of operations.

GenAI and other AI solutions haven’t yet hit the shop floor at scale, though, largely because of the challenges involved in integration.

Challenges of Implementing AI on the Factory Floor Today

AI can’t exist in a vacuum; it must be interwoven into existing systems and architectures. For more digitally advanced manufacturers, the shop floor is already a complex network of smart technology.

The facilities that have progressed most toward digitalization use connected platforms like enterprise resource planning (ERP) software and manufacturing execution systems (MES) to manage inventory and replenishment, monitor machinery performance, diagnose and troubleshoot errors, oversee quality assurance and control, and provide contextualized recommendations for operations optimization.

Any AI solution must be able to integrate seamlessly with this existing architecture, effectively communicate across the shop floor, and provide meaningful information to operators. But like with most advanced technology, AI’s integration is easier said than done, and there are a number of complications that can come into play during the implementation process. Chief among these complications is the question of formatting data.

Connected manufacturing systems contain an untold volume of data—both raw data pulled from IIoT-enabled smart sensors attached to machinery and structured data used to inform contextualized insights. For users to be able to pose questions to a GenAI-based solution, that data will need to be appropriately formatted and contextualized in near-real time.

If this doesn’t happen, the system won’t be able to make the correct assumptions to act on the data and provide an accurate response. Solution providers, manufacturers, and system integrators must work together to ensure not only that the AI integration will function as part of a broader solution but that the customer’s system is adequately set up to support the solution.

But, as previously mentioned, much of the industry is still learning how to wield even “basic” advanced technology like automation, much less GenAI. Many manufacturers are just scratching the surface of shop-floor technology; they might be hungry for GenAI but their infrastructure isn’t mature enough to support successful adoption. With the early adopters on one side of the digitalization threshold and those playing technological catch-up on the other, there’s a significant range of digital maturity within the industry—one that makes it hard to anticipate and accommodate unique needs.

Implementation Complications Call for Team Participation

There’s a lot that manufacturing technology teams can do to prepare their organizations for connected systems enriched by AI.

• Contextualize trusted data not only so it’s ready for GenAI solutions but so teams can begin acting on it today.
• Build a clear system of record with a single source of truth; this will support regulatory compliance, traceability, historical insights and more.
• Make insights accessible across roles so all team members can understand the “why” behind their work, make improvements to their own processes, and learn from mistakes.

Getting AI implementation right isn’t solely up to the developers who design manufacturing solutions—it’s a team effort. Even the best-designed system will fall flat if the existing architecture isn’t prepared for integration.

Set Your Team Up for AI Success

Interoperability is a challenge now, but one that will diminish as the ecosystem matures and common protocols take hold. Just like we’ve seen with past waves of tech—whether in enterprise data systems, IoT or cloud platforms—standards always emerge.

For example, model context protocol (MCP) is a promising step toward semantic interoperability because it helps AI agents and tools share structured context and intent, which is critical to making systems work together more fluidly. 

However, MCP doesn’t solve everything; we’ll still need supporting infrastructure like data normalization layers, security frameworks, and versioning standards to fully unlock seamless integration—but those gaps are closing fast. In the meantime, thoughtful architecture and smart orchestration go a long way in keeping things aligned.

By building a strong foundation for operational data, manufacturers can support their own data proficiency, set themselves up for digitalization success, and pave the way toward a future enriched by AI.