Today’s Practices, Tomorrow’s Promise: Where Manufacturers Stand With AI

Recent reports show that manufacturing AI investments are projected to grow, despite economic uncertainty and supply chain pressures. This momentum bodes well for the 68% of manufacturers still marching toward digitalization and Industry 4.0, according to our 2024 State of Manufacturing Survey.

Industry 4.0 is all about building smarter, more connected manufacturing systems that make operations run smoother, automate workflows and facilitate data-driven strategies. The goal? Cut waste, avoid downtime and meet demand faster and more reliably. Getting there means cultivating partnerships between people and technology.

Effective solutions must speak the language of the shop floor, surface real-time data and support—not bench—the people behind the process. AI has gone from experimental to essential. It acts as the grease in the gears, helping people and machines work as one.

Today’s Reality: Practical AI On The Factory Floor

AI isn’t some distant promise. It’s here, already at work on the floor. Currently, most manufacturers using AI are doing so in very targeted, tactical ways.

Think of it as smarter tools for everyday problems:

• Quality Inspections: Instead of relying on tired eyes and manual checklists, manufacturers are using AI to flag defects in real time. AI works faster, catches more and delivers consistency that human inspectors can’t.

• Predictive Maintenance: AI analyzes sensor data to flag potential issues early, giving teams the gift of proactivity so they can avoid all-out breakdowns.

• Inventory And Logistics: In supply planning, AI replaces guesswork with precision. It helps teams keep what they need on hand without overstocking or running short and right-sizes inventory balances.

• Process Optimization: Machine learning reviews production data to identify bottlenecks or root causes of errors, freeing up capacity and improving output.

While these applications deliver real, proven value, the right mindset is to stay curious but cautious. These are valuable, incremental steps—not revolutions.

Challenge: AI Doesn’t Work In A Vacuum

A cautious mindset matters because AI isn’t an instant fix or silver bullet. Many of today’s manufacturers are still dealing with fragmented systems, varying machine capabilities and misaligned data quality. Simply adding AI won’t smooth over those foundational cracks. When your data is siloed or inconsistent, you won’t get reliable insights, no matter how advanced the model is. AI doesn’t fix broken processes, but it canamplifygood ones.

That’s why the real work is in building the foundation: Automating data collection, building context and ensuring teams can trust what they see. Especially critical is the context step, as data isn’t helpful if it’s out of sync. Connected systems pull and synthesize information from across machines, teams and timelines to stitch together what’s happening, so insights come with context, not question marks.

Education also plays a major role in effective adoption. Employees need to understand how the tools work and how they fit into the larger strategy. AI is also still evolving, and keeping teams in the loop can reduce disruption. When people see AI as a tool that aligns with their role and supports their job functions, they’re much more likely to use it effectively. Laying this groundwork is a must for manufacturers intending to wield advanced technology solutions.

The same foundation that supports traditional AI—clear goals, connected systems and a team that understands the tools—will be critical when it comes time to implement GenAI.

Opportunity: How GenAI Will Come Into Play

GenAI is beginning to show value day-to-day, simplifying how people interact with manufacturing systems. Instead of laboring over dashboards or waiting for IT to pull a report, users can type a question and get a textualized response—charts, graphs and all. It might seem like a small shift, but when your systems are complex and your margins are thin, streamlining how people access data makes a tangible difference.

But the impact goes beyond convenience. GenAI has the potential to meaningfully support the people on the floor, helping them make faster, better decisions. If a production line goes down, GenAI can help teams understand what’s changed, spot relevant historical patterns and act on those insights. It gives people better tools that help them perform their jobs smarter and more efficiently. GenAI helps democratize data by turning complex, data-backed insights into plain language. So anyone, regardless of role or background, can be empowered to make decisions without waiting on a data specialist.

Success with GenAI isn’t about being first; it’s about being ready with a real business case and a plan that fits your operations. The strongest strategies bring in voices from across the organization and take a thoughtful, cross-functional approach. When grounded in real needs, GenAI becomes a decision making engine for the people closest to the work, and that’s where its impact starts to show.

A Tool For The Thoughtful

AI isn’t magic; it’s a method. And it works when built on solid ground.

For manufacturers who take a thoughtful approach—starting with good data, clear goals and a strong team—it’s a tool with the potential to reduce friction, improve workflows and drive faster, more informed decisions. The most successful companies will be the cautious ones, those that align tools with real business needs and involve their workforce early. In other words, the companies that treat transformation as a process, not a checkbox.