Artificial intelligence has officially moved past the hype cycle – at least for manufacturers. For companies across the supply chain, AI proves itself to be both an operational advantage and a macroeconomic growth engine. With real-world use cases and tangible investments driving demand, AI is no longer experimental. It is practical, profitable, and already reshaping the manufacturing landscape.
At the Winter Economic Forum hosted by AMT – The Association For Manufacturing Technology, manufacturers had the opportunity to dive into some of these examples and explore the business opportunities available to them with this technology.
AI at the Point of Production: Lessons From GE Aerospace
Alex Antony, GE Aerospace’s director of data science and analytics, offered manufacturers a uniquely grounded perspective. Unlike tech companies building AI for consumers, GE is deploying AI in some of the world’s most demanding industrial environments – jet engines, MRO shops, supplier networks, and global overhaul facilities.
Because AI is being deployed in the physical world, three principles drive GE’s adoption and ensure they deliver on their paramount promise of safety: The data being used in models must be trusted; the model itself must be transparent; and there must always be a human in the loop. As with all process or system improvements, it is important that manufacturers are deliberate about the business process being impacted before implementing AI or other technologies. Technology alone will not produce the desired outcome if process or goal is not clear.
GE is upfront about its mission in this space: AI is to be used to deliver better outcomes for their customers, their business, and their employees.
AI at Scale in Engine Monitoring
Putting that to work on the shop floor may be difficult, but it can reap big rewards for customers. Approximately three billion passengers a year fly with a GE Aerospace engine on their plane – about 40% of the global population. And at any time, around 1 million people fly on a GE Aerospace-powered aircraft.
The company continuously monitors more than 42,000 commercial engines, ingesting thousands of data points per second. But it’s because of this scale that AI is able to make such an impact. Using AI to monitor engine health has allowed GE Aerospace to expand the conditions it can monitor and better anticipate needed maintenance, leading to anomalies being detected 60% faster, issue detection accuracy increasing by 45%, and false alerts being cut in half. This shift – from condition‑based maintenance to true predictive maintenance – is precisely the kind of outcome manufacturing executives seek: greater uptime, fewer surprises, and faster decision cycles.
Predicting Part Procurement To Reduce Repair Time
One of GE’s most impactful AI applications solves a problem many manufacturers understand: unpredictable demand for parts.
Finding the right mix between having the parts needed to service an engine and not carrying too much inventory is a difficult balance. By combining diagnostic data, engine history, and shop-floor inputs, GE’s model has been able to predict the parts needed for an engine repair and the work required far in advance of when the work or parts are needed. This led to a 10% reduction in the time an engine spends in the shop, meaning a better service offering and more money for their customers.
Saving Engineering Time Through AI-Enabled Inspection
Borescope inspections used to require GE Aerospace engineers to manually sort through hundreds of images to identify engine distress, a process that was also prone to error. GE’s new AI-enabled blade inspection tool now allows an advanced computer vision algorithm to sort through those images, identify the best ones, and reframe them automatically, meaning engineers only have to look through a few images to identify the distress level of the engine. This has reduced the process time from three hours to 1 1/2 hours while improving accuracy.
AI as an Economic Engine for Manufacturers
Chris Chidzik, AMT’s principal economist, described AI as a structural tailwind for manufacturing technology, and Oxford Economics Managing Director Jeremy Leonard reinforced the macroeconomic significance of AI.
Ecosystem Investment Is Propping Up U.S. Economic Growth
AI-related investment now accounts for a disproportionately large share of total business investment, which led Leonard to posit:
AI is masking manufacturing softness in other end-user industries
Without AI investment, U.S. gross domestic product growth would be materially lower
AI-driven capex is invigorating sectors from semiconductors to electrical equipment
However, for manufacturers, this is mostly good news: AI demand is creating downstream opportunities for tooling, automation, materials, and machine technology. Chidzik agrees, noting that AI requires vast amounts of new hardware, automation, and supporting technology. Simultaneously, AI enables manufacturers to produce goods more efficiently, which lowers their costs and can grow their margins. This dual benefit positions the manufacturing technology sector as one of the biggest beneficiaries of AI adoption.
Specifically, energy and infrastructure demand have surged – pun intended – providing a strong tailwind to manufacturers that supply those industries. Data from the U.S. Manufacturing Technology Orders program shows that electrical equipment orders were up by over 20% every month of 2025 compared to the long-run average,pointing to the growing demand for more power to feed data centers and advanced computing infrastructure; all of which drives demand for transformers, switchgears, precision components, automation systems, and metals.
As Leonard summarized: AI’s capital footprint is massive – and manufacturers are central to supplying it.
What Manufacturing Leaders Should Do Now
The message to manufacturers was uniform: AI is no longer optional or just a fad. Early adopters will win and reap the biggest gains while late adopters struggle.
So, what can manufacturing business leaders do now?
1. Start With Business Value, Not Technology Curiosity
Begin with business outcomes, not “doing AI for AI’s sake.” Time and again, organizations that have successfully implemented AI emphasize that these projects shouldn’t originate in the IT department. Instead, every initiative must start with a clearly defined business outcome and be owned by the appropriate business or process leader. This ensures alignment with organizational goals and guarantees that the technology is being applied to solve a real operational challenge. When companies take this approach, early wins generate visible, measurable value – building confidence, fueling internal demand, and paving the way for broader AI adoption across the organization.
2. Modernize Data Infrastructure
AI is only as effective as the data behind it. Put simply: Garbage in, garbage out. Beyond its significant energy requirements, AI depends on data that is accurate, well‑structured, and readily accessible. The real gains come when AI is applied to your own operations, which means the inputs must reflect what’s happening on your shop floor, in your finance systems, or across your sales organization. Whatever outcome you’re targeting, ensure that the right stakeholders understand how data flows through the business and how it is organized. Reliable, repeatable AI results are only possible when your data foundation is solid. Make sure teams fully understand the data fed into any model, and it is clean – AI can only generate value when its inputs reflect the realities of your operation.
3. Put AI in the Flow of Work
Tools succeed when they integrate with the operators’ actual job – not when bolted on from the outside. Retention is perhaps one of the most important metrics in the product world, and it’s widely regarded as a foundational principle of product success. And the key to high retention is building a habit. Having an AI solution may create a “wow” moment, but to form a behavioral change, it must do more than generate excitement; it needs to solve a problem, reinforce continual usage at the right moment, and be as frictionless as possible. Building AI into a new process rather than tacking it onto an existing process can help form those new habits.
4. Build Trust and a Human-in-the-Loop Culture
Transparency, governance, and visibility are essential for workforce adoption. As with any other system or process, users must understand what is going on behind the scenes to trust the output. Humans must be a part of the process – not separate from it – for the organization to reap the most ROI.
AI Is Manufacturing’s Next Growth Curve
From GE’s shop‑floor use cases to Oxford’s macroeconomic modeling and AMT’s demand analysis, the conclusion is unmistakable: AI is simultaneously enabling better manufacturing – and driving more of it.
For manufacturers, the opportunity is twofold. Those in the machine tool space should adopt AI internally and capitalize on AI-driven demand for machines, automation, components, and precision engineering. The next wave of growth in U.S. manufacturing will belong to the companies that embrace AI not as a trend, but as a core operational capability.
For more in-depth discussions on AI’s impact on manufacturing and how businesses are using it, attend The MFG Meeting on March 10-12, 2026, in Fort Lauderdale, Florida.
