US Manufacturing Renaissance Takes Shape at Automate 2026, with AI and Automation Driving Change

The recent Automate show in Chicago has left a lasting impression on the industry. The event was marked by high energy levels, dense crowds, and an unmistakable sense of urgency among exhibitors. It’s clear that the automation landscape is undergoing significant changes, with many feeling compelled to adapt or risk being left behind.

One of the key takeaways from the show is that various factors are now converging to drive a US manufacturing renaissance. Policy pressure, onshoring, manufacturing investment, labor constraints, and usable AI with improved reliability are all reinforcing each other, rather than moving in separate directions. This convergence has created an environment where innovation can thrive.

The Monday keynote speakers at Automate 2026 provided valuable insights into the changing nature of manufacturing. Andre Marino from Schneider emphasized that the US cannot compete with major manufacturing centers like China solely on capacity or labor costs. To achieve a ‘manufacturing renaissance’ in the US, it must focus on efficiency, connectivity, and innovation. However, this requires more than just investing in new factories; it demands better factories.

Mike Cicco added to the argument by highlighting that the barrier to automation is decreasing as AI makes systems easier to deploy and use. When combined with North American onshoring pressure, everything starts to fall into place. The forces driving change are no longer theoretical but practical, visible, and increasingly urgent.

The old model of industrial automation is losing its grip. Matt Moschner described why this shift feels so significant: the complex systems of the past were only as good as the day they were deployed. They worked beautifully until the environment changed, the product changed, or the labor model changed – then there was an expensive rip-and-replace standing in the way of competitiveness.

Now, the promise is different. Systems are becoming simpler and more robust, capable of learning and improving over time. AI makes incremental training possible, allowing models to be refined without turning every change into a massive reintegration exercise. This doesn’t mean industrial automation has become easy; it means the old tradeoff between capability and flexibility is starting to break down.

The industry needs to stop treating AI as just another software feature. It’s increasingly becoming a way to make automation less static, with the real shift being that machines are becoming more adaptable in practice rather than simply ‘intelligent’ in theory.

Software-defined manufacturing was a recurring theme throughout the event. Wendy Tan framed it not as whether AI matters but how to make it useful in production. For too long, the industry struggled to move from AI discussion to industrial impact. Her argument is that the time has finally come for software to leverage existing assets and investments, making hardware do things it couldn’t before.

Wendy also made another crucial point: the clunky user experience of traditional industrial automation systems needs to change. If industrial automation is going to scale broadly, it cannot remain an expert-only craft built on obscure workflows and heroic engineering effort. The next phase of adoption depends on simple workflows, ease of use, and a more standardized, software-driven path from intent to execution.

The Automate 2026 panel described a clear sense of urgency driven by tariffs, trade disruption, workforce shortages, and general economic instability. Their response? To become more agile and automate what can be automated – uncertainty is not a reason to wait out the storm but rather a reason to move now before it’s too late.

The old model tolerated long pilots, year-long science projects, and endless experimentation because the operating environment felt more stable. However, if the market is moving fast, policy is changing rapidly, and supply chains remain volatile, then speed and flexibility become part of the value proposition – a crucial aspect for manufacturers to consider in today’s landscape.

Andre Marino warned against underinvesting: there is still plenty of hype in the market, but waiting too long carries its own strategic cost. The industry needs to strike a balance between caution and timely investment to reap the benefits of automation and AI.

The big questions about AI are trust, reliability, safety, and lifecycle validation – crucial aspects that Mike Cicco highlighted as essential for industrial adoption. He emphasized that AI should be combined with hardcoded, reliable systems that the industry already trusts rather than trying to replace them entirely. This approach will enable AI to add value where it matters most.

The Automate 2026 event has left a lasting impact on the industry, highlighting the need for manufacturers to adapt and innovate in response to changing market conditions. As we move forward, it’s essential to address the challenges of automation and AI adoption while leveraging their potential benefits – driving growth, improving efficiency, and enhancing competitiveness.

The future of industrial robotics and physical intelligence will be crucial topics for discussion as the industry continues to evolve. Manufacturers must prioritize innovation, adaptability, and collaboration to stay ahead in today’s fast-paced landscape.