Most AI vendors tell manufacturers to modernize first, then adopt AI. Rick Rider, SVP of AI Innovation at Infor, thinks that's exactly why so many are stuck in pilot purgatory.

"The 'AI readiness gap' isn't a data problem—it's a vendor problem," he said, adding how he believes that industrial companies run fragmented, hybrid environments by nature, and partners that require perfect conditions before delivering value are the bottleneck, not the solution.

(Picture that Spider-Man pointing at Spider-Man meme.) 

Rider believes that "AI-ready" has become one of manufacturing's most misused terms. In fact, his take is that the companies seeing the most success aren't waiting to become AI-ready, but rather they're adopting AI within the realities of today's fragmented environments and modernizing along the way.

We wanted to learn more...

AW: What is the most common problematic approach to AI adoption in the industrial/manufacturing space?

Rider: The most common pattern we see is what I'd call 'point solution enthusiasm'—manufacturers identify a single pain point, deploy an AI tool to address it, and declare a win. It often looks like predictive maintenance on one line; a demand-forecasting add-on bolted onto a legacy system, or a generative AI assistant layered on top of data that was never cleaned or unified. Each deployment gets its own project team, its own budget cycle, and its own success metric.

The enthusiasm is real, but the strategy rarely connects the dots across the operation.

AW: Why is this not ideal?

Rider: Manufacturing doesn't run in isolated pockets. A shop-floor decision ripples into supply chain, financials and workforce scheduling. When AI is deployed in disconnected fragments, you end up with intelligence that can't act on itself. You get an insight from one system that can't be corroborated, enriched, or executed on by any other system in your environment.

Manufacturers end up with more data than ever and less operational clarity than they need. The AI starts to feel like overhead rather than an asset.

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AW: How do you define the "AI readiness gap"?

Rider: The AI readiness gap is the distance between where a manufacturer's data infrastructure actually is and where it needs to be for AI to deliver sustained, compounding value. Most organizations have years of operational data, but that data lives in siloed systems, often in formats that were designed for reporting rather than real-time intelligence. Add in the human layer—inconsistent processes, tribal knowledge that never made it into any system, change management that hasn't kept pace with technology investment—and you have a structural gap that no AI model can close on its own.

The gap isn't about access to AI tools. It's about organizational readiness to absorb and act on what AI produces.

AW: How are vendors part of the problem?

Rider: The most common piece of advice manufacturers receive from AI vendors is this: modernize your infrastructure first, then we'll talk about AI. Get your data clean, get your systems integrated, get to the cloud—and then you'll be ready.

That guidance sounds reasonable on the surface, but for most industrial companies it's a prescription for standing still. These environments are hybrid by design. You have decades-old PLCs sitting next to cloud-connected assets, ERP systems that haven't moved, and operational technology that predates the concept of interoperability.

A vendor that requires perfect conditions before it can deliver value isn't a partner—it's a bottleneck. And the proliferation of that 'modernize first' mentality is a significant reason why so many manufacturers are stuck running pilots that never scale.

AW: What is the solution?

Rider: The starting point is meeting manufacturers where they actually are, not where a vendor's reference architecture assumes they should be. 

We only need a coherent architecture designed to work in the environments manufacturers actually operate in, with a clear path forward as those environments evolve. What’s most critically important beyond the technologies and tools is the validated ability for a vendor to be a true partner in continuously co-innovating with clients.