Elif Ă–zaksu
Corporate
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AI Readiness: A Vendor Problem?
Most AI vendors tell manufacturers to modernize first, then adopt AI. But Rick Rider from Infor believes this is precisely why many companies get stuck in "pilot hell."
According to Rider, the "AI readiness gap" is not a data problem, but a vendor problem. He states that industrial companies inherently use fragmented, hybrid environments, and partners who demand perfect conditions before delivering value are a bottleneck, not a solution.
Is the Term "AI Ready" Misused?
Rider believes that the term "AI ready" has become one of the most misused terms in the manufacturing sector. According to him, companies that see the greatest success don't wait to be AI ready; instead, they adopt AI in today's fragmented environments and modernize in the process.
What is the Most Common Problematic Approach to AI Adoption?
Rider states that the most common problem in AI adoption in the industrial sector is "point solution enthusiasm." Manufacturers identify a single pain point, deploy an AI tool to address it, and declare it a victory. However, this strategy rarely establishes connections across the entire operation.
Why is This Approach Not Ideal?
Manufacturing does not operate in isolated pockets. A shop floor decision reverberates through the supply chain, financials, and workforce planning. When AI is deployed in disconnected pieces, you get an intelligence that cannot act on its own. As a result, manufacturers end up with more data than ever, yet lack the operational clarity they need. AI begins to feel more like an overhead than an asset.
How is the "AI Readiness Gap" Defined?
The AI readiness gap is the distance between a manufacturer's current state of data infrastructure and where it needs to be for AI to deliver continuous, compounding value. Most organizations have years of operational data, but this data resides in siloed systems, often in formats designed for reporting rather than real-time intelligence. When you add the human layer to this situation (inconsistent processes, tribal knowledge that has never made it into a system, change management that can't keep up with technology investments), a structural gap emerges that no AI model can close on its own.
This gap is not about access to AI tools, but about the organization's readiness to absorb and act on what AI produces.
Where Do Vendors Fit into the Problem?
The most common advice from AI vendors to manufacturers is: modernize your infrastructure first, then let's talk about AI. Clean your data, integrate your systems, move to the cloud – and then you'll be ready. While this guidance seems reasonable on the surface, for most industrial companies, it's a recipe for treading water. These environments are hybrid by design. Decades-old PLCs sit alongside cloud-connected assets; ERP systems haven't moved, and operational technology predates the concept of interoperability.
A vendor who demands perfect conditions before delivering value is a bottleneck, not a partner. And the prevalence of this 'modernize first' mindset is a significant reason why many manufacturers get stuck running pilot projects that never scale.
What is the Solution?
The starting point is to meet manufacturers where they actually are, not where a vendor's reference architecture assumes them to be. All we need is a consistent architecture designed to work in the environments manufacturers actually operate in, and a clear path for progression as those environments evolve. Beyond technologies and tools, what is most critical is a vendor's verified ability to continuously co-innovate with their customers.
