From Pilot Purgatory to Production: How Interoperability Unlocks the True Value of Industrial AI
- May 20
- 3 min read
It's a scenario that plays out across asset-intensive industries with remarkable consistency: a company invests in an AI pilot, generates promising early results, and then...stalls. The project never makes it to “full production.” It becomes one of countless promising experiments that live indefinitely in what some have come to call "pilot purgatory."
According to Trudi Hable, Chief Revenue Officer for Radix North America, this pattern isn't a coincidence. "Many projects never graduate from the pilot phase because the foundations are not solid for building AI, and the foundations often lack a clear and aligned understanding of the use case that leadership, engineers, and operators hope to solve."
But there is another critical factor that often goes unaddressed: interoperability. Or more precisely, the lack of it.
In most industrial environments, data lives in silos. Operators log into multiple disconnected systems; sometimes as many as 15, just to gather the information they need to complete a single task. When AI is introduced into this fragmented landscape without a plan to bridge those systems, the result is an AI that is only as smart as the narrow slice of data it can access.
Misunderstanding the importance of interoperability and the role it plays in successful AI integration is a common mistake we get called to fix after unsuccessful attempts to integrate AI. True interoperability means that AI agents can pull information from across the enterprise — from operational technology to ERP systems and back — in a seamless, bidirectional flow.
Without this, AI systems generate insights that are incomplete or, worse, contradictory to reality on the ground. Engineers are left doing manual reconciliation. Trust in the AI erodes. The pilot is quietly shelved.
Moving from pilot to production also requires addressing what I consider three core traps. The first is a heterogeneity trap — when assets across a facility are too dissimilar to allow a pilot to scale. The second is a context trap — when data lacks the metadata and meaning needed to generalize a model across the operation. The third is a software maintenance crisis — when deploying AI to hundreds of assets creates a code maintenance burden that no single team can sustain.
The solution to all three lies in the templatization and parameterization of assets — what software engineers call class modeling. This is combined with a unified data namespace that carries the full context of every asset across the facility.
For industrial leaders ready to move beyond the pilot phase, the message is clear: invest in interoperability before you invest in AI. The organizations that do will find that their AI doesn't just work in the lab. It works at scale, across every corner of their operation.
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About Radix
Founded in 2010, Radix is a privately held technology solutions and services company operating globally, empowering customers with consulting, engineering, operations technology, and digital solutions. Radix combines key capabilities and practices to enable our worldwide customers to thrive in their technology journey. With North American headquarters in Houston, TX, and headquarters in Rio de Janeiro, Brazil, including offices in São Paulo and Belo Horizonte, Radix provides technology-based, data-driven solutions to asset-intensive industries. Radix's robust capabilities extend to more than 30 countries worldwide. Learn more: www.radixeng.com
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