Industrial AI Pilot: Why 68% of Manufacturers Can’t Scale Past the POC

The Pilot Worked. Nobody Funded the Rollout.

Your Compressor predictive maintenance pilot at Plant A production line worked. Unplanned downtime dropped. The operations team saw the value. The plant manager signed off. You brought the results to the steering committee expecting approval for a multi-site rollout.

Instead, you got questions you couldn’t answer. What will it cost to deploy at the other four plants? How long until we see returns at each site? Can we reuse the same model, or do we need to rebuild? Who owns this once it’s in production? Is it OT, IT, or the data science team?

The pilot didn’t fail. It succeeded in isolation. And isolation is where it stayed.

The 2026 Accelerating Industrial AI Survey, drawn from hundreds of industrial professionals across manufacturing, energy, and transportation, reveals this isn’t an exception but rather the dominant pattern.

The Scale Gap Is Wider Than Most Teams Realize

The survey captures the industrial AI adoption landscape in a single, sobering contrast: only 7% of respondents have AI embedded in core operational processes today while 68% are still in pilots, proof-of-concepts, and limited deployments without an overall strategy, or still researching their approach.

And yet, 44% expect to have AI embedded in core processes within three years. That’s a six-fold increase in adoption in 36 months. Given that most organizations haven’t moved from pilot to production for their first use case, the expectation is detached from the operational reality.

The disconnect stems from the assumption that scaling is an incremental step from piloting. Yet in reality, scaling is a fundamentally different problem: one that requires replicable data infrastructure, consistent measurement, and clear organizational ownership. Most pilots are built for none of these.

Three Failure Modes That Kill Industrial AI at Scale

1. The Data Pipeline Can’t Travel

Most AI pilots are built on custom data pipelines. A data scientist works with an OT engineer to connect a specific historian to a specific model for a specific use case on a specific line. The pipeline works because it was hand-crafted for that context.

But when leadership asks for the same use case at another plant, the pipeline can’t travel. The PLC vendor is different. The tag naming convention is different. The historian exports in a different format on a different schedule. The data scientist built for one site’s specific parameters and requirements, not for portability.

The result is that the second deployment costs nearly as much as the first. The third costs the same again. Leadership sees linear cost for linear rollout and pulls funding. The AI worked, yet the economics didn’t.

2. There’s No Baseline to Prove ROI

A pilot that reduces unplanned downtime by 30% sounds impressive. But 30% of what? Measured how? Over what period, and compared to which baseline?

Most pilots measure success against a locally defined baseline that exists only at the pilot site. Dortmund measures unplanned downtime as any stop that is longer than fifteen minutes. Lyon counts every stop over five minutes. Pune doesn’t separate planned from unplanned maintenance windows in their historian.

When leadership asks for the enterprise business case, the pilot team can’t produce comparable numbers across sites. The $180,000 saved at Dortmund cannot be projected to Lyon without making assumptions that finance won’t accept.

The survey flags this directly: 39% cite budget and ROI uncertainty as a core challenge for establishing a reliable industrial data backbone. In practice, this often means: “We can’t prove the pilot’s value in a way that justifies enterprise-wide investment.”

3. Nobody Owns the Outcome End-to-End

In most industrial organizations, the AI pilot was sponsored by one team: maybe the innovation group, maybe the digital transformation office, maybe a forward-thinking plant manager. But production deployment requires coordination across OT (who controls the equipment), IT (who manages infrastructure and security), operations (who owns the process), and finance (who approves the budget).

When the pilot team hands off to production, nobody picks it up. OT doesn’t own AI. IT didn’t build the pipeline. Operations wasn’t involved in the design. The pilot sits in organizational no-man’s-land.

The survey captures this: 20% cite lack of leadership support as a core challenge. When no single leader owns the path from pilot to production, that means nobody can build the business case, secure the budget, or mandate the cross-functional collaboration required.

What Practitioners Who’ve Crossed the Pilot-to-Production Gap Did Differently

The survey’s leader-versus-laggard comparison reveals a consistent pattern among organizations that have moved past pilots. None of them scaled by replicating what the pilot team did. They rebuilt for production from the start.

They designed for portability before the first pilot. Instead of building a custom pipeline for one use case at one site, they invested in a shared data backbone that any use case could subscribe to. They standardized naming conventions, metadata, and data models upfront—so the second site didn’t require re-engineering.

They embedded measurement from day one. Before the pilot ran, they defined KPIs, established baselines using consistent methodologies, and agreed on how success would be calculated across sites. The business case was designed to be replicable, not retrofitted after the fact.

They assigned production ownership before the pilot ended, clarifying who would own the use case in production, how it would be maintained, and what governance would apply. The handoff from pilot to production was, instead, a transition within a team that had production responsibility from the beginning.

The survey’s architecture data supports this: organizations with MQTT widely deployed in production (22%) and a Unified Namespace in production (13%) are disproportionately represented among those who have scaled past pilots. They invested in infrastructure that makes the second use case cheaper than the first.

What This Means for 2026

Industrial AI is not failing because the algorithms don't work but rather because the surrounding systems can't support them at scale. Data pipelines that require custom engineering for every site, measurement practices that can't prove ROI consistently, and ownership models that leave accountability unclear are structural problems, not technical ones. Organizations that fix those problems before scaling will pull ahead. Those that keep running pilots without addressing the foundation will remain stuck in the 68%.

The full survey report breaks down the architectural and organizational patterns that separate organizations making progress from those spinning in place. It includes adoption data on MQTT, unified namespace, edge-cloud architectures, and cross-functional team models. If your AI roadmap depends on moving from pilot to production in 2026, the patterns in the report will save you months of trial and error.