Everyone wants AI. Few organizations are actually ready for it.
Executive teams are exploring AI-powered pilots, predictive maintenance, operational intelligence and digital twins. Technology vendors promise faster decisions, improved reliability and greater efficiency. Organizations are eager to understand how AI unlocks value from decades of operational data.
Yet most hit a wall before they leave the pilot phase.
The reason isn’t technology. It’s data.
Between 70% and 85% of AI failures are linked to poor data foundations, not algorithm problems. At the same time, 63% of organizations either lack or are unsure whether they have the data management practices needed to support AI. The gap isn't between those with AI and those without. It's between those with trusted data and those without.
The real data problem
Industrial organizations have spent years collecting more data. Sensors. Historians. New systems. The result: more information than ever before.
The problem: most of it remains disconnected.
The world generated approximately 181 zettabytes of data last year. Between 60% and 85% of enterprise data is never analyzed. In industrial environments, 85% of IoT data goes unused. These numbers don't reflect a data shortage. They reflect a usability problem.
Engineering records live in one system. Maintenance histories in another. Operational data somewhere else entirely. Teams spend more time locating, validating and reconciling information than using it.
Collecting more data doesn’t solve this problem. Creating trust in existing information does.
Context beats volume
The biggest misconception about AI is that more data automatically means better outcomes.
It doesn't. Context matters more than volume.
AI systems can process enormous amounts of information, but they cannot understand the operational context behind it. They depend on engineering records, equipment relationships, asset hierarchies and operational history to provide meaning. Without that context, you have data without understanding.
An AI model can identify a pattern. It cannot verify that the underlying information is accurate. It can surface recommendations. It cannot confirm that engineering records reflect current conditions.
The value of AI is limited by the quality of information supporting it. Period.
Engineering drawings, equipment specifications, asset records and change histories provide the context needed to understand how assets were designed, how they operate and how they've evolved. Without this foundation, AI gives you patterns. With it, AI gives you insight.
Digital twins need trusted information
The same principle applies to digital twins.
Organizations treating digital twins as core to their digital transformation strategy see them as a way to create visibility, improve planning and make better decisions. That promise depends on one thing: the engineering information behind the twin.
If asset records are incomplete, the digital twin is incomplete. If engineering changes don't get reflected in documentation, the digital twin no longer reflects reality. If ownership is unclear and information quality deteriorates over time, trust in the digital twin declines along with it.
Many organizations focus on building the twin while overlooking the engineering information required to sustain it. The result: a sophisticated technology platform supported by information that people don't trust.
That's not transformation. That's theater.
Information problems start in the project
The organizations that move from pilots to scaled AI don't wait until handover to build trusted information foundations. They start during the project.
They establish engineering data requirements in project and EPC contracts, defining what information must be captured, in what format and at what level of detail. They conduct milestone database handovers throughout the project to identify and resolve information quality issues while the project team is still engaged. Engineering deliverables are validated against field conditions before they're finalized, ensuring drawings and records accurately reflect what was built.
By the time handover occurs, engineering information has already been reviewed, validated and trusted. Operations inherits information ready to support AI rather than spending years correcting it.
Organizations that wait until operations take over to address information quality are already behind. The ones that successfully scale AI build accountability into engineering information from the start.
Before you automate, build trust
There's a framework worth following for organizations looking to scale AI.
First, establish visibility into existing information. You cannot manage information you cannot find or understand.
Second, address issues at the source by defining engineering data requirements early and validating engineering information throughout the project lifecycle. Information problems become exponentially more expensive when they reach operations. Early intervention is critical.
Third, connect information silos. Engineering, operations and maintenance teams need a shared view of asset information. Isolated systems and disconnected records make AI impossible to scale.
Finally, build trust before automating. AI, digital twins and advanced analytics all depend on information that people trust. If teams don't trust the underlying data, they won't trust the AI sitting on top of it.
These steps don't generate the excitement of launching a new AI initiative. They're often the difference between successful transformation and expensive failure.
Operational intelligence starts with information
Digital transformation doesn't mean deploying new technology. It means enabling better decisions.
Operations and maintenance teams make thousands of decisions every year. The quality of those decisions depends on the quality of information available. When engineering, maintenance and operational information remain disconnected, teams waste time searching for answers, validating records and reconciling conflicts.
Connecting engineering, maintenance and operational information create the context AI needs to generate meaningful insights. Engineering information explains how assets were designed and intended to operate. Maintenance records provide historical performance. Operational data reflects current field conditions.
Together, they create a more complete picture of the asset and enable faster, more informed decisions.
This is where operational intelligence begins, not with AI, but with trusted information connecting everything.
Who leads the AI era
Organizations asking how to prepare for the next wave of industrial innovation often get the wrong answer.
They don't need the largest technology budgets or the most ambitious transformation programs. They need something more fundamental.
They need to know what information they own. Where it resides. Whether it can be trusted.
The organizations that will get the greatest value from AI, digital twins and advanced analytics aren't the ones with the fanciest technology. They're the ones with foundations solid enough to build on.
Because while AI may represent the future of industrial operations, trusted engineering information remains the foundation that makes that future possible.
Everything else is built on top of that.