Build it right. Run it right.

By Tjidde Boers, Global Industry Principal, Assai

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Most discussions of operational excellence begin with what happens after an asset enters service: improving maintenance, reducing downtime, raising safety and efficiency, or helping people make better decisions. More recently, they have expanded to include artificial intelligence and its role in supporting operations.

These are all important. But they often start too late.

Many operational challenges can be traced to decisions made years earlier, during engineering and project execution. Long before the first operator takes control of a facility, and long before the first maintenance team performs an inspection, a foundation is already being laid, in the form of engineering information. Building an asset well means building that information well, not only the steel and concrete.

Engineering information outlives the project

Projects create physical assets, but they also create drawings, specifications, vendor documentation, certificates, operating manuals, change records and thousands of engineering decisions that explain how an asset was designed, constructed and commissioned.

When a project finishes, the physical asset remains. So does the information. The difference is that while the project team moves on, the information becomes part of daily operations and continues to influence decisions for years to come.

This is a pattern I see regularly. Industrial organisations are not struggling with a lack of information. They are struggling to connect information in ways that reflect how people actually work.

Engineering information therefore follows a very different timeline from the projects that create it. A project may last only a few years; the information it generates can stay relevant for decades, supporting maintenance, shutdowns, inspections, modifications, audits and future capital projects long after the original team has moved on.

Much of its value is realised later, when someone needs to rely on it.

Engineering information is one of the few things that connects every stage of an asset’s lifecycle. From design and construction through operations, maintenance and future modifications, it provides the continuity that allows organisations to understand how an asset was built, how it has changed and how it should be managed.

Information quality is decided at handover

Consider a large capital project. Thousands of documents move between EPC contractors, vendors, licensors and owner operators. Drawings are reviewed and revised, vendor packages are assessed, comments are resolved and decisions are documented. Eventually, all of it must be handed over to the teams responsible for operating and maintaining the facility.

When projects are viewed purely as construction exercises, attention focuses on schedule, budget and delivery. When they are viewed through the lens of lifecycle performance, another consideration emerges: the quality of the information being handed to the future. That quality has consequences that are easy to overlook at the time but expensive later. On one delayed refinery expansion, the owner was ultimately able to substantiate a warranty claim against the contractor, not because of the physical work but because document delivery and data quality obligations had been defined and enforced from the start, leaving an auditable record of what was promised and when.

The same quality becomes decisive when modifications are needed years later. Before engineers can design a change, they need a reliable understanding of the existing asset. Drawings may no longer reflect what is installed in the field, changes may have accumulated over many years, and documentation may be incomplete or hard to verify. Much of the early effort in a retrofit is spent establishing a trustworthy baseline.

How large that effort can be is easy to underestimate. On one brownfield plant, engineers were told the facility was documented across tens of thousands of process and instrumentation diagrams. After validation and consolidation, it was accurately described by closer to a thousand drawings reflecting what had actually been built. Had design work begun against the larger, inconsistent set, with mechanical from one version and instrumentation from another, those discrepancies would have propagated straight into construction. Resolving them at the front end is far cheaper than discovering them later.

That kind of work rarely receives much attention, yet it can determine how quickly a project moves and how much uncertainty carries through execution. When information is accurate and complete, engineers can focus on the problem in front of them. When it is not, field verification becomes necessary, assumptions have to be tested, and decisions take longer. The result is additional effort, additional cost and additional risk.

The challenge is not information management for its own sake. It is ensuring that people have access to reliable information when important decisions need to be made.

The foundation AI depends on

This is one of the reasons operational readiness depends so heavily on engineering information. Readiness is not simply about having procedures, systems or people in place. It is about ensuring that trusted information is available when it is needed.

That dependence grows as organisations adopt technologies such as digital twins, advanced analytics and artificial intelligence. There is understandable excitement around AI, and the pace of new capability is rapid. But industrial organisations face a particular version of a familiar problem, because engineering decisions depend so heavily on context.

When engineers investigate an operational problem, they rarely rely on a single document. Drawings, specifications, maintenance records, operating procedures and asset history all contribute to understanding what is happening and what should be done. The same principle applies to AI: a system is only as effective as the information and context available to it. Used well, that combination is powerful. On one project, a transmittal of tens of thousands of documents was checked for completeness automatically and returned as substantially complete within a weekend, with engineers verifying the output rather than assembling it by hand. Used on an incomplete or unreliable foundation, the same automation simply produces uncertainty faster.

This is why discussions about AI should begin with information quality, not because information management is more exciting than AI, but because it remains one of the most important and most overlooked elements of digital transformation. A strong information foundation lets organisations adopt new technologies with confidence. A weak one limits the value of everything built on top of it.

Operational excellence is shaped in both places: in how an asset is run, and in the thousands of decisions made earlier during engineering, project execution and information handover. The second is the part most often neglected.

The organisations that perform best over the long term understand this. They treat engineering information not as a project deliverable to be closed out, but as an operational asset whose value depends on how well it is built, maintained and trusted over time.

If you want to run it right, you have to build it right.

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