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One of the recurring problems in engineering companies is that project knowledge is scattered. Not the design documents themselves, but the reasoning behind the choices, the assumptions, the technical considerations and the logic behind engineering decisions. Capturing lessons learned is also largely a formality in many projects, and happens only at close-out; so most of the decisions and experience gained during execution are never recorded, and when senior engineers leave, part of the organisation's engineering memory and accumulated experience goes with them.

Checking hundreds of documents for deviations from standards and from the rest of the project record is a slow, highly detailed process that can run for weeks. The accuracy of that review depends on the concentration and experience of the engineers doing it. As the volume of documents grows, so does the likelihood that a significant deviation is missed.

Comparing several technical proposals depends in practice on the experience, memory and judgement of whoever is doing the evaluation. The result, seen time and again, is that items are missed during tendering — items that go on to affect the contract and to cost the organisation twice over.

In many organisations, a bad experience with a supplier on one project repeats itself on the next project in the same organisation, because nowhere in the system was it recorded in a citable form, with the detail that would make it useful.

Contractor data usually arrives late and in inconsistent formats. Consolidating, reconciling and correcting it takes time, and delays the final report. By the time the reports are ready, they do not necessarily give an accurate picture of where the project currently stands.

A significant share of deviations only comes to light once a project has reached installation, construction or commissioning. At that point, correcting a deviation can mean rework, replacing equipment, or delaying the project schedule. The cost and time to put it right are therefore usually far greater than they would have been had the same deviation been caught at design stage.

Control systems have been recording a considerable volume of operating data for years. It is usually summarised in periodic reports, but much of its analytical value goes unused. The data that already exists rarely turns into practical insight or better operating decisions.

Repairs usually begin after a failure has occurred, even though the early signs of it may have been visible in the equipment's own data weeks or months beforehand. Without continuous analysis of that data, the chance to catch it early is lost. What follows is an unplanned shutdown, a higher repair bill, and disruption to operations.

One of the main obstacles to bringing AI into industrial companies is organisational resistance to change — resistance that comes from doubt about the value and reliability of AI, and from concern about people being replaced. That outlook reduces internal engagement and means part of the organisation's time, energy and resources goes into overcoming internal resistance rather than into improving productivity and creating value. Building shared understanding across the organisation is therefore one of the basic preconditions for digital transformation.

The distance between industry expertise and data and AI expertise is one of the significant obstacles to defining solutions that work. The data specialist may not understand the process and its operating constraints well enough, and the process engineer may not be familiar with what AI models can and cannot do. In many cases the result of that gap is a solution designed around a minor or incorrect problem rather than the organisation's real one.