Refineries have long depended on experienced people who understand the assets, know the history and can make sound decisions under pressure. They know why a unit was modified, which drawing reflects the field condition, what failed during the last turnaround, which vendor recommendation was ignored for good reason, and who still remembers the full story behind a recurring problem.

That model is under strain from changes in the workforce.

A generation of engineers, operators, maintenance specialists and reliability experts is retiring. Fewer new graduates are choosing refining as a career path. Many facilities are operating assets that have been modified, expanded and maintained for decades, leaving critical information scattered across a wide array of documents, drives and individuals.

There is no shortage of data. Rather, most refiners have a problem of access, trust and context.

Artificial intelligence (AI) can help, but only when it is grounded in reliable refinery information. If the underlying information is scattered, stale, conflicting or disconnected from asset context, AI will reproduce those weaknesses at a faster speed. Practical value starts with a managed context layer that makes existing operational knowledge easier to find, understand and apply with confidence. Refiners who treat this knowledge as a managed asset will be better positioned to protect availability, improve reliability and bring the next generation of technical talent up to speed.

The refining industry is going through a significant demographic shift. Many of the people who built, operated and maintained today’s facilities are approaching retirement or have already left. Their knowledge accumulated over decades through operating experience, troubleshooting, turnarounds, failures, modifications and lessons learned in the field.

Much of that knowledge was never fully captured. It lives in memory, informal networks, old project files, handwritten notes, and the judgment of people who know where to look and what to trust. Some examples:

• An experienced engineer might know that a pump’s current operating envelope differs from the original design assumptions.
• A maintenance specialist might remember why a repair approach was selected during a constrained outage.
• An operator might know which alarm pattern usually precedes a particular unit constraint.
• A reliability lead might know that the most useful failure history is not in the newest report, but in an older turnaround file on a shared drive.

These examples are not exceptions. They are how refineries have often functioned. Knowledge has moved through mentorship, hallway conversations, personal files and the judgment of people who know where to look.

That transfer is becoming harder today. Experienced personnel are leaving, teams are leaner, and many new engineers are entering the industry with strong technical capability but less refining-specific experience.

At the same time, refining is competing for talent against sectors that many graduates view as more innovative or future-oriented, including renewable energy, battery manufacturing, hydrogen, carbon management, advanced materials and technology. The result is a widening gap between the complexity of refinery operations and the experience level of the available workforce.

Many refinery assets have been in service for generations. Over time, units are modified, expanded, debottlenecked, repaired, inspected and upgraded. Each project creates more information, but this knowledge is rarely centralized. Instead, critical details become scattered across a wide array of formal systems, informal records and individuals. A single piece of crucial context might be found within any number of sources:

• Engineering drawings
• Maintenance systems
• Project files
• Vendor documentation
• Inspection records
• Operating procedures
• Shared drives
• Personal files
• Tribal knowledge

Some records may be current. Others may be obsolete but still visible. Some may be technically correct but incomplete. Others may contain the missing explanation behind a decision that no longer makes sense on paper. Knowing where the current, authoritative and relevant information resides is what ultimately matters.

The issue is rarely the absence of information. Engineers and reliability teams do not simply need more documents. They need the right document, connected to the right asset, with enough context to determine whether it should influence the decision at hand.

A context layer organizes information by meaning, rather than by storage location alone. In refinery terms, that means connecting documents and records to the units, assets, equipment tags, process areas, failure modes, operating events, revisions, decisions and time periods they describe. It also means preserving source traceability so users can see where an answer came from and judge whether it should be trusted. Search is the user-facing capability. The context layer is what makes the search useful.

Engineers often spend valuable time searching for documents, validating revisions, tracking down historical decisions or reconciling conflicting sources. A keyword search can find files, but it does not always know which drawing is authoritative, which inspection report relates to the same equipment, which recommendation was superseded, or why two documents appear to conflict. It may return results without understanding the refinery meaning behind them. As assets age and documentation becomes more fragmented, that burden only increases.

For refiners, asset availability remains one of the most important drivers of financial performance. A single reliability event can result in lost production, reduced throughput, increased maintenance cost, schedule disruption and millions of dollars in business impact.

Yet in many organizations, experienced technical personnel still spend too much time looking for information instead of solving problems. Hours spent searching for information add up to time not spent improving reliability, resolving constraints or preventing the next event.

The industry has become very good at generating information. Most refineries have more documents, records, files, reports, drawings and system data than any individual could reasonable navigate. The challenge is making that information usable when decisions need to be made.

Most refiners understand the value of digital transformation, but discretionary projects compete with mandatory spending. Safety, environmental compliance, regulatory requirements, equipment replacement and turnaround needs often take priority.

This makes long-term efficiency projects harder to fund, especially when the value case depends on productivity, avoided downtime or improved decision-making.

AI initiatives face even more scrutiny. Many leaders have seen ambitious claims that failed to translate into measurable business results. Their skepticism can be reasonable. AI depends on the quality of the information behind it. When information is scattered, inconsistent, outdated or difficult to trust, AI produces limited value. The model may be advanced, but the outcome remains constrained by the underlying knowledge base.

In a high-consequence operating environment, AI should help engineers reach better decisions — not replace engineering judgment. Its value lies in reducing the time required to locate trusted information, connect related records, surface relevant history and point users back to the original source material.

The most effective starting point is simple: Make operational knowledge easier to navigate and apply.

When engineering information is connected and contextualized, people can move faster. Engineers can locate relevant documentation. Reliability teams can review asset history. Operations can resolve issues with better context. New employees can learn from prior decisions and lessons learned without depending entirely on informal networks.

The first wave of value is productivity. Engineers spend less time searching and more time solving. Reliability specialists gain faster access to historical information. Operations teams can respond more efficiently. New employees become productive sooner. Institutional knowledge becomes available across the organization instead of remaining concentrated in a small group of experienced personnel, who can spend less time acting as human search engines and focus more on solving higher-value technical problems.

This is where AI becomes practical. It helps people navigate complex information environments, but only after the organization has done the work to identify, structure and govern the information that matters.

Once trusted information is easier to access, broader opportunities begin to open up. Structured asset information can support faster engineering decisions, improved troubleshooting, stronger reliability analysis, accelerated onboarding, better knowledge retention, and more effective collaboration across disciplines. It also creates the foundation for higher-value AI use cases.

Instead of using AI as a stand-alone experiment, refiners can apply it to a trusted operational knowledge base. It can help identify similar issues, summarize long technical documents, point to source documents and help newer employees understand the background behind current practices. The technology demonstrates its utility when helping people find patterns, surface relevant history and make better decisions with less friction. Being able to trace information back to trusted sources is key to unlocking that value.

The refining industry faces a workforce, knowledge retention and operational continuity challenge.

Retirements are reducing institutional knowledge. Recruiting trends are tightening the future talent pipeline. Aging assets are increasing information complexity. At the same time, competitive pressure continues to demand higher availability, disciplined cost control, safe operation and faster response to constraints.

Refiners that activate their operational knowledge will be better prepared for this environment. Those that do not may still possess the information they need but fail to find it when it matters.

The opportunity hidden in this challenge is making operational experience accessible to every engineer, operator and reliability professional who needs it. The next competitive advantage will come from helping the right people find, trust and use the right knowledge when it matters. By building an operational knowledge foundation that connects trusted information across existing systems, organizations can leverage that context layer to enable better decision-making and make AI a trustworthy catalyst for more efficient operations.