The Problem Nobody Likes to Talk About

Every process plant I have walked into in the last four decades has faced the same awkward truth: the distance between what an operator is expected to do and what they have actually been trained to do is larger than anyone publicly admits.

We have Operator Training Simulators (OTS) for this. They have existed since the 1980s. The major vendors — Yokogawa, Honeywell, AVEVA, Omega Simulation, ABB, Corys, and specialist firms like Siminfosystems Pvt Ltd — have built genuinely impressive dynamic simulation engines.

And yet. Walk into the training room of a large process industry and you will usually find one of three things:

  • An OTS that is used heavily during the first six months of a greenfield project and then gathers dust
  • An OTS that is used sporadically because the instructor left and no one else knows the scenarios
  • An OTS that runs during the annual refresher week and then sits idle for another year

The technology is not the bottleneck. The human layer around it is.

That is where AI is about to make a difference — not by replacing the simulator, but by finally solving the problem of who helps the trainee when the instructor is not in the room.

What an OTS Actually Is

For the sake of non-specialist readers, a quick grounding.

An Operator Training Simulator is a software twin of a real process plant. It models the process dynamics — how pressure, temperature, level, flow, and composition respond to operator actions — accurately enough that a trainee sitting at a simulated console experiences something very close to the real control room.

A good OTS does four things well:

  • Normal operations — the routine of keeping a running plant running
  • Startup and shutdown — the highest-risk phases of any plant’s life
  • Abnormal operations — what happens when something goes wrong
  • Emergency response — what happens when many things go wrong at once

Alongside the simulator, there is usually an Instructor Station from which a trainer controls the session: loading the initial plant condition, injecting malfunctions, speeding up or slowing down simulation time, and evaluating how the trainee performed.

This is solid, well-understood technology. The installed base across industries and academia is enormous. And this is exactly why the gap is so striking — we have the simulators; we do not have enough capable humans around them.

The Six Human Failures AI Is Beginning to Address

When you look closely at where traditional OTS falls short, the failures are almost always human, not technical:

1. The Instructor Shortage

A good OTS instructor is a combination of process engineer, operations veteran, and patient teacher. These people are rare, expensive, and usually too busy on real plant problems to run training sessions consistently.

2. The Self-Study Vacuum

A trainee who wants to revise at 9 pm after their shift has nowhere to go. The simulator might be sitting there, but without a guide, they will not know what to practice.

3. The Assessment Gap

Instructors intuitively know whether a trainee performed well. Converting that intuition into objective, repeatable assessment across dozens of trainees, shifts, and sites is hard. Most organizations do not try.

4. The Documentation Paradox

Plant operating manuals, startup procedures, control narratives, and ESD philosophies exist, usually as thick PDF documents that trainees are expected to read. Almost nobody reads them cover to cover. The knowledge is there; the interface to it is wrong.

5. The Knowledge Decay Problem

What an operator learns during initial training fades over months. Refresher training is typically annual, often in a classroom, and rarely tailored to what a specific operator actually struggles with.

6. The Onboarding Cost

Every new hire requires weeks of instructor time to get to basic operator competency. For a large operating company with routine attrition, this is a permanent tax on both productivity and plant safety.

AI is now in a position to reduce each of these six failures. Not eliminate them. Reduce them.

What AI-Enabled OTS Actually Looks Like

Disclosure: I work closely with Siminfosystems Pvt Ltd on their AI-enablement program for ProSimulator. Where I reference other vendors, I’m drawing on public information and industry observation.

Guided Tours of the Operator Station and Instructor Station

An AI-driven guided tour — a twenty-to-thirty-minute narrated walkthrough — takes the trainee through every major screen of the Operator Station. The same applies to the Instructor Station. This compresses what used to be a two-day hand-holding session into a self-paced experience.

Process Description Walkthroughs

An AI-driven process description walkthrough breaks the model into logical segments — feed preparation, reaction section, separation, product handling, utilities — and explains each segment with the clarity a good process engineer would bring.

Control Philosophy Walkthroughs

An AI walkthrough explains every major control loop in the model, its type (cascade, split range, ratio, feedforward, three-element), its tuning rationale, and what happens when it fails or is overridden.

ESD System Walkthroughs

An AI walkthrough of the Emergency Shutdown system — what instruments protect the plant, what conditions cause a trip, and the correct reset sequence.

Three Levels of Startup Training

Level 1: Observation (Automatic Startup) — The AI drives the plant from cold-start to steady state automatically. The trainee watches and listens to voice-over explaining the sequence.

Level 2: Supervised Practice (Guided Startup) — The AI guides the trainee step by step. The system tracks progress and only permits advancement when each step is executed correctly.

Level 3: Independent Execution (Manual Startup) — The trainee performs the full startup independently. The system logs alarm excursions, procedural deviations, and timing data.

The progression from watching to guided practice to independent execution is how humans actually learn complex motor-cognitive tasks. Flight training has used this structure for decades. It is overdue in plant operator training.

Contextual Chat — A Patient Instructor Available at 9 PM

A trainee operating the simulator can ask questions through a chat interface at any point. A well-designed chat layer responds with context-aware answers informed by the current state of the simulator, the loaded model, and the operating procedure.

The trainee has, for the first time, something like a patient instructor available at all hours — one who does not get tired, does not lose patience on the tenth repetition of the same question, and does not get pulled away to handle a real plant upset.

Adaptive Quizzing and Assessment

After a module is completed, the system generates questions relevant to what was covered, tracks progress, and adapts to where the trainee struggled. This produces what operating companies have wanted for decades: an objective, comparable, trendable measure of operator competency.

Certification as the Closing Loop

Tying the quiz to a certification credential turns training from an expense into an asset. Universities particularly appreciate it because it gives students something concrete to show recruiters.

Features Worth Building, But Not Yet Built

  • Adaptive difficulty — the AI adjusts scenarios based on trainee performance
  • Natural-language malfunction invocation — instructors type scenarios in plain language instead of selecting from menus
  • Post-session debrief reports — AI produces natural-language analysis of what the trainee did well and where they struggled
  • Scenario library generation — from P&IDs and historical incidents, AI proposes training scenarios
  • Voice-interactive field operations in VR — combining AI chat with 3D virtual plant environments for crew communication training

What AI Is NOT Going to Do

  • It will not replace the dynamic simulation engine
  • It will not replace human instructors entirely — it shifts the ratio of instructor-hours to trainee-hours
  • It will not produce hallucination-free answers — mitigation requires strict grounding in plant-specific knowledge
  • It will not be cheap to build well — value comes from careful per-model curation
  • It will not fix a badly designed simulator — AI is only as useful as the simulator it sits on

What Should a Training Department Do Now

  • Do not wait for your vendor to package all of this. Start asking now which features they will offer and when.
  • Start with the common layers — Instructor Station and Operator Station guided tours are lowest-risk, highest-impact.
  • Pick a pilot model or two. Build the full AI stack on those. Measure the impact. Expand from there.
  • Invest in the curation — assign a process engineer to own the knowledge base and quiz bank for each model.
  • Measure what matters — track training hours saved, assessment scores, time to competency for new hires.

Closing Thought

AI-enabled OTS is the next version of the same idea that Operator Training Simulators were in the 1980s. It will not arrive all at once, and it will not land evenly across the industry. But the operating companies and educational institutions that engage with it now will have better-trained operators, safer plants, and more employable graduates than those that wait.

Originally published in the Reinvention in the AI Era newsletter on LinkedIn.