Ask a master negotiator how they read a room and you will usually get a shrug. Ask a surgeon how they decide where to cut first, or a trader how they sense a market turn before the numbers confirm it, and the answer is the same kind of shrug. Expertise this deep runs on autopilot. The expert cannot see their own process because it stopped being conscious years ago.
This is the real obstacle standing between human expertise and AI systems making the right decisions. It is not a data problem but a structure problem. Before an AI can inherit a decision-making pattern, someone has to find that pattern first.
This article looks at what behavioural modelling is, the specific skills it demands, and how those skills can be used to produce frameworks such as the Galdren Lead Gen Decision and Judgement Framework for building AI systems that reason the way an expert reasons, not only the way a manual describes.
What Expert Behavioural Modelling Means
Practitioners use the word “modelling” to describe at least three different things, and the difference matters for what an AI system ends up inheriting.
One approach requires the modeller to absorb an expert’s patterning unconsciously first, and only analyse it afterwards. The modeller suspends any conscious, analytic attempt to understand the expert’s patterning during that initial assimilation stage, and only claims success once they can reproduce the pattern in new situations and draw out responses of comparable quality and speed to the original expert.
A second approach skips the immersive step and instead applies existing categories and labels to describe what an expert does. This can mean structured strategy elicitation that genuinely surfaces the expert’s underlying structure, or it can mean something far shallower: someone asks the expert a few questions, writes up a summary, and calls it a knowledge base. People use “modelling” for both. The results are not comparable. One reaches the structure underneath the expert’s behaviour. The other copies what is visible on the surface and stops there.
The third approach, now the most common one in AI projects, does not involve the expert at all. It works from what the expert has already produced: emails, documents, and speeches, parsed into a retrieval system the AI searches when it needs an answer. The AI is left to infer the underlying decisions, choices, and behaviour from the artefacts alone.
Most AI knowledge capture today uses this third approach, because it is faster and easier for everyone involved. It is also the approach least likely to produce a usable model. Feeding an AI what an expert wrote is not the same as feeding it how the expert decided what to write. The result is a system that can echo the expert’s language without inheriting the expert’s judgement.
True behavioural modelling sets a higher bar than any of these three. It insists someone tests the extracted pattern against the real world before anyone calls the job complete.
The TOTE: A Map of How a Decision Runs
The core unit of any modelling is the TOTE (Test, Operate, Test, Exit), a concept the psychologists George Miller, Eugene Galanter, and Karl Pribram introduced in their 1960 book on cognitive psychology, drawing on ideas from cybernetics. A TOTE breaks a piece of behaviour into a trigger (test), an action taken in response (operate), a check on whether the action worked (test again), and the point at which the process stops (exit). If the outcome is not reached, the person loops back through testing and operating until it has. The model can describe something as small as hammering a nail, or be nested to build a map of far more complex behaviour.
This is the piece most AI encoding projects skip. They capture what the expert decided. They do not capture the loop: what triggered attention in the first place, what the expert checked before acting, what told them the first attempt had not worked, and what specific signal told them to stop. Encode the decision without the loop and the AI inherits an answer, not a method. It tends to fail the moment conditions shift even slightly from the example it saw.
Strategy Elicitation: The Skill of Finding a Pattern the Expert Cannot See
Extracting a TOTE from an expert requires strategy elicitation, and it comes in two forms. Informal elicitation happens in conversation. People tend to run through their internal strategy as they describe a past experience, and simply asking “how did you know?” is often enough to draw the sequence out naturally. Formal elicitation is more deliberate. It works through a set sequence, because experts routinely leave out the step that matters most, not because they are hiding it, but because it happens too fast for them to notice.
From Elicited Pattern to Working System
Once the modeller has elicited a strategy, it needs a structure an AI can act on. Prompt engineering and knowledge graphs earn their place here. They translate the TOTE sequence and its trigger conditions into something the AI checks at each step, rather than a paragraph it reads once.
The Galdren Lead Gen Decision and Judgement Framework is one working example of this translation in practice. It runs three parallel layers: an Execution Layer where the AI acts as generator and architect, a Judgement Layer where it acts as coach, challenger, and critic, and an AI Behaviour Layer where it acts as recorder and analyst, shaping thinking through the interaction itself. Underneath sits a seven-step decision sequence: frame the problem, gather the evidence, list the options, weigh the trade-offs, make the decision, state a confidence level from 0 to 100 percent, and apply a reversal test asking what would change the decision-maker’s mind.
This sequence is a TOTE with the trigger and exit conditions made explicit. Frame and Evidence form the test that opens the loop. Options, Trade-offs, and Decision form the operate stage. Confidence and the Reversal Test form the second test, the check on whether the decision holds up, before the process exits. Structuring it this way is what lets the framework’s judgement training loop, Ask, Question, Push Back, Educate, Support, Iterate, work as a genuine feedback cycle rather than a checklist someone bolts onto the AI’s output after the fact.
Validation: Proving the Model Works
Any model is unproven until it produces the expert’s results, as confirmed by the expert. The modeller only claims success once they can reproduce the original patterning in new situations and get responses of comparable quality and timing to what the original expert would have produced. That is a demanding standard, and it should be. Extracted expertise that only works inside the interview room is not extracted expertise. It is a transcript.
For AI systems, this means human-in-the-loop review cannot stop at “does the output look reasonable”. It has to ask whether the AI, given a new situation the expert never discussed, reaches the judgement the expert would have reached. This is where the Lead Gen Decision and Judgement framework’s emphasis on validation and correction earns its keep. Experts review outputs against unfamiliar cases, flag the gap between what the AI did and what they would have done, and the difference points straight back to which part of the TOTE the modeller captured wrongly or left out.
The Difference Between a Coder and a Modeller
Anyone can prompt an AI with instructions that someone already wrote down somewhere. That is transcription, and it is useful, but it is not modelling.
A modeller does something else entirely. They sit with an expert who cannot explain their own genius, use structured elicitation to surface the trigger, the check, and the exit point the expert never noticed themselves, sort those pieces by the level they operate at, and only then hand the result to an AI system for testing against reality. A coder formats information that already exists in explicit form. A modeller extracts a pattern that has never been explicit, and proves it works before calling the job finished.
Three Problems Behavioural Modelling Has Not Solved
Several problems remain unresolved.
Depth of elicitation. Formal strategy elicitation takes time and skill most AI projects do not budget for. Rushing it tends to produce a shallow model dressed up as a deep one.
Drift. An expert’s TOTE for a given decision changes as their environment changes. A model captured once and never revisited tends to fall behind the person it was built from.
Proof of transfer. The real test of encoded expertise is whether it performs on cases the expert never saw, not whether it matches the training examples. Too few AI projects test for this directly.
The Discipline That Data Cannot Replace
AI systems thinking delegation will not advance much further on data volume alone. What moves it forward is the discipline of behavioural modelling: find the trigger the expert cannot name, map the loop they run without noticing, sort what you find by the level it belongs to, and refuse to call the job done until the pattern produces the expert’s results in someone else’s hands. Frameworks such as Galdren LGD show what this looks like once someone builds it into a working system. Applied properly, behavioural modelling gives AI encoding projects something most currently lack: a defined way to know whether the expertise was ever captured at all.