---
title: "L&D Leaders: Capture How Top Performers Decide Before Building Courses"
description: Diagnose the capability gap, then capture expert judgment with CIT interviews, structured elicitation, and scenario simulations. Test before you build...
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# L&D Leaders: Capture How Top Performers Decide Before Building Courses

[Mark Ondash CPTD® MPC™](https://cognistry.co/edge/author/mark-ondash-cptd-mpc)

 Sep 29, 2026, 9:30:00 AM ·  16 min read

![L&D Leaders: Capture How Top Performers Decide Before Building Courses](https://media.babylovegrowth.ai/blog-images/organization-48833/1790650869313_Decorative-expert-judgment-title-card.jpeg)

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#### About the Author

[Mark Ondash CPTD® MPC™](https://cognistry.co/edge/author/mark-ondash-cptd-mpc) mark.ondash@digitalcommand.co

Mark Ondash, CPTD |MPC | is a co-founder of Cognistry. His work focuses on helping organizations overcome Data Drag and build decision capability in the AI economy through simulation-based capability development

[View all posts](https://cognistry.co/edge/author/mark-ondash-cptd-mpc)

Start by diagnosing whether learning is even the right response, then use structured elicitation to capture how your best performers actually decide. Run the [Critical Incident Technique](https://digital.ahrq.gov/health-it-tools-and-resources/evaluation-resources/workflow-assessment-health-it-toolkit/all-workflow-tools/critical-incident-technique), individual expert elicitation, and participatory modeling together. Convert what you learn into decision frameworks and scenario seeds, then test them against real outcomes before you build a single course.

---

> **TL;DR:**
> 
> - Most organizations skip diagnosing whether a performance gap is due to capability issues, risking training for non-structural problems like process flaws or policy gaps.
> - Valid decision data comes from expert interviews focusing on specific incidents, not general descriptions, and should be complemented by participatory modeling for shared understanding.
> - Turning raw judgment into measurable assets requires framing decisions as conditional rules and attaching metadata like context, risk, and outcomes to support testing and application.
> - Practice environments should simulate real decision points and track decision signals with live data, rather than rely on completion rates or memory recall.
> - Regular governance includes comparing captured decisions to actual outcomes, recording disagreements, and updating models when process changes or recurring errors occur.

---

Cognistry

Build Capability From Real Evidence

 

Cognistry helps enterprises diagnose capability needs, capture operational judgment, and measure outcomes back to the business.

[Explore Cognistry](https://cognistry.co)

## Table of Contents

- [Diagnose first: define what capability you need and the evidence to gather](https://cognistry.co/edge/capture-how-best-people-make-decisions#diagnose-first-define-what-capability-you-need-and-the-evidence-to-gather)
- [Capture methods: run CIT interviews, structured elicitation, and participatory modeling](https://cognistry.co/edge/capture-how-best-people-make-decisions#capture-methods-run-cit-interviews-structured-elicitation-and-participatory-modeling)
- [Structure captured judgment: decision frameworks, competencies, and scenario seeds](https://cognistry.co/edge/capture-how-best-people-make-decisions#structure-captured-judgment-decision-frameworks-competencies-and-scenario-seeds)
- [Design practice and measurement: build simulations and telemetry that test decisions, not recall](https://cognistry.co/edge/capture-how-best-people-make-decisions#design-practice-and-measurement-build-simulations-and-telemetry-that-test-decisions-not-recall)
- [Validate, govern, and refresh: governance for trustworthy captures](https://cognistry.co/edge/capture-how-best-people-make-decisions#validate-govern-and-refresh-governance-for-trustworthy-captures)
- [What practitioners get wrong about capturing expert judgment](https://cognistry.co/edge/capture-how-best-people-make-decisions#what-practitioners-get-wrong-about-capturing-expert-judgment)
- [How Cognistry helps: diagnosis-first capture, simulations, and measurable outcomes](https://cognistry.co/edge/capture-how-best-people-make-decisions#how-cognistry-helps-diagnosis-first-capture-simulations-and-measurable-outcomes)
- [Authoritative reading: CIT, elicitation, scenario design, and competency modeling](https://cognistry.co/edge/capture-how-best-people-make-decisions#authoritative-reading-cit-elicitation-scenario-design-and-competency-modeling)
- [Sources](https://cognistry.co/edge/capture-how-best-people-make-decisions#sources)
- [FAQ](https://cognistry.co/edge/capture-how-best-people-make-decisions#faq)

## Diagnose first: define what capability you need and the evidence to gather

Before you build that course, find out whether the gap is even a capability problem. Most organizations skip this step and go straight to content. That is how training gets built for a process failure, a tooling gap, or a policy nobody enforces.

Start with the business outcome that is underperforming. Trace it back to the decisions that shape it: where do discretionary judgment calls actually move the number? That is where capture belongs, not in every job function that touches the workflow.

Then gather a minimal evidence set before committing to any build:

- Strategy goals the team is accountable for, stated in plain business terms
- Recurring frontline errors or friction points tied to the outcome
- Quality incidents or near misses that show where judgment failed or succeeded
- KPIs that move when discretionary decisions go one way versus another

Once that evidence is in hand, apply a quick heuristic. If the gap is environmental (unclear process, missing tool, conflicting incentive), fix the environment. If top performers consistently outperform others under the same conditions, you have a genuine capability gap worth capturing. Cognistry’s [diagnostic approach](https://cognistry.co/strategy-transformation) treats this as the first checkpoint, not a formality.

## Capture methods: run CIT interviews, structured elicitation, and participatory modeling

Once diagnosis confirms a real capability gap, the work shifts to getting usable decision data out of your best people’s heads. Three methods do this reliably.

1. **Critical Incident Technique.** Ask experts for one specific incident, not a general description of their job. Probe the context, the decision made, and the outcome in that order, since CIT interviews built around concrete events produce sharper judgment data than abstract descriptions of “how I usually handle this.” A [realist evaluation applying CIT-style probes](https://pmc.ncbi.nlm.nih.gov/articles/PMC7249649/) found that asking for one positive and one negative incident, rather than a general narrative, produced richer context-mechanism-outcome patterns useful for refining program theory.
2. **Structured elicitation.** Pilot your protocol before running it at scale, train experts to reduce anchoring and overconfidence, and elicit individually rather than in group settings that can flatten disagreement. Reference-case methods for expert elicitation recommend documenting uncertainty and provenance alongside the judgment itself, not just the final answer.
3. **Participatory modeling.** Bring subject-matter experts into the room to map tacit narratives into shared models. [Graphical aids and card-sorting tasks](https://www.pnas.org/doi/10.1073/pnas.1319946111) help experts surface the factors that actually drive a decision and separate real influence from simple uncertainty.

Logistics matter as much as method. Brief interviewers on neutral probing language, record and transcribe sessions rather than relying on notes, and watch for conflicts of interest when an expert’s incident selection flatters their own performance.

**Pro Tip:** *Run two or three pilot interviews before scaling the protocol. Bad questions compound across dozens of sessions.*

## Structure captured judgment: decision frameworks, competencies, and scenario seeds

Raw interview transcripts are not usable artifacts. The next step turns captured judgment into structures your team can build from.

Translate each incident into a conditional rule: when this condition appears, the expert weighed these tradeoffs and chose this path. That “if, when” framing is what makes a captured decision teachable instead of anecdotal.

![Branching conditional decision rule illustration](https://media.babylovegrowth.ai/blog-images/organization-48833/1790651023383_Branching-conditional-decision-rule-illustration.jpeg)

From there, draft observable task and key behavior statements. Write them so a manager can watch a person work and confirm the behavior happened. Vague statements like “shows good judgment” cannot be tested. “Flags a supplier delay within one business day and escalates to procurement” can.

Each scenario you build from this needs metadata attached:

- Context tags describing the situation the decision occurred in
- The role responsible for making the call
- Risk level if the decision goes wrong
- The measurable lever the decision actually moves
- The expected outcome when the decision is made well

Resist the urge to collapse everything into one canonical process. Keep multiple successful patterns and the contextual triggers that separate them, since experts rarely converge on a single method for the same result.

## Design practice and measurement: build simulations and telemetry that test decisions, not recall

Artifacts only prove their worth when someone has to use them under pressure. That means building practice environments that test decisions, not memory.

Good scenario design starts with a model of the learner or team facing the decision, adapts in real time based on the choice made, and ties every branch back to an observable outcome rather than a quiz answer. [Competency-based scenario design guidance](https://www.adlnet.gov/assets/uploads/Vol%209_CompetencyBasedScenarioDesignBook_Complete_Final_021722v2.pdf) recommends structuring these as discrete experience events, or xEvents, so each decision point can be scored on its own rather than folded into a single pass or fail grade.

Measurement follows the same logic. Track what someone chose and why, alongside the operational signal that choice would have triggered in the real workflow. Completion rates tell you nothing about judgment.

- Log activity-level xEvents tied to specific decision points, not just module completion
- Capture decision timestamps to see how quickly judgment forms under pressure
- Attach context labels so results can be filtered by scenario type later
- Compare simulated choices against the outcomes those choices produced in the model

**A pilot round is where captured rules meet reality.** Stress-test the rules against live operational records before scaling, and treat every disagreement between the model and reality as a signal worth documenting rather than a bug to quietly fix. Scenario-based approaches using xEvents allow adjudication logic to adapt scenario difficulty automatically, which keeps the practice environment honest as more data comes in.

## Validate, govern, and refresh: governance for trustworthy captures

A capture is a hypothesis, not a finished artifact. Treat it that way and build a governance loop around it.

1. **Pilot against operational records.** Compare the captured decision model to what actually happened in the business, and document where the two diverge and why.
2. **Preserve disagreement.** When two experts solve the same problem differently, record both patterns and the context that triggers each one instead of forcing a false consensus, since [differing expert processes can produce similarly strong results](https://business.columbia.edu/faculty/research/process-performance-paradox-expert-judgment-how-can-experts-know-so-much-and).
3. **Set refresh triggers.** A process change, a new product line, or a recurring error pattern should automatically reopen the capture for review on a set cadence.
4. **Record provenance.** Log who was interviewed, when, and how the data was analyzed so business owners can audit the artifact rather than take it on faith.

Skipping this loop is how a good capture goes stale within a year of the workflow it was built to represent.

## What practitioners get wrong about capturing expert judgment

Three mistakes show up again and again. The first is capturing polished, after-the-fact rationales instead of what actually happened in the moment. Experts tell better stories in hindsight than they lived in real time, and a good interviewer probes past the tidy version. The second is skipping the pilot test because the deadline is close. That is exactly when a bad protocol does the most damage, since it gets repeated across every session that follows. The third is treating a process failure like a capability gap and building training for a problem training cannot fix.

This week, do three things: run a micro-diagnosis on one underperforming outcome, schedule two CIT interviews with your strongest performers in that area, and draft one scenario seed from what you learn. Small and specific beats broad and theoretical every time.

> *— Brian*

## How Cognistry helps: diagnosis-first capture, simulations, and measurable outcomes

This approach focuses on diagnosing what capability the work requires, grounding the design in the organization’s own evidence, and only then building the practice environment that develops judgment. That order matters. It’s the difference between a course nobody applies and a decision framework your team actually uses under pressure.

![Cognistry](https://csuxjmfbwmkxiegfpljm.supabase.co/storage/v1/object/public/blog-images/organization-48833/1785714030894_cognistry.jpg)

The platform maps capability signals from strategy, frontline friction, and quality data before recommending a build. From there, teams move into structured capture and scenario design, then into simulation and measurement tied back to the business outcome that started the diagnosis.

Several entry points support different stages of this work:

- Platform tools for the full diagnosis-to-measurement workflow
- Signal mapping for converting organizational evidence into capability signals
- Authoring tools for converting captured judgment into assets
- Simulation features for building decision practice environments
- Adoption support tools once the capability is built

Start with a capability diagnosis on the [Cognistry Platform](https://cognistry.co/platform) or request a demo to see how capture and simulation connect to your own evidence.

## Authoritative reading: CIT, elicitation, scenario design, and competency modeling

For readers who want to go deeper into the methods behind this approach, five sources stand out. The AHRQ toolkit on the Critical Incident Technique explains the core interview method. A realist evaluation study shows CIT applied to refine program theory. The ADL competency-based scenario design guide covers scenario and xEvent design in depth. Two elicitation references, one on reference-case methods and one on elicitation protocols from PNAS, cover how to run structured sessions without bias.

## Sources

- [Critical Incident Technique (CIT) — AHRQ digital toolkit](https://digital.ahrq.gov/health-it-tools-and-resources/evaluation-resources/workflow-assessment-health-it-toolkit/all-workflow-tools/critical-incident-technique)
- [Application of the critical incident technique in refining a realist initial programme theory — PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC7249649/)
- [Competency-based scenario design — ADL](https://www.adlnet.gov/assets/uploads/Vol%209_CompetencyBasedScenarioDesignBook_Complete_Final_021722v2.pdf)
- [Expert elicitation: strategies and protocol considerations — PNAS](https://www.pnas.org/doi/10.1073/pnas.1319946111)

## FAQ

### What is the first step in capturing expert decision-making?

Run a capability diagnosis before anything else, tracing the business outcome back to the specific decisions that move it. Only after confirming a real capability gap should you move into elicitation methods like the Critical Incident Technique.

### How is the Critical Incident Technique used to capture judgment?

Interviewers ask experts to recall one specific incident and probe the situation, the decision made, and the outcome in sequence. This produces sharper data than asking someone to describe their general approach, and a realist evaluation study found the incident-based format generates richer patterns for training design.

### Should we capture a single “best” decision process or multiple patterns?

Preserve multiple successful patterns along with the context that triggers each one rather than forcing a single canonical process. Research on the process-performance paradox shows experts can use different methods and still reach strong results.

### How do we turn captured interviews into training simulations?

Convert incidents into conditional decision rules, then attach scenario metadata like context tags, risk level, and expected outcomes to each one. From there, competency-based scenario design recommends structuring practice around discrete experience events that can be scored individually.

### How often should captured decision models be refreshed?

Set refresh triggers tied to events like a process change, a new product line, or a recurring error pattern, rather than a fixed calendar date alone. Pilot the captured model against operational records regularly and document any variance as a signal to revisit the capture.

## Recommended

- [Courses Don’t Build Capability. Decisions Do](https://cognistry.co/edge/courses-dont-build-capability.-decisions-do)
- [Decide Build or No Build in 2–4 Weeks With Capability Mapping for L&D](https://cognistry.co/edge/capability-mapping)
- [Build Capability Not Courses: Learning Architecture for Enterprise L&D](https://cognistry.co/edge/learning-architecture)
- [Decision Intelligence in L&D: A Playbook for Enterprise Teams](https://cognistry.co/edge/decision-intelligence-in-ld)

[Enterprise Learning](https://cognistry.co/edge/tag/enterprise-learning)

## Continue Reading

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