The request is not the problem

Gilbert, Mager and Pipe, Rummler and Brache, and the Dutch line of Arets, Overduin and Heijnen. Half a century of method, put within reach of every learning advisor.

A request is a solution in disguise

A manager comes to L&D and asks for training. The sentence sounds like a problem statement. It is a solution, chosen before anyone established what is actually stopping the result. Reaching for training before finding the cause is the most expensive habit in the field, and the Dutch literature gave it a name: the costly misunderstanding (Arets & Overduin, 2008; Arets & Heijnen, 2008).

The request is easy to act on, which is precisely the trouble. It arrives with the solution attached, a number of participants, sometimes a date. A department can process it without ever asking which result is missing, in whose work, and how anyone would see it. The programme runs, the dashboard looks healthy, and the problem sits where it sat.

What makes the habit expensive is not the invoice. It is that the request names the one cause a course can address, and that cause is rarely the one holding the result back. Meanwhile the working hours disappear, in numbers nobody put on the request.

This paper describes the alternative, which is old, well documented and until recently almost impossible to run on every request: find the cause first, then decide. And it describes what changes when every learning advisor has a performance consultant beside them at the intake, instead of one specialist for the few requests there is time for.

The request says training. The cause almost never does.

What a request really costs

50 people · 2 days · classroom

de-escalation training, complaint scores are down

€70k

working time50%

direct (trainer, venue)36%

travel14%

never on the request

1,000 people · 1 day

customer-experience training, sales per visit are falling

€720k

working time42%

direct (trainer, venue)42%

travel17%

never on the request

20,000 people · 8 hours · e-learning

mandatory compliance module, incidents are rising

€6.9M

working time93%

direct (licence, content)7%

travel0%

never on the request

Two things stand out the moment the numbers sit on the table. The invoice is the small number, and the large one is working time, which almost never appears on the request. And time scales without mercy. The e-learning for twenty thousand people looks cheap because it is digital; it consumes roughly 160,000 working hours.

None of this is controversial. It is arithmetic. What has changed is that the arithmetic now takes less time than reading the request it belongs to.

Most of the leverage sits around the person

Thomas Gilbert built the discipline on one observation: a person’s environment engineers performance far more than what sits inside their head (Gilbert, 1978). Knowledge, skill and motive matter, and they operate inside a work system that either makes good performance easy or quietly makes it impossible.

Four decades of quantitative work point the same way. A synthesis of meta-analyses across sixteen determinants of behaviour finds that knowledge, general skills, attitudes and beliefs have negligible effects as targets of an intervention; habits and social support have medium effects; and access, the material and logistic means that make a behaviour feasible, has the largest (Albarracín et al., 2024). A training request aims at the weakest determinant on that list.

the work around the person

expectations and feedback

tools, information, time

incentives and consequences

the person

knowledge and skill

capacity

motives

the only box training can fill

Work the case backwards

Performance is produced from left to right: people carry out tasks, tasks combine into processes, processes deliver results. A case gets analysed the other way. Start with the business result the organisation wants, then work back through the processes and the tasks to the place where performance actually leaks. Starting at the tasks, which is where the training request points, means starting at the wrong end (Rummler & Brache, 1995).

performance is produced this way

the person

the task

the process

the business result

a case is analysed the other waystart where the result is measured

1. The result

   Stated as a measurable outcome, not as an activity. “More customer focus” is a wish. “Repeat orders at 62 percent against a needed 75” is a case.

2. The gap

   The quantified distance between the result now and the result needed, with a value attached. Only a number tells you whether the problem is worth solving and whether it got solved.

3. The causes

   Separated cleanly from the symptoms, drawn from more than one source, and never accepted on the strength of a confident opinion (Mager & Pipe, 1997).

None of this takes months. A good performance analysis is lean and fast, aimed at the few causes that matter rather than at an exhaustive audit. Its output is a short, evidence-based diagnosis that decides whether training is the answer at all.

Why so few functions work this way

If the case is this strong and this old, the obvious question is why almost no L&D function runs on it. Not because the method is in doubt. Because the daily pressures of the job run against it at every turn.

The field has known how to do this for decades. It has not been able to do it on every request.

None of these barriers is an argument against the method. Each one is an argument for making the method cheap enough to run on every request, which is exactly what changed.

What the Performance Consultant AI does

The tool guides a conversation, not a form. It sits beside the learning advisor at the intake and runs the same five moves every time.

1. The request In plain language

   It starts with one field and one sentence: “a two-day sales training for all ten thousand reps, is training the fix?” No intake form, no template to fill in first.

2. The questions back About numbers, not wishes

   The tool asks where the problem shows up in the figures, who is missing which target, and where the revenue leaks. The advisor supplies the detail the organisation already has.

3. The data What the organisation knows

   Documents, dashboards and loose figures go in. The diagnosis rests on the organisation’s own evidence rather than on an assumption about it.

4. The gap, quantified Cause by cause

   Current and needed results side by side, and under each one the cause, tagged by factor, by level, and by how far the evidence carries.

5. The advice And the documents to use it

   Four versions of the same diagnosis, edited by the advisor, ready for the meeting where the decision gets made.

The method does not change. What changes is the scarcity: an analysis that took a specialist weeks now fits inside the intake conversation, on every request rather than the few there was time for.

The anatomy of a diagnosis

Diagnosis

retail · 1,000 people · request: customer-experience training

DIAGNOSTIC CONFIDENCE

moderate to high

THE GAP

sales per visit €18.40, target €21.00, worth about €2.6M a year

WHAT IS CAUSING IT

• out-of-stocks on the top 40 linesprocess · triangulated
• pricing errors at the registertools · substantiated
• no feedback on visit value per storeexpectations · hypothesis

RECOMMENDED NEXT STEPS

fix replenishment and the price file first; coach three stores; no company-wide training

STILL TO CONFIRM · does the pattern hold in the four stores that already meet the target?

One conversation, four documents

The person who owns the money, the manager who owns the team and the analyst who owns the detail do not read the same document. The same diagnosis therefore comes out four ways, from one conversation, without a second analysis.

one intake conversation

Fullthe complete analysis: gap, every cause with its evidence and confidence, next steps, open questions

Execthe bottom line, the money, the decision, in three short paragraphs a board can act on

Managerwhat to do next week: the actions, the owners, the numbers to watch

Slidesa board-ready deck: the gap, the cause, the case, generated rather than built by hand

This is what changes the report L&D brings to the table. For years the function reported what it did: completions, participants, satisfaction. It can now report what it protected and what it made possible, in the language the board already speaks.

The dividend as a standing metric

dividend this month

€1.42M

61 requests reviewed · 12 redirected

dividend by month

J

F

M

A

M

J

J

A

S

O

N

D

completions

94%

skills coverage

78%

engagement

4.2/5

dividend

€1.42M

Performance consulting is the crossing

The Arets L&D Business Models place four ways of working on one rising line. The two on the left begin after someone else has decided that learning is the answer. The Performance Partner begins before that decision, establishes the cause and prescribes what the business needs, training or not. The Performance Consultant AI is what makes that crossing survivable on a normal working day.

Business impact

Order Taker

Learning Specialist

Performance Partner

Value Creator.

HR-drivenBusiness-driven

L&D maturity

1. Order Taker

   Takes requests and delivers courses. Attendance counts as success. The cause of the problem never gets established, so the result rarely moves.

2. Learning Specialist

   Designs excellent evidence-informed learning, and starts only once somebody else has decided that learning is the answer.

3. Performance Partner

   Establishes the cause first and prescribes accordingly: process, tooling, guidance, or training where training is genuinely the answer. Includes 02.

4. Value Creator

   Performance consulting and evidence-informed organisational learning, reported in the organisation’s own numbers. Includes 02 and 03.

The programme, the tool, the author

Selected sources

• Albarracín, D., Fayaz-Farkhad, B., & Granados Samayoa, J. A. (2024). Determinants of behaviour and their efficacy as targets of behavioural change interventions. Nature Reviews Psychology, 3 (6), 377–392. https://doi.org/ 10.1038/s44159-024-00305-0
• Arets, J. (2026). Learning that crosses the levels: The Learning Conversion Framework [White paper]. Tulser.
• Arets, J., & Heijnen, V. (2008). Kostbaar misverstand: van training naar business improvement. Academic Service.
• Arets, J., & Overduin, B. (2008). Liever (g)een training: op weg naar performanceverbetering. Academic Service.
• Binder, C. (1998). The six boxes: A descendant of Gilbert’s behavior engineering model. Performance Improvement, 37 (6), 48–52. https://doi.org/10.1002/pfi. 4140370612
• Gilbert, T. F. (1978). Human competence: Engineering worthy performance. McGraw-Hill.
• Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.
• Mager, R. F., & Pipe, P. (1997). Analyzing performance problems: Or, you really oughta wanna (3rd ed.). Center for Effective Performance.
• Rummler, G. A., & Brache, A. P. (1995). Improving performance: How to manage the white space on the organization chart (2nd ed.). Jossey-Bass.
• Stolovitch, H. D., & Keeps, E. J. (Eds.). (1999). Handbook of human performance technology (2nd ed.). Jossey- Bass/Pfeiffer.