From facts to causes

How can you use AI to establish evidence-based causes and prevent an operational incident from recurring?

1960 € per session, excl. VAT

In short

This seven-hour day, excluding breaks, structures an incident investigation, from collecting evidence trails through to the corrective action plan. Generative AI helps organise the facts and challenge hypotheses. Participants verify causal links, document uncertainties and define how to measure the effectiveness of the actions.

Why this training?

After a production stoppage or a supplier non-conformity, urgency encourages hasty conclusions: individual error, a material defect or a procedure not followed. Without a timeline or evidence, corrections risk addressing the symptom rather than the conditions that allow recurrence. AI can connect evidence trails and suggest lines of enquiry, but it can also fabricate a convincing explanation. The training requires facts, hypotheses and conclusions to be separated, followed by verification that considers opposing evidence before any decision involving operations or procurement. Ongoing case study: At « Odrance Assemblage », a fictitious organisation, a batch of non-conforming seals causes a line stoppage after a change of supplier. Orders, incoming inspections, anonymised emails and the production log contain contradictions that must be resolved before reaching a conclusion. Transfer to the workplace: For an upcoming closed incident, revisit the template with the quality manager, compare the proposed causes with the available evidence and schedule an effectiveness review after the corrections have been implemented.

Objectives of this training

  • Define the scope of an incident and its operational consequences.
  • Reconstruct a referenced timeline from evidence trails of different types.
  • Develop causal hypotheses with AI support.
  • Test each hypothesis against evidence and counterexamples.
  • Define corrective actions with effectiveness criteria.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Operations, production and maintenance managers involved in lessons-learned reviews.
  • Buyers, supplier quality managers and continuous improvement leads.

Prior skills

  • Know the stages of incident handling in your professional field.
  • Be able to formulate and refine a request in a generative AI assistant.

What you will work on

  1. Scope the investigation

    Objective: Define the problem without prejudging its cause. Scope, impacts, containment measures and the distinction between immediate safeguarding and lasting correction.

  2. Reconstruct events

    Objective: Establish a verifiable timeline. Sources, timestamps, document versions, contradictions and minimisation of transmitted data.

  3. Explore possible causes

    Objective: Explore several explanations without confusing plausibility with evidence. Cause-and-effect diagram, five whys, technical and organisational causes; limitations of overly linear chains.

  4. Test causal links

    Objective: Retain only defensible conclusions. Correlation, causation, counterexamples, contradictory evidence and failed preventive barriers.

  5. Link corrections to causes

    Objective: Build a verifiable action plan. Correction, prevention, prioritisation by criticality; implementation and effectiveness indicators.

  6. Defend the case file

    Objective: Assess the robustness of the final analysis. Critical review considering opposing arguments, traceability of conclusions and recurrence review.

What you take away

  • Scoping sheet and sourced timeline
  • Matrix of hypotheses, evidence and contradictions
  • Annotated causal tree with uncertainty levels
  • Corrective action plan and effectiveness review protocol

Method and assessment

  • Short instructional inputs followed by immediate practice using the ongoing case study.
  • Individual work followed by work in pairs, with peer review of outputs.
  • Facilitated group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Arrange access to a generative AI tool authorised by the organisation; the exercises use only fictitious or anonymised cases. Identify an internal incident report template without transmitting confidential documents.

Observable criteria: Each decisive event refers to an identifiable record. ; Facts, hypotheses and missing information are explicitly separated. ; Each retained cause has evidence and verifiable causal reasoning. ; Each action targets a documented cause and includes a person responsible, a deadline and an indicator. ; Any unsupported assertion produced by AI is removed or flagged..

Frequently asked questions

Can AI determine the cause on its own?

No. It suggests lines of enquiry; evidence from the field and validation by domain experts form the basis of the conclusion.

What should you do if the records contradict one another?

Keep the versions, flag the disagreement and define an additional verification step without inventing a resolution.

Can a real incident be used?

Only after anonymisation and validation of the data that may be transmitted to the authorised tool.

How do you verify a correction?

Define an observation period, a recurrence indicator and a check that the corrected barrier is functioning.

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