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1. Lessons Learned

Purpose

Structuring and documenting insights gained so that future AI projects benefit from them.

1. Objective

We formally close the project by structuring, documenting and making available the insights gained for future AI projects within the organisation.

2. Entry Criteria

  • Gate 4 (Go-Live) has been approved and the system has been handed over to the management organisation.
  • All project members are available for the closing session.
  • The project dossier (artefacts, validation reports, decision log) is complete.

3. Core Activities

Lessons Learned Session

Organise one structured closing session of 3 to 4 hours with the full project team. Use the 4L format:

L Question Focus
Liked What worked well and do we want to keep? Strong approach, good collaboration
Learned What did we learn that we didn't know? Surprises in data, model, governance
Lacked What was missing and would have helped? Knowledge, tools, time, mandate
Longed for What did we wish had been different? Structural wishes for the organisation

AI-specific additional questions:

  • How accurate was our initial risk assessment (Pre-Scan)?
  • Which data quality problems surprised us the most?
  • Was the Golden Set representative enough? What would we compose differently?
  • How effective was the Guardian role in practice?
  • Which Hard Boundaries turned out to be too narrow or too broad in retrospect?
  • Were the chosen Collaboration Modes correctly estimated?

Documentation and Dissemination

After the session:

  1. Write a summary (max. 2 A4) with the top 5 insights per category.
  2. Include the summary in the project archive.
  3. Report relevant insights to the AI CoE or knowledge management officer.
  4. Translate critical findings into adjustments to the Blueprint (via feature/<topic> branch).

Feedback Loop to the Blueprint

Lessons Learned are the most important source of improvement for this Blueprint. If a finding shows that a template, checklist or procedure is inadequate, we follow this process:

  1. Register it as an improvement proposal (GitHub Issue or internal equivalent).
  2. Discuss it with the authors of the Blueprint.
  3. Process it in the next version with a mention in the Release Notes.

4. Team & Roles

Role Responsibility R/A/C/I
AI Product Manager Facilitates the session, writes the summary A
Tech Lead Delivers technical insights and modelling experience R
Guardian Reports on governance effectiveness R
Data Scientist Reports on data trajectory and model development R
End users (optional) Provide perspective on usability and adoption C

5. Exit Criteria

  • Lessons Learned session has taken place with all core team members.
  • Summary has been prepared and included in the project archive.
  • Relevant insights have been passed on to the knowledge management officer.
  • Improvement proposals for the Blueprint have been registered.

6. Deliverables

Deliverable Description Owner
Lessons Learned Summary Top 5 insights per 4L category (max. 2 A4) AI PM
Blueprint Improvement Proposals Registered change requests AI PM
Project Archive Fully archived dossier AI PM

Related modules:

Next step: Prepare the formal handover via Handover Procedures → See also: Retrospectives