The same map in text. Each node states what the term means and what breaks without it. Each edge names one failure mode. Every figure carries the organisation, the period, the basis of measurement, and the case study behind it. Figures without a stated basis are named and withheld rather than rounded into something safer.
01 Data foundation
Data foundation is the layer that decides whether a number can be trusted at all: named ownership, tested contracts, and metered cost.
The layer where the failure usually started.
Owner
Dataset
Feature
Feature is a derived signal computed from datasets and reused across models, with its definition versioned.
Failure modeFeatures computed twice diverge, and the second definition is always the one in production.
60%down
data platform storage cost
Babbel GmbH·2023-05 to 2026·Basis: Reduction in cloud storage spend, achieved by sunsetting the monolith and the legacy systems around it rather than running them alongside the replacement, and by migrating onto a stack that bills storage separately from compute. Measured as absolute storage spend against the pre-migration run rate.·Seven data product managers, and a company that turned EBITDA positive
Withheld2 figures claimed against this capability (RETIRED — reattributed to babbel-maintenance-cost; annual cost saved by platform consolidation) stay off the page until the basis of measurement is written down. A number without a method is a number a buyer can take apart in thirty seconds.
EvidenceSeven data product managers, and a company that turned EBITDA positive·From one month to one week
02 AI context
AI context is the layer that gives a model the vocabulary, the sources, and the provenance of the business it is answering about.
The layer most programmes skip.
Metric
Knowledge
Knowledge is the retrievable corpus a model can reach at query time, with permissions that hold at retrieval rather than at the interface.
Failure modeYour model is only as good as the corpus it can reach, and nobody curates a corpus by accident.
No published figure yet. This capability is current work, and the evidence is the engagement and the software underneath it.
EvidenceAI governance for a Berlin deep-tech manufacturer·AI Control Plane
Lineage
03 Agentic systems
Agentic systems is the layer where software acts on its own: traceability, policy enforcement, and evaluation of every autonomous step.
The layer everyone photographs.
Policy
Model
Agent
04 AI enablement
AI enablement is the layer where people and money meet the technology: literacy, product management, and the standing operating model.
The layer that gets the blame.
Prompt
Decision
Outcome
Outcome is what the decision actually changed, measured, and fed back into the features that produced it.
Failure modeDecisions nobody measured are opinions with a log line.
20peopleup
people in the business intelligence centre of excellence
HelloFresh SE·2017-04 to 2018-12·Basis: Full-time employees in the business intelligence centre of excellence at its largest point during the role.·From one month to one week
4peopleup
people in the Australian business intelligence team, from 0 at role start
HelloFresh Australia·2015-11 to 2017-04·Basis: Full-time employees hired into the Australian business intelligence team. There was no such team before the role started.·From one month to one week
EvidenceFrom one month to one week·AI governance for a Berlin deep-tech manufacturer
14 edges, each one a failure mode
The nodes above are vocabulary. The sentences below are the argument. Each number matches a numbered edge in the diagram.
- 01Owner→MetricOwner is accountable for the metric definition.
- 02Dataset→KnowledgeDataset is indexed into the retrievable corpus.
- 03Feature→LineageFeature records what it was derived from.
- 04Dataset→MetricDataset resolves the metric.
- 05Metric→PolicyMetric is governed by policy.
- 06Knowledge→ModelKnowledge grounds the model at query time.
- 07Lineage→AgentLineage makes every agent hop traceable.
- 08Lineage→ModelLineage records what the model was trained on.
- 09Policy→PromptPolicy constrains the prompt before execution.
- 10Model→DecisionModel produces the decision.
- 11Agent→OutcomeAgent action is measured as an outcome.
- 12Model→OutcomeModel performance is measured as an outcome.
- 13Dataset→PromptDataset supplies the context a prompt is given.The thesis edge. It runs the full height of the stack because that is the distance between where the failure starts and where the blame lands.
- 14Outcome→OwnerOutcome feeds back to the owner who acts on it.The return edge. The stack is a loop, and the funding decisions taken at the top decide whether the bottom holds.