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Figure Figure 1 Revenue operations & AI

The AI forecast control map

Automation can produce a number; a governed forecast also declares the target, cutoff, version, override, owner, and later evaluation.

A schematic mirror chart compares an automation-only path with a bounded human-AI forecast path. Both declare target, data cutoff, model version, and baseline. The bounded path additionally records override boundary, reason, owner, and later actual. Values are coded field presence, not performance.← Declared fieldsProcess path →DefinitionInformationModelForecastGovernanceEvaluation1 coded presence1 coded presenceTarget and unit1 coded presence1 coded presenceData cutoff1 coded presence1 coded presenceModel version1 coded presence1 coded presenceBaseline value0 coded presence1 coded presenceOverride boundary0 coded presence1 coded presenceReason and owner0 coded presence1 coded presenceLater actual
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Reference & Evidence

Source: Author's schematic comparison grounded in Shrestha, Ben-Menahem and von Krogh (2019) and Raisch and Krakowski (2021). Values are coded control-field presence, not model performance, accuracy, or commercial results.

Each line is a claim from the register this journal publishes against, resolved from the register at build time.

  • A The five, named: "five key contingency factors specificity of the decision search space interpretability of the decisionmaking process and outcome size of the alternative set decision making speed and replicability" Shrestha, Ben-Menahem & von Krogh. (2019) · SBK19-C1
  • A The factors are a comparison frame, not a ranking: the article "identifies the idiosyncrasies of human and ai based decision making along five key contingency factors", so different combinations imply different splits of the work Shrestha, Ben-Menahem & von Krogh. (2019) · SBK19-C2
  • A The two concepts, defined: "whereas automation implies that machines take over a human task, augmentation means that humans collaborate closely with machines to perform a task" Raisch & Krakowski. (2021) · RAI21-C1
  • A "if organizations adopt a broader perspective comprising both automation and augmentation they could deal with the tension and achieve comp"lementarities. A conceptual paper in AMR, so this is argued, not measured Raisch & Krakowski. (2021) · RAI21-C3
  • B AI demand forecasting is a forecast process in which a model uses declared data and horizon to produce a demand estimate for a named unit Author framework grounded in SBK19-C1 and RAI21-C1 · V11-OWN-C1
  • B A demand-forecasting record should preserve baseline model, input snapshot, horizon, output, confidence or interval, override, reason, and later actual Author operating framework · V11-OWN-C2
  • B Automation, augmentation, human judgment, forecast output, and realized demand are separate objects Author taxonomy grounded in RAI21-C3 · V11-OWN-C3
  • B AI forecast quality must be compared against a declared baseline, loss function, segment, maturity window, and data cutoff Author evaluation framework · V11-OWN-C4

Grades: A, verified against the printed page of the primary source · B, primary source, text layer only · C, authoritative secondary · D, reported.

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