This is not a new demonstration format. It follows the existing public-case verification process used across this series. The material comes from Arthur D. Little's official public case study. We first isolate the answer that the article reveals later, then reconstruct only what was actually knowable at the moment of decision into a teaching data package. Minerva Advisor completed one paid, live run, and this video is a verified replay of that actual Decision Room session. It does not imply endorsement, review, sponsorship, or certification by Arthur D. Little, and it does not represent real client results. This is an independent teaching simulation based on an official public source, not real correspondence or real client results.

The case centers on a global automotive supplier with revenue in the low double-digit billions and more than one hundred plants worldwide. The real question was not which trending technologies to list, but whether to set a single global technology sequence first, without cross-plant value evidence, or let business units run bounded validations first. Minerva kept the constraint against directly extrapolating local results at the center of the decision.

This live run produced five work receipts: one selected source locked in; four roles identified; six facts, two inferences, and three unresolved items separated out; two paths compared; and finally, three alternative explanations plus one reversal condition preserved. The system did not first pick a digital-transformation framework and then force the case to fit it.

What was known: more than one hundred plants worldwide with no common technology sequence, and data, integration dependencies, talent, and accountability boundaries that had not yet formed comparable evidence. What was unknown: how much value each technology would create in which processes, which processes needed global standardization, and the extent of existing capability gaps. Minerva did not extrapolate one plant's potential success into a global answer.

The system proactively noted that even small-scale validation could be distorted by differing data definitions, organizational bias, or the absence of a common measurement framework. The reversal condition is therefore not that the technology proves ineffective, but that if consistent cross-unit data definitions, dependency mapping, and accountability boundaries still cannot be established before launch, validation should be paused first, to avoid generating data that looks objective but is not actually comparable.

Executives can expand the original reconstructed data package. Arthur D. Little's five-step methodology, its target mapping, seed investment, concrete pilots, short payback periods, external capability network, governance adjustments, and the self-funding outcome all appeared only later in the published case, and none of that was included in Minerva's inputs. Minerva's direction was derived entirely from pre-decision evidence, not restated from the official answer.

Marcus defined the actual question Phase One needed to answer. Sofia compared second-order consequences across global operations, business units, and capability teams. Evelyn challenged whether the validation data would truly be comparable. All three advisors converged on small-scale validation, but only on the condition that common evidence standards be established first, with an explicit prohibition on scaling single-point results directly to the global level. Evelyn's objection, that small-scale validation carries inherent selection bias and governance gaps, and that inconsistent data, dependencies, talent gaps, and accountability boundaries could make results incomparable, was preserved for the executive's attention rather than buried in a footnote.

The executive added one condition: all participating units must first adopt a common set of value signals, data definitions, and stop conditions, and results that are not comparable must not be extrapolated to global plants. The system recorded a response receipt, marking the original judgment as upheld. This addition did not turn unknowns into knowns; it clearly specified the conditions under which the validation would be considered valid.

Setting a global sequence first can reduce duplicate investment, but it risks premature lock-in when evidence is insufficient, making reversal costly. Business-unit validation generates real evidence and preserves room for adjustment, at the cost of short-term duplicate investment and consistency risk. The executive chose the second path, while explicitly prohibiting direct extrapolation of any single unit's results to the whole company.

The global operations and digital capability teams will, before launching Phase One validation, define value signals, conduct a capability-gap assessment, establish globally common boundaries and stop conditions, and then select a small number of business units for validation. Minerva will track the next steps, but does not claim the technology's value has been proven, nor that the global transformation has succeeded.

This Arthur D. Little case passed ten out of ten decision-quality checks. Using the existing Decision Room first-pass path, with four model calls, Minerva delivered the first decision in 17.618 seconds and completed the full result, including the three-advisor cross-check, in 25.969 seconds, passing the 30-second first-decision threshold and the 45-second complete-result threshold. Decision quality, user experience, and performance status all passed, but this remains a single case test, not a claim of production-environment service levels or real client outcomes.