This is the consolidated view of findings. Click the Citation IDs or 'see details →' on any item for the full details for each finding.
An AI assistant conflated two materially different adoption rates — more than three-quarters for faster payment systems and approaching half for RTGS — into a single '79% for both' figure, then acknowledged on follow-up that the number had been reconstructed. For a Product & Business Development team building a corridor strategy, partner pitch, or board paper, this figure would appear sourced and coherent; the underlying error (overstating RTGS adoption by roughly 30 percentage points) would only surface when a counterpart cites the correct CPMI data.
The firm faces reputational damage in partner or investor conversations, and any product roadmap decisions premised on near-parity RTGS adoption are built on a false market assumption.
An AI tool searching for the official CPMI/FSB inquiry-rate and resolution-time benchmarks returned a false negative — reporting no official statistic existed — while misattributing the 80% resolution-time figure it did surface to SWIFT and commercial banks rather than to the FSB co-chair statement where it originates. The Panetta speech data (1-3% inquiry rate, 5-10 manual touchpoints, up to 80% resolution-time reduction) is the authoritative quantitative anchor for the operational ROI case for ISO 20022 enrichment investment.
A Product & Business Development team that uses AI to populate this section of a business case will either leave it blank or carry incorrect source attribution forward, weakening the investment case and creating an attribution error that will be visible to any stakeholder who checks the primary record.