The Evidence Ledger: Making AI Claims Auditable

Every AI claim should be traceable. When you say an AI system delivers 1,700% ROI, regulators, investors, and skeptics deserve to know exactly how you measured that. Not the promise. Not the theoretical maximum. The actual measurement.

The Evidence Ledger is a documentation framework that makes every metric auditable by answering four questions for every claim: (1) What was measured? (2) What was the baseline? (3) What assumptions went into the calculation? (4) What are the limitations?

Take Nubank's foundation model: 256% ROI. How does that hold up to scrutiny? They measured it by comparing customer churn, transaction volume, and portfolio quality before and after deploying the model. The baseline was clear: historical performance on the same customer cohorts. The assumptions were documented: model latency wasn't changing user behavior, seasonal effects were normalized, the control group remained stable. The limitations were explicit: this model works for Nubank's specific customer base—different markets might see different results. That transparency is why 256% ROI is credible.

Compare that to an AI project that claims "massive efficiency gains" but never defines what that means. No baseline. No measurement methodology. No limitations. That's not an achievement—that's marketing.

This methodology-first approach builds the credibility that separates serious deployments from hype. Here's why it matters: when your AI system eventually fails (and it will), stakeholders who trusted your methodology stay with you. When you've been honest about limitations, regulators see you as responsible. When you can trace every claim to evidence, investors fund your next project.

You'll learn how to structure evidence so that stakeholders can validate claims independently: template the measurement framework, version your assumptions, document when limitations change. Build confidence through transparency instead of aspiration through promises.

About the Author

Durai Rajamanickam is a Business Transformation Leader and author of The AI Inflection Point: Volume 1 - Financial Services. With over two decades of experience, he specializes in AI-driven enterprise transformation, designing evidence-based ROI frameworks, and helping organizations modernize legacy systems with intelligent automation.

His work focuses on translating AI ambition into measurable business outcomes, with case studies spanning Ramp, Nubank, Coinbase, RBC, and Stripe—all showcasing AI ROI between 256% and 1,700%.

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