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From AI Pilot to Sustainable Value: A Prudential Framework for AI Commercialisation

Authors

Amit Godbole

Independent Resercher (India)

Article Information

DOI: 10.51584/IJRIAS.2026.11090112

Subject Category: Artificial Intelligence

Volume/Issue: 11/9 | Page No: 1625-1671

Publication Timeline

Submitted: 2026-09-24

Accepted: 2026-09-29

Published: 2026-10-19

Abstract

Surveys show organisations adopting artificial intelligence (AI) faster than they report earnings from it: the share using AI rose from 78 per cent in 2024 to nearly nine in ten in 2026, while the share attributing any earnings impact stayed near 37 per cent. Research on business models, scaling and governance does not connect its evidence to the decision to commit more to an initiative. This paper develops the AI Commercialisation Prudential Framework (AICAF) to make that connection. Its principle is that commercial exposure should rise only when the evidence on six adequacy dimensions meets a floor set by the gate and an exposure rating. The design borrows four mechanisms from Basel II and from supervisory model-risk guidance (SR 11-7, issued in 2011 and since superseded, and PRA SS1/23): minimum conditions set in advance, independent challenge, a recorded assumption set, and an evidence burden that rises with what is at stake. Capital has no counterpart, and without a mandate to enforce the gate the framework is advisory. A purposive structured review of five literatures yields the six dimensions; a sixth, on decision under uncertainty, supplies the rationale for a hold. The framework has four evidence levels with a non-compensatory rule, a three-tier exposure rating, five gates and four decision outcomes: advance, hold, remediate or stop. A calibration rubric and a lean version support its use. I applied its dimensions and outcomes retrospectively to eight initiatives that I led or advised, excluding two whose later outcome could not be established; every reading is my own, and exposure tiers and evidence levels were not recorded. A retrospective reading of Zillow Offers and GitHub Copilot illustrates the gates. The framework is compared with Stage-Gate, SPARK, Lean Startup, MLTRL and the NIST AI RMF. Six propositions and five validation designs remain as future work.

Keywords

AI commercialisation, evidence adequacy, exposure maturity, business model innovation, AI governance

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References

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