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March 6, 20260 citationsOpen Access

Methodological Foundations of Ingentic AI: Building Specialist Engines from Doctoral Research

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CBCHANDRA SHEKHAR BHARTIYANational University of SingaporeACAnitha Chinnaswamy

Key Points

  • The aim is to establish methodological foundations for five AI engines that enhance financial performance understanding post-ERP.
  • Developed five specialist engines based on doctoral research insights.
  • Analyzed post-ERP financial performance data across 69 firms and 3,956 quarterly observations.
  • Employed three theoretical foundations to derive the engines' methodologies.
  • Introduced novel techniques like Sign Log Difference for cross-firm KPI normalization.
  • Developed an AI Prescription Ecosystem to guide enterprise AI adoption.
  • Created a library of over 50 consulting patterns to externalize expert knowledge.

Abstract

This companion paper presents the methodological foundations of the five specialist engines underpinning the Ingentic AI framework (Bhartiya (2) Sign Log Difference (SLD) — a novel normalisation enabling robust cross-firm KPI comparison; (3) Simulated Process Mining — outside-in process deviation inference from financial signals; (4) Consulting Pattern Intelligence (CPI) — a 50+ pattern library externalising practitioner knowledge; and (5) AI Prescription Ecosystem — a dual-axis taxonomy for enterprise AI adoption roadmaps. The paper traces how a three-hypothesis arc (H₀ → H₁ → H₂) and three theoretical foundations (Resource-Based View, IT Business Value Theory, Shang & Seddon's ERP Benefits Framework) led to each engine's methodology. The integrated pipeline is deployed in production as konsults.ai. Companion to: Bhartiya & Chinnaswamy (2026). Ingentic AI: Intelligence through structured composition of specialist engines. DOI: 10.5281/zenodo.18846384

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Cite This Study

BHARTIYA et al. (2026) studied this question.

synapsesocial.com/papers/69aa70a9531e4c4a9ff5a9aahttps://doi.org/10.5281/zenodo.18857191
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