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

This companion paper presents the methodological foundations of the five specialist engines underpinning the Ingentic AI framework (Bhartiya & Chinnaswamy, 2026). Each engine emerged from doctoral research at Aston Business School investigating post-ERP financial performance across 69 firms and 3,956 quarterly observations. The five engines are: (1) Quarterly Process Intelligence (QPI) — deterministic regime classification across 21 KPI time series; (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