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Companion paper outlines methodologies for five AI engines, emphasizing financial performance insights across firms.
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
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BHARTIYA et al. (2026) studied this question.
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