Embedding AI Into Enterprise DNA – Operational Models that Work (Part 2 of 3)

Richard Keenlyside
Jun 26
2 min read

Three stages of AI: ANI (Machine Learning), AGI (Machine Intelligence), ASI (Machine Consciousness) are illustrated with icons.

Integrating AI into the Operating Model

From Project to Platform

Successful AI deployment no longer lives in isolated innovation labs. It’s becoming intrinsic to enterprise architecture. AI must now integrate with ERP systems, cloud environments, CRM platforms, and decision-making frameworks.

During my tenure advising various PE-backed and manufacturing firms, I led the consolidation of AI-enabled workflows within Microsoft Dynamics, Oracle SaaS, and Epicor ecosystems. By embedding AI capabilities into ERP suites, businesses gain real-time intelligence on supply chain variances, customer churn, and operational bottlenecks.

This shift—from discrete AI tools to AI-as-a-service within existing platforms—ensures resilience and scalability.

Governance: The Foundation of Scalable AI

AI governance isn't optional; it's fundamental.

Embedding AI into your enterprise requires controls for ethics, data quality, model drift, and performance auditing. This is especially true in regulated industries. I’ve helped design AI oversight frameworks that include:

AI-specific KPIs
Data lineage mapping
Automated performance validation
Escalation protocols for unintended outcomes

Robust governance structures not only reduce risk but also increase stakeholder trust, paving the way for AI-led decision-making.

Use Case Alignment is Mission Critical

Prioritise Business Value

AI success hinges on selecting the right use cases. These must tie directly to revenue protection, efficiency gains, or cost reduction. At a UK-based food manufacturer, I implemented an AI chatbot that resulted in a 40% improvement in HR and finance processing speeds, leading to a 29% reduction in late payments. These are the metrics that drive board-level engagement and ongoing investment.

Focus your AI roadmap on: