Industrial AI Reality Check + A 3-Wave Framework for Deploying AI at Scale

In this episode of TechEDGE Podcast, we explore what it actually takes to operationalize AI in the real world—especially in industrial environments where reliability, safety, and trust matter as much as performance.

First, Sheila Kennedy breaks down what’s working (and what still needs work) across multiple AI categories—traditional AI, generative AI, edge AI, agentic AI, and emerging causal AI—with a simple guiding message: don’t overcomplicate it.

Then, we examine a practical rollout model from Devon Energy CEO Clay Gaspar: a clear three-wave framework for scaling AI from data accessibility to workflow integration to full AI-first reinvention.

AI in Industry: What Works and What Needs Work?
AI has been used in industrial settings for years—but the shift now is how many teams are expected to use, validate, and operationalize AI across roles and workflows.
This article breaks down the strengths, limitations, and maturity of key AI approaches, while emphasizing that success depends on aligning initiatives to business value and following the “KISS” principle: keep it simple.
The article reinforces that sustainable AI value requires disciplined pilots, trusted data, and strong coordination between AI teams and operational leaders.

How One Evangelist CEO Has Organized His Teams to Deploy AI in Three Waves
AI success is often more about structure than tools. In this article, Devon Energy CEO Clay Gaspar outlines a clear, simple model for deploying AI across the enterprise—one that scales from foundational wins to transformative reinvention.
With more than 80 AI-enabled projects underway, Devon’s approach, outlined in our episode, offers a scalable blueprint for leaders trying to move beyond pilots toward operational transformation.

Why It Matters
Together, these articles deliver a clear message for IT leaders: AI isn’t just a technology rollout—it’s an operational discipline.
Industrial AI requires trust, data quality, governance, and oversight. Enterprise AI requires structure, sequencing, and repeatable frameworks. Leaders who keep AI strategy simple, measurable, and aligned to real workflows will scale faster—and more safely—than those chasing hype.