Rethinking Marketing Strategy with Causal AI

The Guest

Joining us today from Sydney, Australia, is James Ward—Founder and CEO of Epistemology, a company on a mission to help people build better futures through smarter resource management and strategic reasoning powered by Artificial Intelligence.

James is a seasoned AI researcher, strategist, and technologist. His background includes roles at McKinsey, launching multiple tech startups, and academic work in generative AI. His approach blends rigorous epistemology, deep systems thinking, and a strong ethical stance on how technology, particularly AI, should be developed and deployed in professional settings.

“Most of us are walking around trying to achieve goals,” Ward says, “but we’re usually not so aware of the method we use to reason about what to do.” His “Five Rings” method aims to fix that—by helping individuals and organizations confront not just what they believe, but how those beliefs form and how they stand up to evidence.

How to Think Like a Machine (But Better): The Five Rings of Strategic Reasoning

At the core of Ward’s philosophy is a deceptively simple idea: most of us are reasoning with outdated or unexamined mental models. His Five Rings framework isn’t just a self-help tool—it’s a rigorous method for confronting flawed beliefs and rebuilding them based on evidence and logic. In a world where AI is reshaping how decisions get made, Ward’s framework gives human teams a way to reason with machine-like clarity—without losing their judgment.

Five Rings takes its name from the famous book of strategy authored by Miyamoto Musashi around 1650 titled “Go Rin No Sho” or (translated) “the Book of Five Rings”

In Ward’s application, each “ring” represents a layer of reasoning, and like the rings of a tree or layers of armor, they protect—or sometimes conceal—the core of how we make decisions. The process is sequential but recursive: you move through the five rings in order, but insights from later stages can force a return to earlier ones. It's less a checklist and more a discipline—a way of thinking that sharpens over time.

Let’s break it down.

Ring 1: Axiology — What do you really value?

‍Most teams say they value performance, but their behavior often tells a different story. Ward calls this difference “strategic axiology”—the gap between stated values and actual motivations. In marketing, this might look like optimizing for efficiency when the real (but unspoken) driver is creative control or agency politics. You can’t fix what you don’t confront.

Ring 2: Ontology — What do you believe about the world?

‍Every strategy rests on beliefs—about customers, markets, competitors, and even what “good” looks like. The problem? “We’re wrong about 99% of it,” Ward says. Yet most beliefs go unchallenged. Ontology is about surfacing and naming these assumptions so they can be stress-tested.

Ring 3: Semiology — Do your beliefs survive reality?

‍This is where theory hits the wall. Semiology demands rigorous evaluation: compare your assumptions to actual outcomes. If your campaign flops, are you learning or just rationalizing? Ward’s blunt assessment: “Every failed attempt to create a sale is wasted energy, wasted money, wasted time.” This ring is where ideas get falsified—or replaced.

Ring 4: Epistemology — Can you learn what really matters?

‍Epistemology, Ward argues, is “the destroyer of ego.” It’s the painful but necessary process of discarding broken models. This is where learning happens—but only if organizations are willing to let go of cherished but ineffective assumptions. It’s the intellectual reboot many teams avoid.

Ring 5: Strategy — What will you do now that you know better?

‍Only after clearing the previous rings can a strategy be built that’s not just logical, but aligned with reality. The result: smarter bets, fewer wasted cycles, and more consistent performance. “The strategy you write after confronting all your prior assumptions,” Ward says, “will outperform the one you wrote before.”

Why Organizations Struggle to Change

If the Five Rings provide a clear method for improved reasoning, why aren’t more businesses  using it? The barriers, Ward argues, are both psychological and structural. People resist discomfort. Organizations are often designed to preserve the status quo. Even when individuals recognize flaws in current approaches, they're frequently constrained by systems and hierarchies they don’t control. “You're trapped inside the epistemology of the company or industry you work in,” James explains.

Another issue is tunnel vision around the customer base. Most marketing teams direct efforts toward existing customers or high-intent leads, while ignoring the vast majority of their potential market. “You’re going to keep remarketing to your very tiny beachhead... while 99% of humanity you don't even know about remains untouched.”

Why Generative AI Falls Short

This ties into a deeper misunderstanding: the difference between correlation and causation. Much of modern marketing, especially where AI is concerned, leans on predictive models that identify patterns without explaining why they occur. “Generative AI is the junk food of AI,” Ward says. “It tells you exactly what you already believe. No discipline required.” The result is a cycle of reinforcing false confidence—what feels familiar is mistaken for what’s effective.

The Case for Causal AI

Ward argues that the path forward lies not in more predictive models, but in a shift toward causal AI—systems built to reason about how actions produce results. “If you reason well about cause and effect, every action you take to create a sale would create a sale.” Causal AI doesn’t just identify patterns; it identifies mechanisms. This approach, rooted in scientific methodology and falsification, demands more effort but yields insights that generative AI cannot match.

Inside SKAI: Scientific Knowledge Through Artificial Intelligence

To leverage causal AI insights, Ward and his team have developed a proprietary system called SKAI—Scientific Knowledge through Artificial Intelligence. Unlike traditional AI systems that rely on static models, SKAI operates in what Ward calls the “space of plausibility.” It continuously generates, tests, and refines hypotheses at scale, guided by cause-and-effect reasoning. It’s built not to parrot the past, but to navigate complex, evolving conditions with rigor.

As organizations rush to integrate AI into marketing operations, Ward offers a litmus test: “The central question should be: how does this AI system handle causality?” If the answer is that it doesn’t—because it’s just another language model—then it’s the wrong tool for the job.

Advice for Navigating Change

When asked for a lasting principle that can guide organizations through all this change, Ward keeps it deceptively simple: “You either know what to do—or you don’t. If you know, do it. If you don’t, learn.”

The Links

Jame’s website - https://epistemology.com.au/
James on LinkedIn

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