Briefing #15: Your AI Model Isn't Your Moat
Why the most valuable part of your AI is human
Note: This briefing was originally published on LinkedIn on October 31, 2025. It has been migrated to our new home on Substack to create a complete archive. Multi-format features like video and audio commentary are available for all new briefings published from April 2026 onwards.
In the race to deploy AI, the market is fixated on the wrong prize. Leaders are pushed by their boards to find and implement the “best” model, believing the algorithms and insights that emerge from these models are the keys to unlocking AI’s value.
Put simply: they aren’t.
Access to powerful foundation models is rapidly becoming table stakes. Your competitors can (and will) buy access to the same technology. The real, lasting competitive advantage — your competitive moat — isn’t the AI you buy, but the unique intelligence you build.
This is where many AI game plans fall short. We try to plug a generic AI into a rigid, existing process. That process, built for human predictability, inevitably breaks when it meets a complex, real-world exception.
The most AI-fluent organizations don’t build processes. They architect decision systems.
A process is rigid. It breaks upon encountering exceptions. A system is adaptive. It learns from exceptions.
This is the true purpose of a “Human-in-the-Loop” (HITL) workflow. Contrary to enterprise intuition, HITL isn’t a safety brake. Rather, it’s the engine of your decision system. It’s the mechanism that systematically captures the irreplaceable, nuanced judgment of your human experts, which is critical wherever high-stakes business decisions are made.
As the Harvard Business Review shared, AI isn’t yet ready to make decisions that necessarily involve many additional nuanced elements beyond data and algorithms and where the consequences of an error are significant. For any decision with material legal, financial, or brand risk, human oversight is non-negotiable.
But — and this is the key — this oversight cannot be passive.
The strategic flaw in most HITL designs is that they inadvertently promote “automation bias,” a well-documented phenomenon where human reviewers become overly trusting and simply “click yes” on AI-generated suggestions. This is the worst of both worlds: you pay the cost of human review without gaining the benefit of their expertise, and you fail to catch the very errors the system was designed to prevent.
A properly architected decision system reframes the human’s role. You aren’t hiring them to be a passive rubber stamp. You’re promoting them to be a “strategic reviewer.” Their job is not to approve the AI’s good decisions, but to hunt for its bad ones.
This is where the moat is built. In data science, this is called “Active Learning.”
When an AI flags a fraudulent transaction in a process, a human clicks “approve” or “deny.” The AI learns little, and the expertise is lost.
When an AI flags a transaction in a system, the human expert’s override (e.g., “Approve: This matches the seasonal shipping pattern for this specific client”) is captured as priceless, proprietary data.
That single data point — that “why” from your expert — becomes the unique “data deposit“ of your business. It’s an asset no competitor can replicate.
When you architect your AI workflows as a system, every human interaction becomes a data deposit. You’re creating a proprietary feedback loop that methodically distills your team’s collective expertise, training an AI that becomes deeply, contextually aligned with your business, not your competitor’s.
Stop worrying about which AI model to buy. Start architecting the decision system that will capture, scale, and monetize your human expertise. That’s the asset that scales. That’s your real competitive moat.