Briefing #33: Unlocking AI's Compound Interest
Realizing ROI from your AI initiatives comes from compounding capability.
Note: This briefing was originally published on LinkedIn on March 20, 2026. 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.
For years, we’ve been told that data is the new oil, especially in an AI climate where data fuels the very AI models that generate enormous capability.
It turns out this is only half true. Raw data is indeed a commodity, but the most precious aspect of it is derived as a by-product of data creation: it’s the proprietary knowledge your team generates when they turn that data into a successful business outcome that’s most valuable.
It’s the “why” behind the what – the hard-won wisdom that comes from navigating your organization’s unique challenges and learning from how such wisdom can be deployed to the organization’s benefit over time.
And most companies let it vanish into thin air.
I have worked with a number of aerospace companies over the course of my career, and all of them were deeply concerned with the loss of institutional knowledge. What would happen to their engineering prowess when their most senior engineers departed the firm? How would the organization best ensure younger engineers keep that knowledge alive in succeeding them?
All enduring organizations are subject to that challenge. When an employee leaves, along with it goes the institutional memory that this individual held. The same is true for every AI initiative, pilot or otherwise. What happens when the initiative is marked as “complete” by the team that led it? How would other teams and other parts of the organization know what was learned?
Without a system for capturing and sharing what was learned, the institutional memory gained from every AI project gets lost. Many organizations are capable of doing excellent work, but they may be poor at systematically learning from it.
This is often the default state for most businesses. These companies operate with a slow “organizational metabolism,” burning immense energy on individual projects but failing to convert that effort into lasting institutional strength.
In the AI-native era, a slow metabolism is a death sentence. But there is a remedy: building a lasting “learning organization,” where the goal of an AI strategy is not to complete a series of disconnected AI projects, but to develop an ingrained organizational capability that is a dedicated engine for change.
It’s an organization that recognizes its most valuable asset is the ever-growing AI playbook of what it has learned. It has a formal process for “locking in” the insights from every success and failure, ensuring that the entire organization gets smarter with each initiative.
This is what transforms a one-off AI project into a compounding institutional asset, and it’s the single greatest source of durable competitive advantage where every business is “doing AI.”