From Currency to Compounding: The Enterprise AI & IP Governance Series
What This Series Is About
Ninety-five percent of U.S. enterprises are deploying generative AI on their intellectual property — the most valuable assets they own. Fewer than one in four can connect that AI activity to measurable business outcomes. And as of January 2026, major insurers have begun excluding AI-related claims from standard commercial general liability policies.
This is the gap nobody is talking about clearly enough: enterprise AI governance has a demand-side problem.
The supply side — model certification, security testing, safety standards — is being built. What doesn’t exist yet is the infrastructure that governs what AI does inside the enterprise, with the intellectual property that defines how that enterprise competes.
This series is about that gap. How it formed. Why it’s widening. What it costs. And what building the right infrastructure actually looks like.
Each post stands alone. Together, they construct a single argument:
Intellectual Property (IP) is your currency. AI compounds it - or dilutes it. The difference is governance infrastructure.
The Series At a Glance
This is a 12-part series published biweekly, structured in three parts. It builds the argument from liability exposure to operational architecture to proven deployment.
Part 1: The Problem Nobody Can See
Post 1
IP as Currency: The Missing Half of AI Governance
Post 2
What AI Is Actually Doing with Your IP
Post 3
The $1.5 Billion Wake-Up Call (Coming Soon)
About the Author
Ken Herfurth is the Founder and CEO of Ander, a performance intelligence company. With 30 years in engineering and C-suite roles across financial services and technology, he has spent more than two decades building and operating enterprise IP systems. He writes about demand-side AI governance as an operator , with a firsthand view of the widening gap between what AI vendors promise and what enterprises can actually control.
Frequently Asked Questions
What is enterprise AI governance?
Enterprise AI governance is the set of policies, processes, and infrastructure that controls how AI systems interact with a company's intellectual property — including what content AI consumes, what outputs it produces, who is authorized to act on those outputs, and how the full chain of AI-driven decisions is tracked and audited.
Who is liable when AI makes a mistake?
Courts are increasingly assigning liability to the enterprises that deploy AI, not just the companies that build it. In Mobley v. Workday, a federal court held that an HR platform could be liable as an agent performing hiring functions — and that employers cannot escape liability by delegating decisions to a third-party AI system. California's employment regulations (effective October 2025) make this explicit: employers are responsible for discriminatory decisions made by automated systems, even when provided by vendors.
What is the difference between AI safety and AI governance?
AI safety focuses on whether an AI system behaves reliably and without harm — it is primarily a supply-side concern addressed through model testing, red-teaming, and certification (e.g., AIUC frameworks). AI governance focuses on whether the enterprise deploying AI is using it responsibly — it is a demand-side concern that includes IP provenance, human competency in the loop, derivative action tracking, and auditability. Both are necessary. Neither substitutes for the other.
What does "IP as currency" mean?
Intellectual property has always been an enterprise's most competitive asset. AI has changed the transaction structure: when AI interacts with enterprise IP, it consumes governed inputs and produces ungoverned derivatives — at machine speed, continuously, across every department. Without infrastructure to track these transactions, enterprises cannot distinguish value creation from value dilution. The "IP as currency" framing makes the structural gap visible: every financial currency needs exchanges, clearinghouses, and audit trails to transact safely. Enterprise IP, now being consumed by AI, needs the same.
Series published biweekly beginning February 27, 2026. New posts added as published.