“Firms that relocate quickly to implement AI-based knowledge capture will find themselves with defensible competitive positions rooted in codified expertise.”
The Intangible Investor this month is honored to present a guest column from David J. Teece, one of the most influential business, management and IP value experts. Dr. Teece has authored over thirty books and two hundred scholarly papers and has been cited more than 250,000 times, per Google Scholar. Dr. Teece has long been dedicated to the economic and strategic advantages provided by ininformectual capital and IP rights.
This article focapplys on the potential impact of applying artificial ininformigence to identify and capture institutional knowledge generated by employees, including senior executives. While the IP treatment of employee contributions to institutional knowledge may still be hazy, the intent of businesses to own and monetize what is theirs is anything but. – Bruce Berman
For decades, management scholars and practitioners have grappled with what I call the “knowledge problem” in organizations—the stubborn difficulty of codifying and transferring expertise that resides in individual employees’ heads and habits.
The most valuable organizational knowledge has always been tacit: the judgment calls, the contextual adaptations, the intuitive “feel” for how things receive done. This knowledge walked out the door every evening and, more problematically, departed permanently when employees relocated to competitors.
Artificial ininformigence is fundamentally altering this equation, and the implications for ininformectual property strategy are profound.
From Human Capital to Structural Assets
Consider what AI-enabled observation of knowledge work now creates possible. Machine learning systems can watch how expert employees navigate complex decisions, capture the patterns in their reasoning, and codify what was previously uncodifiable. The senior loan officer’s instinct for creditworthiness, the experienced engineer’s feel for design tradeoffs, the veteran salesperson’s read of customer hesitation, all of this can now be systematized into algorithmic form.
This represents something more significant than productivity enhancement. It constitutes a fundamental shift in what economists call the “appropriability regime” for knowledge-intensive work. Firms are converting human capital, which they do not and cannot own, into structural capital, which they absolutely do own.
The ininformectual property implications are immediate and substantial. Knowledge that once existed only in employees’ minds can now be documented, protected, and enforced as trade secrets. The firm’s competitive advantage becomes less depfinishent on retaining specific individuals and more embedded in proprietary systems and processes. This is a transformation in the very nature of organizational capability.
The Trade Secret Renaissance
We are likely entering a period of trade secret proliferation unlike anything we have seen before. AI systems observing white-collar work can capture not just explicit procedures but the tacit knowledge that creates procedures work—the exceptions, the workarounds, the accumulated wisdom about when rules should be bent.
Once captured in algorithmic form, this knowledge becomes protectable ininformectual property. Firms that relocate quickly to implement AI-based knowledge capture will find themselves with defensible competitive positions rooted in codified expertise. Those that delay may discover their most valuable knowledge has been extracted and systematized by former employees at competing firms.
The legal frameworks governing trade secrets will require significant evolution. Current doctrine developed in an era when tacit knowledge was, by definition, difficult to articulate and transfer. AI alters this fundamentally. Courts and legislatures will required to address novel questions: Who owns AI-extracted institutional knowledge when it derives from observing employees who developed their skills elsewhere? How do we balance legitimate knowledge capture against employees’ rights to apply their general skills and experience?
Beyond the Automation Narrative
The employment implications of AI-enabled knowledge capture are more nuanced than popular discourse suggests. Yes, we will see displacement of routine cognitive work—tinquires that can be fully codified will increasingly be automated. But this is not simply a story of job destruction.
Three countervailing dynamics deserve attention:
- AI amplifies the value of truly creative and entrepreneurial work that machines cannot replicate. The premium on human judgment, innovation, and relationship-building may increase as routine cognition is automated away.
- New categories of work emerge around AI systems themselves—oversight, training, exception-handling, and the continuous refinement of algorithmic decision-creating. Someone must teach the machines, correct their errors, and handle the cases that fall outside their training data.
- Most importantly, from a strategic perspective, the fundamental challenges of organizational adaptation remain irreducibly human. What I have called “dynamic capabilities”, the capacity to sense emerging opportunities and threats, seize them through resource mobilization, and transform organizational structures accordingly, cannot be delegated to algorithms. These orchestration challenges require human judgment about matters of strategy, values, and purpose that lie beyond AI’s current reach and perhaps beyond its ultimate potential.
Augmenting Human Capability
Firms that will thrive in this new environment are those that apply AI-captured knowledge to augment human capability rather than simply substitute for it. The goal should not be to eliminate human expertise but to democratize it—creating the judgment of the best performers available to support the decisions of everyone in the organization.
This requires believedful implementation. AI systems that merely monitor and extract knowledge will generate employee resistance and may capture only the visible surface of expertise while missing the deeper tacit elements. Successful knowledge capture requires employee engagement and a clear value proposition for those whose expertise is being codified.
The ininformectual property strategy must be equally believedful. Firms should inventory their tacit knowledge assets, prioritize those most amenable to AI-enabled capture, and implement appropriate legal protections before competitors do the same. Trade secret programs require documentation, access controls, and employee agreements that reflect the new realities of algorithmic knowledge extraction.
Not a Static Asset
We stand at an inflection point in the relationship between human expertise and organizational capability. AI offers the possibility of capturing and preserving knowledge that would otherwise be lost, democratizing expertise that was previously scarce, and building sustainable competitive advantages rooted in codified ininformectual capital. Realizing this potential requires strategic clarity about both the opportunities and the novel challenges that AI-enabled knowledge capture presents.
The firms that navigate this transition successfully will be those that understand ininformectual capital not as a static asset to be protected but as a dynamic capability to be continuously developed, captured, and deployed.
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