As AI agents take on more complex work, the key constraint is no longer access to technology but an organization’s ability to make its decision-making processes explicit. Many critical judgments—about risk, exceptions, quality, escalation, customer treatment, and tradeoffs—have traditionally existed as tacit knowledge embedded in experienced employees. To scale AI effectively, firms must translate that knowledge into structured guidance, build governance that treats digital labor as part of the workforce, and equip managers to codify and continuously refine expertise. Organizations that succeed will create “judgment infrastructure” that makes institutional knowledge portable, consistent, and scalable, enabling faster innovation and better performance. Those that fail to encode how they think and decide risk falling behind competitors that can deploy expertise through both human and AI workers.
is the author of The Explorer’s Mindset: The Leadership Advantage in an AI-Driven Economy (forthcoming) and co-author of Open Talent: Leveraging the Global Workforce to Solve Your Biggest Challenges (Harvard Business Press). He is an executive fellow at Harvard Business School’s AI Institute, where his research focuses on the intersection of AI, organizational design, and the future of work. He advises senior leaders and organizations navigating the shift.