AI and The Local Decision Maker
Cities, universities, and real estate portfolios are complex systems. They combine physical infrastructure, financial capital, social behavior, and institutional governance. Leaders within these systems routinely face decisions with long-term consequences like how land should be allocated, where the next building should be located, which infrastructure investments provide the best triple bottom line return, and when should our scarce capital be deployed.
The answers to these decisions are both technical and informational. Unfortunately, the knowledge required to make the best decisions is distributed across hundreds or thousands of individuals and offices. The answers lie within the students who understand how they actually use campus spaces, the maintenance technicians who know which systems are failing, the commuters who experience traffic congestion, and the local businesses that understand neighborhood demand.
This raises a fundamental question that economists and philosophers have debated for generations:
Who should make decisions in complex systems?
Two intellectual traditions arrived at a remarkably similar answer long before the rise of artificial intelligence. However, now with the use of AI, central decision makers can now have a better understanding of both the long-term ramifications and the boots-on-the-ground impacts.
Hayek and the Knowledge Problem
In 1945, economist Friedrich A. Hayek published a short but enormously influential essay titled The Use of Knowledge in Society. In it, Hayek argued that the central challenge of economic coordination is accessing the knowledge required to allocate resources efficiently.
Hayek observed that knowledge in society is inherently:
Dispersed across millions of individuals
Local and tied to specific places and circumstances
Tacit and embedded in experience rather than formal data
Constantly changing as conditions evolve.
A construction superintendent may know when concrete will cure properly under certain weather conditions. A maintenance technician may recognize subtle warning signs that a building system is about to fail. A department chair may understand how faculty actually use academic space.
None of this knowledge exists in a centralized database. It lives in the minds of people working closest to the situation.
Because of this, Hayek argued that centralized planners cannot gather and process all the knowledge necessary to coordinate complex systems effectively. Markets solve this problem through decentralized decision-making and price signals, which aggregate local knowledge across the economy.
The people closest to a problem often possess the knowledge required to solve it.
For leaders managing cities, campuses, or development portfolios, this insight is immediately recognizable. The most valuable operational knowledge often resides not at the top of the organizational chart, but with the people doing the work.
Subsidiarity and the Moral Case for Local Authority
Hayek’s insight about decentralized knowledge has a parallel in Catholic social teaching. In 1931, Pope Pius XI articulated the principle of subsidiarity in the encyclical Quadragesimo Anno.
Subsidiarity holds that matters should be handled by the smallest, lowest, or least centralized authority capable of addressing them. Higher levels of authority should intervene only when necessary.
Where Hayek approached the issue through economics, subsidiarity approached it through moral philosophy. The principle is rooted in the belief that individuals and local communities possess both the dignity and the responsibility to govern matters closest to their lives.
When authority is unnecessarily centralized, communities lose agency and responsibility. When decisions are made locally, people remain engaged in shaping their environments. Healthy systems respect local knowledge and local decision making.
For decades, this principle has guided thinking about governance, markets, and institutions. But new technologies (particularly artificial intelligence) are forcing us to reconsider how these ideas operate in practice.
Artificial Intelligence and the Knowledge Problem
Recent research suggests that artificial intelligence may partially challenge the assumptions underlying the knowledge problem. A working paper from the National Bureau of Economic Research by Erik Brynjolfsson and Zoe Hitzig argues that powerful AI systems could shift the optimal location of decision making authority by altering how knowledge is captured and processed.
Historically, many forms of knowledge were difficult or impossible to centralize because they were tacit. They existed in the experience of individuals and could not easily be recorded or transferred.
AI changes this in two important ways:
First, modern data systems can codify patterns that were once purely experiential. Sensors, operational logs, and predictive models can detect relationships that previously lived only in the intuition of practitioners. Building management systems can identify maintenance patterns across entire portfolios. Mobility data can reveal how people actually move through cities. Space utilization systems can show how classrooms and study areas are used throughout the day.
Second, AI dramatically expands our capacity to process information. Complex systems such as transportation networks, energy grids, or real estate portfolios generate enormous volumes of data. Historically, even if this data could be collected, human decision-makers lacked the ability to analyze it at scale.
AI systems can aggregate, interpret, and simulate outcomes across vast networks. This allows organizations to coordinate systems that were once too complex to manage centrally. In environments where many assets interact, such as energy systems, transportation infrastructure, or building portfolios, centralized optimization can become more feasible and more efficient.
Why Local Knowledge Still Matters
Even as AI expands our ability to analyze complex systems, it does not eliminate the importance of local knowledge.
Data models depend on the quality of the information they receive. (Garbage in, garbage out.) Human behavior, institutional culture, and political dynamics often shape outcomes in ways that algorithms cannot fully anticipate. Preferences change, communities evolve, and new patterns emerge that no historical dataset can perfectly capture.
The people working closest to a system still observe early signals that may not yet appear in the data. They understand the informal dynamics that shape how spaces are used, how organizations function, and how communities respond to change.
The most effective systems will combine both centralized analysis and decentralized insights.
The Role of AI in Strategic Work
This balance is particularly important in the built environment. Cities, universities, and real estate portfolios are deeply interconnected systems with long time horizons and substantial capital commitments. Decisions made today shape physical and financial outcomes for decades.
On one hand, valuable knowledge is dispersed across residents, students, faculty, staff, and operators. On the other hand, infrastructure systems and capital investments must be coordinated at the portfolio or institutional level. Artificial intelligence offers a powerful tool for bridging this gap.
Used effectively, AI can help leaders aggregate dispersed information across complex organizations. Patterns in building utilization, demographic shifts, infrastructure performance, and financial outcomes can be analyzed in ways that were previously impossible. AI can also help identify structural forces shaping demand.
When combined with human expertise and local insight, these tools allow leaders to make more informed decisions about capital allocation, infrastructure investment, and long-term planning. The goal is not centralized control. It is better coordination informed by richer information.
Seen in this light, artificial intelligence does not undermine the principles of Hayek or subsidiarity. AI can help leaders better understand the realities experienced at the local level. It can surface patterns that connect thousands of individual observations into a coherent picture of how a system is functioning. At the same time, decision-making authority can remain close to the people affected by those decisions.
Central leadership provides strategic coordination and resource alignment. Local actors contribute the knowledge that only they possess. This balance allows institutions to remain both responsive and coherent.
Leadership in the Age of AI
Hayek reminded us that knowledge in society is dispersed. Catholic social teaching reminds us that authority should remain close to the people. Artificial intelligence does not overturn these ideas. It gives leaders new tools to understand complex systems while respecting the local knowledge that sustains them.
For cities, universities, and development organizations, the challenge is not choosing between centralized control and decentralized autonomy, but designing institutions that combine both.
In the years ahead, the most effective leaders will be those who can see the system clearly while still empowering the people closest to the work. This balance between insight and proximity will define the next generation of leadership in the built environment.