Industrial Site Selection in the Age of AI: What Technology Solves and What It Does Not

Artificial intelligence is already influencing how industrial site selection decisions are made. Advanced tools now enable even novice users to perform tasks such as analyzing logistics networks, benchmarking labor markets, comparing economic incentives, and screening geographies. As these capabilities become more accessible, many corporate decision-makers will naturally begin to ask a reasonable question: Is a site selection advisor still worth the expense?

It is a fair question, and the answer is nuanced. 

AI, in its current form, is unquestionably further commoditizing the upstream aspects of site selection. (In reality, this process has been underway for more than a decade as most reputable providers have access to the same datasets and analytical platforms.) The aforementioned upstream tasks can now be conducted by a novice at a lower cost by leveraging AI.

Where AI falls short is not in computation, but in judgment. It does not solve for incomplete datasets, time-sensitive infrastructure constraints, negotiation dynamics, political risk, or execution uncertainty—all factors that most often determine whether a site selection decision ultimately succeeds or fails.

When AI Is Used Without Experience, Risk Moves Upstream

AI’s ability to make upstream site selection tasks accessible is often viewed as an unqualified advantage. In practice, it introduces a new category of risk: Early decisions made with unwarranted confidence. When novice users rely on AI for preliminary screening or shortlisting, flawed assumptions can become embedded at the outset. Outputs that appear objective may obscure critical context, such as infrastructure constraints, emerging demand pressure, or local development dynamics that experienced practitioners recognize instinctively. Once early filters are applied, viable geographies or sites may be excluded prematurely, narrowing options before meaningful risks are identified. Experienced advisors use AI as an accelerant, not a substitute for judgment, ensuring early analysis informs direction without constraining outcomes.

AI Optimizes What Is Known, Not What Is Real

AI systems are highly effective at analyzing structured, digitized information. They assume that the universe of candidate sites is known, comparable, and reasonably current. In practice, this assumption rarely holds.

Despite what some in the industry might tell you, industrial sites are not comprehensively cataloged or uniformly described. Many viable locations are not publicly marketed, require land assembly, involve multiple stakeholders, or only emerge through local negotiation. 

Others appear viable in databases but fail under closer technical or operational scrutiny. As a result, AI frequently optimizes within an incomplete, or distorted, option set.

Experienced site selection advisors understand that the greatest risk is not choosing the wrong site from a complete list; it is believing the list itself is complete.

Site Qualification Cannot Be Automated

Beyond identifying sites, effective site selection requires qualification. This includes validating technical characteristics, confirming infrastructure feasibility, understanding development constraints, and assessing execution risk. Much of this information is neither standardized nor consistently updated, and, in many cases, is not publicly available at all.

Utility studies may rely on idealized assumptions. Zoning interpretations can vary materially by jurisdiction. Environmental and permitting risks may be technically solvable but practically disruptive. AI can process what is documented. It cannot independently verify accuracy, intent, or feasibility.

Site selection advisors apply experience to distinguish between sites that are theoretically viable and those that are practically executable, often eliminating high-risk options early, before time, capital, and credibility are misallocated.

Utility Capacity Has a Short Shelf Life

One of the most significant gaps in AI-driven site selection is the treatment of utility infrastructure. Capacity is frequently modeled as a static attribute of a site. In reality, it is a scarce and time-sensitive resource.

Power, water, wastewater, and gas availability represent a snapshot in time. What exists today may be gone tomorrow as competing projects reserve capacity, utilities reprioritize investments, or capital plans shift. These changes often occur without public visibility and well before facilities are constructed.

AI struggles to account for unseen competition, informal reservations, or the likelihood that a utility will prioritize one project over another. Experienced advisors understand that capacity must be secured, not merely identified, and that speed, credibility, and relationships often determine whether a project advances or stalls.

Negotiation and Leverage Remain Human

Economic incentives and infrastructure commitments are not static offerings waiting to be discovered. They are negotiated outcomes shaped by competition, timing, political context, and credibility. AI can benchmark economic incentives, but it cannot create leverage between jurisdictions, read negotiating posture, or structure commitments that balance near-term upside with long-term risk. Nor can it assess the durability of commitments across election cycles, budget pressures, or leadership changes. These dynamics require judgment, experience, and trusted relationships—attributes that remain fundamentally human.

Execution Risk Is Where Value Is Created, or Lost

Ultimately, site selection is not an analytical exercise, it is an execution decision. Community sentiment, workforce reliability, infrastructure delivery, and regulatory follow-through often determine success long after the initial analysis is complete.

As AI accelerates upstream analysis, executives face a paradox: more options, more data, and greater decision-making complexity. The role of the site selection advisor shifts from information provider to risk manager to help leaders understand what could change, what could fail, and downside exposures.

The Stakes Have Not Changed, Only the Tools

To be sure, AI is transforming how site selection analysis is performed, and organizations should embrace its advantages. But the stakes of site selection remain unchanged. These are long-term, capital-intensive decisions with limited reversibility and significant operational consequences.

Site Selection Group, a leading location advisory, real estate, and economic incentives firm, integrates advanced analytics with deep execution experience to help organizations navigate this complexity. 

AI may commoditize upstream portions of the analysis, but it does not commoditize judgment or experience. In an AI-enabled world, the most valuable site selection advisors are not those who generate more data, but those who help executives navigate and execute well-reasoned decisions that endure.