Shiva Dhawan is the CEO and cofounder of Attentive.ai, the company behind Beam AI.gettyIn this moment, AI is not displacing preconstruction expertise; it may be the most viable path to preserving it.​​​The Wall Street Journal recently covered a wave of workers choosing to retire rather than adapt to AI. Just this week, companies like Meta began reassigning workers to AI roles. The implication is straightforward: In the current state of the industry, as AI arrives, standard labor demand often contracts.Construction is heading in the opposite direction. As the CEO and cofounder of Beam AI, an AI-powered estimating software, I’ve seen the same pattern play out repeatedly: Most experienced professionals aren’t leaving because of AI; they’re leaving anyway. Retirement in this industry is driven far more by demographics than by technology. And in this case, AI may be one of the only mechanisms capable of preserving what’s about to be lost—because what’s walking out the door right now is also decades of judgment, pattern recognition and decision-making experience.According to Spark, 41% of the construction workforce will retire by 2031, and 1 in 5 workers is already over 55. The industry is already operating under a $124 billion annual productivity gap driven by unfilled roles. But those figures still understate the real risk. This is a total knowledge system failure, one that shows up most clearly in preconstruction.​Estimating has always functioned as a kind of institutional memory, even if it’s never been labeled that way. A senior estimator doesn’t just price a job faster; they evaluate it differently. They carry a mental archive of what tends to go wrong: how scopes evolve, which partners introduce risk and which costs behave unpredictably across regions. Two bids might look identical on paper, but one may include a contingency informed by a similar project years earlier, while the other does not. Months later, one job absorbs the hit and the other doesn’t. That difference is often where the margin and feasibility of major projects lives or dies. And almost none of it is formally documented.​As that layer of thinking exits the workforce, the construction industry risks entering a compounding negative cycle. Experienced people leave, remaining teams stretch to cover the gap, preconstruction timelines compress and decisions get made with less context. Predictably, rework increases downstream, burnout accelerates and more people leave. At the exact moment when U.S. infrastructure demand is ramping up, the industry is at risk of having to rebuild its decision-making layer from scratch.This is where the role of AI is often misunderstood. In construction, I've found the most immediate value of AI is actually the preservation of knowledge. AI can observe how experienced estimators and preconstruction teams actually work by analyzing historical bids, revisions, project outcomes and cost data—not just what numbers were submitted, but how those numbers changed and under what conditions. Over time, it begins to surface patterns that were previously locked inside individual experience: which scopes tend to shift late, which subcontractor bids consistently come in low and correct upward and which project types require additional buffers in specific regions.A three-year estimator can now operate with visibility into patterns that previously required decades of repetition to internalize. At the same time, AI can reduce the cognitive load on the industry’s most constrained resource: its senior talent. Instead of aggregating inputs, reconciling spreadsheets or searching for precedent, experienced professionals can focus on high-value judgment: reviewing, adjusting and refining. Their decisions, in turn, can become part of a feedback loop that strengthens the system over time.The result is something construction has historically lacked: institutional memory that persists beyond any individual. Most firms today have robust data infrastructure but limited knowledge infrastructure. They can tell you what happened, but not always why it happened. AI is emerging as the bridge between those two states.I'm already seeing this play out. In the work we do at Beam AI, teams aren’t starting from a blank sheet anymore. They’re working off what’s been done before, how similar jobs were scoped, where things went wrong and where they added buffers. Over time, that becomes a baseline. It’s not perfect, but it’s a lot closer to how experienced estimators actually think than starting from scratch.But implementing AI effectively comes with its own challenges. One of the biggest challenges I see is firms trying to layer AI onto fragmented workflows. Historical bids and project learnings are often scattered, making it difficult to build consistency or learn systematically from past work. In many cases, teams also fail to feed accurate plans and drawings into these systems, limiting how effectively AI can identify patterns and improve over time. Another common issue is expecting AI to replicate senior-level judgment without first standardizing how estimates are scoped and reviewed. ​There’s also a broader adoption challenge. Construction has historically been hesitant to change established processes, especially when new tools require major workflow overhauls or come with steep learning curves. Firms that want to succeed must focus first on feedback loops: centralizing project data, creating more consistent estimating processes and keeping experienced teams closely involved in reviewing outputs.​The construction firms that recognize this shift early can fundamentally change how expertise scales within their organizations. They may be able to onboard faster, make more consistent decisions and reduce dependency on any single individual. In an industry where experience has always been a primary differentiator, that is a structural advantage.​And this dynamic extends beyond construction. Across developed economies, skilled workforces are aging out just as physical industries are being asked to do more: build infrastructure, reshore supply chains and modernize energy systems. In these environments, the constraint is not demand, but accumulated expertise. Some industries are using AI to reduce reliance on people, while others will use it to extend the impact of the people they still have. Construction sits firmly in the latter category.​Most sectors are asking whether AI will replace workers. Construction is confronting a more immediate question: How can we retain the thinking of a workforce that is already leaving. In this moment, AI is not displacing preconstruction expertise; it may be the most viable path to preserving it.​​​​Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?