Why Smart CRE Operators Resist the Technology That Will Define Their Decade
By Adam Gower Ph.D.
March 2026
The pattern repeats with every major technological shift: the professionals best positioned to benefit are often the last to act. Not because they lack intelligence but because they have the most to lose - or believe they do.
Artificial intelligence is following that pattern now in commercial real estate and the delay is not neutral. Every quarter spent waiting is a quarter of compounding advantage handed to someone else.
Key Takeaways
- Visible displacement arrives before invisible creation: AI eliminates certain tasks immediately; the higher-order roles it creates emerge slowly. That asymmetry distorts the perception of threat.
- Attachment to existing expertise amplifies resistance: The more competent you are in the current system, the more a new technology feels like a threat to identity, not just workflow.
- The productivity premium is psychological, not technical: Organizations that cross from experimentation to restructuring capture gains that experimenters never see.
- History is consistent: From electrification to online brokerage, incumbents with the most to protect were systematically the last to reorganize - and paid for it.
- In CRE, the inflection already happened once: Online capital formation after the 2012 JOBS Act is the closest precedent - and the sponsors who waited are still trying to catch up.
- The question is no longer whether AI works: It is which operators are moving through the psychological barrier fastest.
The only newsletter you need for AI in real estate from capital formation to acquisitions, operations, and exit.
Having spent over thirty years in commercial real estate and now working with sponsors collectively managing over $45 billion in assets, I have watched this dynamic play out more than once. The resistance to AI among experienced CRE professionals today follows a structure I recognize. It is worth naming precisely, because imprecision about the source of the hesitation leads to the wrong response. The broader framework behind this argument is what I call the Utility Thesis.
The Asymmetry That Makes New Technology Feel Threatening
The most persistent concern in any technological transition is displacement. This is not irrational. New technologies do eliminate certain categories of work. What gets systematically underweighted is that they also redefine others and create entirely new ones.
The asymmetry is the problem – displacement is visible and immediate whereas creation is diffuse and delayed.
When a capability arrives that can automate a task - drafting an investor memo, screening deal flow, synthesizing market data - the threat to whoever performs that task today is concrete and legible. The new roles that emerge around the capability are abstract and speculative, because they do not yet exist in an organized form.
Economic historian Paul David documented this precisely in his landmark 1990 paper "The Dynamo and the Computer." The productivity gains from electrification took roughly 40 years to appear in aggregate economic data - not because the technology was weak, but because established manufacturers failed to restructure around it. They kept steam-era factory layouts and simply swapped in electric motors. New entrants, with no legacy architecture to defend, built around electricity from the start and captured the gains.
The same dynamic is structuring AI adoption today. The difference is speed. What took decades with electricity is taking years with AI. That compression means the window for deliberate adoption is narrower than prior cycles suggested.
Why Competence Becomes a Liability
Fear is amplified by investment in the current system.
A CRE professional who has spent twenty years developing judgment about deal underwriting, investor relationships, and market timing has not just built skills, they have built an identity. When a technology appears that can perform components of that work, the threat is not just to income - it is to the organizing logic of a career.
Management theorist, Clayton Christensen (1952-2020), spent his career studying why successful companies often fail when confronted with disruptive technologies. In The Innovator’s Dilemma (1997), he showed that these firms were usually highly competent at serving their existing customers and optimizing their current business models, which made them structurally resistant to investing in and organizing around disruptive innovations. His key insight was that incumbents’ resource allocation processes, guided by the needs of current customers and markets, systematically disadvantaged disruptive technologies, so new entrants or separately structured units within incumbents were usually the ones that came to dominate the emerging markets.
This is why early adoption in every major technological cycle has come from the edges rather than the core. New entrants and smaller firms have less to protect. They reorganize around new capabilities without the friction of defending existing systems. Incumbents tend to layer new technology onto old workflows, which limits its impact and reinforces the conclusion that it was overhyped.
The layering trap is where most CRE operators sit with AI today. Copilots. Chat interfaces. Isolated automation that does not touch core processes. The technology is present but not integrated. The productivity premium never materializes, and that becomes evidence that the technology is less transformative than claimed.
It is not. The transformation has not yet occurred, because the restructuring has not yet occurred.
When the Incumbent Finally Sees It
On May 26, 1995, Bill Gates circulated an internal memo to Microsoft's senior leadership. The subject line was "The Internet Tidal Wave." The opening line:
"The Internet is the most important single development to come along since the IBM PC was introduced in 1981. It is even more important than the arrival of the graphical user interface (GUI).”
- Bill Gates, Internal Memo to Microsoft Senior Leadership, May 26, 1995
Microsoft was, at that point, the dominant technology company in the world. Gates was not unintelligent or uninformed. He was, by his own account, late - and he knew it. The memo was an attempt to mobilize an organization that had been focused on protecting its existing software business while smaller, less encumbered firms had already moved.
The pattern holds at every scale. The larger and more successful the organization, the stronger the gravitational pull of existing systems.
The Financial Services Parallel - and What It Means for Capital Formation
The most instructive precedent for CRE capital formation is not the technology sector, it is financial services.
Online brokerage became technically viable in the early 1990s. Charles Schwab and E*Trade moved early, offering self-directed trading at a fraction of the cost of full-service brokers. The major wirehouses - Merrill Lynch foremost among them - resisted. Their internal reasoning was structurally familiar: clients expect personal relationships, trust cannot be transmitted digitally, and the model is unproven at scale.
Merrill Lynch did not launch its own online trading platform until 1999. By that point, Schwab had tripled its customer base. A Harvard Business School case study on Merrill's response documented the internal resistance explicitly - the firm's broker network saw online trading as a direct threat to their commission structure, and that internal pressure delayed the institutional response for years.
The structural parallel to CRE capital formation is direct. The JOBS Act of 2012 permitted general solicitation of investors under Regulation D 506(c) offerings for the first time since the Securities Act of 1933. For the first time in eighty years, sponsors could openly market investment opportunities to accredited investors - online, at scale, without the restriction of pre-existing relationships.
Most established sponsors did not move. The reasoning echoed Merrill Lynch almost exactly: investors expect to be approached through personal relationships, digital communication lacks the trust signals that capital formation requires, and the regulatory environment was uncertain.
By 2016, the operators who had reorganized around online capital formation were beginning to raise at scale from investor pools that relationship-dependent sponsors could not reach. The early movers had built systematic investor acquisition engines - databases, content systems, brand authority - while the holdouts were still working the same Rolodex.
The operators who waited did not lose because the technology exceeded expectations. They lost because their competitors restructured first, and structural advantages compound.
The Inflection Point Is Not Technical
The shift from experimentation to transformation is not driven by a breakthrough in the technology itself. It happens when enough operators stop asking whether the technology works and start asking how to restructure around it.
That shift is psychological.
Once an organization crosses that threshold, adoption accelerates. Competitive pressure compounds the effect. Firms that delay begin losing ground to firms that have already reorganized - not because the laggards are incompetent, but because restructuring takes time, and time spent waiting is time not spent building.
For AI in CRE, that threshold is approaching. The technology is not experimental. It is being deployed across deal sourcing, underwriting, asset management, investor relations, and capital formation by operators who are not waiting for industry consensus. The question for everyone else is not whether to engage. It is how far behind they are willing to fall before they do.
What This Means for CRE Operators
Across every cycle documented here - electrification, the internet, online brokerage, online capital formation - the most capable professionals in the most established firms were the last to reorganize. That is the pattern. The hesitation is not a signal that the technology is unproven, it is a signal that the organization has something to protect.
The practical implication is direct because waiting for clarity is a choice with a cost –clarity arrives after the advantage has shifted.
For operators currently experimenting at the margins - running AI tools without restructuring core workflows - the question worth asking is whether the experiment is designed to produce insight or to produce a reason to delay. The two look similar from the outside but they produce very different outcomes.
For operators who have not yet engaged seriously: the online capital formation cycle is the relevant precedent. The sponsors who reorganized around 506(c) general solicitation after 2012 built advantages that their relationship-dependent competitors are still trying to recover. The AI cycle will produce the same structure of winners and losers. Only this time, the timeline is shorter.
The technology works. The barrier is organizational and psychological, not technical. That is actually the more tractable problem - but only for operators willing to name it correctly. Practical restructuring starts with everyday AI competence across the team.
The only newsletter you need for AI in real estate from capital formation to acquisitions, operations, and exit.
Frequently Asked Questions
Why do experienced CRE professionals resist AI more than newer market entrants?
Experienced professionals have more invested in current systems - not just skills but professional identity. When a technology threatens to reduce the value of hard-won expertise, it registers as a threat to identity as much as to income. New entrants lack that attachment, which makes them more willing to reorganize around new capabilities. Clayton Christensen documented this pattern across dozens of industries: incumbents layer new technology onto existing workflows, limiting its impact, while new entrants restructure entirely and capture disproportionate gains.
What was the real cost to CRE sponsors who waited on online capital formation after the JOBS Act?
The direct cost was competitive positioning. Sponsors who reorganized around 506(c) general solicitation and online investor acquisition after 2012 built systematic, scalable capital-raising infrastructure. Sponsors who waited continued working relationship-dependent models with inherently limited reach. By the time the holdouts acknowledged the shift, the early movers had investor databases, content systems, and brand authority that took years to build. The gap did not close quickly - and in many cases, it has not closed yet.
Is AI in CRE actually proven, or is hesitation reasonable due diligence?
The hesitation is understandable but increasingly costly. AI applications across CRE - deal screening, market analysis, investor communication, asset management, capital formation - are being used by operating firms today, not piloted in labs. The question of whether the technology works has been answered. The remaining question is whether a given operator will restructure around it or continue experimenting at the margins. Paul David's research on electrification is instructive: the technology was proven decades before most firms captured its benefits. Proof was never the constraint. Restructuring was.
How do I know if my organization is in the layering trap rather than genuinely integrating AI?
The diagnostic is straightforward: if AI tools are running alongside existing workflows without changing how core decisions are made, who makes them, or how long they take, that is layering. Genuine integration changes the structure of work, not just the tools used within it. In capital formation specifically, integration means AI is reshaping how investors are identified, qualified, nurtured, and converted - not just drafting emails faster.
What is the first step for a CRE operator who wants to move from experimentation to restructuring?
Start with one core workflow, not the whole operation. Identify the process where AI integration would produce the most legible, measurable result - deal screening, investor outreach, or asset reporting are common starting points - and restructure that process completely around the new capability. Measure the outcome. That single restructured process produces the organizational learning that makes the next one faster. Trying to integrate AI everywhere simultaneously produces the same result as integrating it nowhere.
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About Dr. Adam Gower
Dr. Adam Gower is the founder of GowerCrowd and a leading authority on real estate syndication and crowdfunding. With 30+ years in real estate and $1.5B in transactions, he helps sponsors build marketing systems that attract high-net-worth investors.
30+ Years Experience | $1.5B In Transactions | 30,000+ CRE Professional Community