AI at the forefront of proptech

From theory to application: Practical gains and strategic questions

• The industry has moved into the “application layer” of AI, with practical tools built on top of established models

• Point solutions are delivering clear time and effort savings, but enterprise-wide platforms remain limited

• AI’s operational benefits are evident, but its broader strategic and financial impact is still unproven

Earlier this month, 3,000 commercial real estate (CRE) and technology professionals traveled to Las Vegas for Blueprint 2025. The event, a key gathering for founders, investors, and institutional users of property technology (“proptech”), offered a clear and resounding message: artificial intelligence (AI) is no longer a future concept but a present-day force actively reshaping the broader CRE industry. While the sentiment was overwhelmingly bullish on AI’s potential, the discussions revealed an industry in a critical phase of rigorous testing in search of practical applications.

Throughout the conference, the consensus was clear: AI is a fundamental technological shift, not a passing trend. We are currently in what can be described as the "application layer" phase of development. The foundational models and infrastructure are largely in place (though will keep developing and changing further), and the focus has shifted to building practical tools that leverage these lower layers and solve specific industry problems.

The most visible manifestation of this is the proliferation of point solutions within proptech. These are applications designed to execute specific tasks more quickly, accurately, and efficiently than their manual predecessors and workflow. Examples discussed included tools for faster data processing, automated routine tasks, and improved information transfer across teams within CRE organizations. These solutions are delivering quantifiable gains in the form of time and effort savings, providing an immediate and tangible benefit to users. However, while the landscape is rich with these targeted tools, there was a noticeable absence of mature, cross-functional AI platforms capable of providing holistic, enterprise-wide solutions, or at least connecting and solving for multiple of these “points”.

This reality highlights one of the central tensions discussed across many of the panels: the gap between operational impact and strategic value. Panelists repeatedly confirmed that AI is adding significant value at the operational level. It is making processes faster and teams more efficient. Yet, the conversation often stopped short of connecting these operational gains to higher-level strategic outcomes, such as improved portfolio performance, enhanced investment decision making, or overall business profitability. This leads to a significant challenge in measuring return on investment. Beyond calculating hours saved or tasks automated, the industry is still grappling with how to consistently measure and prove the broader financial ROI of these new technologies and generally seems to be mostly targeted at internal expedition efforts by the existing tech-teams within CRE organizations (e.g., institutional owners and operators). While many acknowledged this is an ongoing effort, there were few concrete examples presented that translated the adoption of an AI tool into demonstrable, long-term business value other than time-savings within certain teams or specific processes. The next chapter of AI adoption will need to focus on building this bridge, moving from task-level efficiency to firm-level strategic advantage.

"Trust but verify" mentality: The new rules of engagement

• Firms are navigating a complex “build vs. buy vs. partner” decision, often adopting hybrid approaches that combine in-house point solutions with external platforms

• Successful AI deployment hinges on clean, well-structured internal data, with many firms investing heavily in improving their data infrastructure

• Vendor selection and technology due diligence are more rigorous than ever, with SOC audits and data security playing a decisive role in investment and client decisions

The decision matrix for how firms should integrate AI is becoming increasingly complex. For large institutional owners and investors, the "build vs. buy vs. partner" dilemma was a recurring topic of conversation at the conference. There is no one-size-fits-all answer, and many are opting for a hybrid approach. This often involves developing smaller, highly specific point solutions in-house to address unique internal needs while relying on external vendors for larger, more comprehensive platforms. This strategy allows firms to leverage vendor expertise and accountability, effectively outsourcing significant elements of risk, particularly around performance and security.

Regardless of the approach, a critical prerequisite for any successful AI implementation is data integrity. Panelists from large institutional firms were candid about the need to get their own "data house in order" before they could effectively deploy sophisticated AI tools on top of the data. Clean, organized, and well-defined data is the fuel for any AI engine, and many firms noted they are currently investing significant resources into improving their internal data infrastructure and processes.

This internal focus is mirrored by an external one: vendor vetting has become more rigorous than ever. Potential clients and capital sources are conducting deep due diligence on how proptech companies build and manage their technology stack and process client data. Scrutiny is being applied to every layer, including where/how data, code and hardware are created, processed and stored. In this environment, System and Organization Controls (SOC) audits have become a non-negotiable requirement by proptech investors and customers. A clean audit is table stakes, and any issues can be an immediate deal-killer, effectively halting a potential investment or client relationship. This underscores the importance of data security and risk management in the current landscape.

The search for sustainable models: Durability in a changing market

• Investors are rethinking what constitutes a competitive advantage, prioritizing sustainable differentiators like proprietary data, network effects, and client adoption

• Metrics such as customer retention and clear unit economics are increasingly central in evaluating proptech startups, alongside traditional market size and growth measures

• Strong client “stickiness” has become a critical factor in funding decisions and long-term investment viability

The investment community is also adapting to the new realities of an AI-driven proptech market. One of the most significant shifts is the re-evaluation of a company's competitive advantage, or "moat." With the barriers to building and replicating many technology solutions having been lowered significantly by AI, a good idea is no longer enough for a proptech startup to attract capital. Venture and private equity investors are now searching for businesses with more tangible and sustainable differentiators, such as proprietary data sets, strong network effects, or clear evidence of client adoption and "stickiness."

This seems to have led to a reordering of key investment metrics when proptech VC’s are assessing potential portfolio companies. While overall market opportunity (e.g., TAM, SAM, SOM) and revenue growth remains important (the rule of 40 still applies), proptech investors are now placing a much heavier emphasis on customer retention and a clear understanding of unit economics by founders / proptech company management teams. These metrics are seen as more reliable indicators of a product's market fit, a company's long-term viability, and its ability to scale profitably. Panelists noted that in today’s environment, strong customer retention is front and center in any funding conversation.

Teaming up: The evolving sales process

Finally, the way proptech is sold is also changing. Sales cycles are becoming more complex and, in many cases, longer. This is a direct result of the increased due diligence and the need to get buy-in from a wider range of stakeholders within a client's organization, including IT, legal, and compliance, in addition to the business users.

To navigate this complexity, proptech providers are being advised to deploy multi-disciplinary sales teams. A successful sales effort may now require a team approach, combining a traditional salesperson with a technical expert and somebody with clear knowledge of the client’s business or process. This team-selling approach allows the proptech company to address a client's questions from all angles - technical feasibility, business impact, and financial value – more efficiently and cohesively.

Perhaps most importantly, panelists representing potential proptech clients signaled a growing "AI buzzword fatigue" across the industry. CRE clients and proptech investors are tired of marketing pitches saturated with AI terminology but detached from real-world application. The demand now is for clear, direct communication that identifies a genuine pain point and demonstrates exactly how a product provides a tangible solution. In the end, the companies that will win are not those who talk about AI the most, but those who can translate AI-driven efficiencies into strategic, long-term business impact.

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