Authored by Kevin Rippon and Matt Ernst

Before getting into our short tirade, we’ll start with punchlines and proceed to unpack them. We’ll be upfront: We have a point of view, and we also have a stake in the game. But after Realcomm, our conviction around the market opportunity and what Outcome is building has only gotten stronger. Our perspective:

• Mid-market real estate firms have never had a better opportunity to compete against larger institutions, and yes – AI is the strategic linchpin, as long as it’s applied to be more than an expensive learning experience.
• The window is probably 6-12 months as larger firms are either navigating “LLM lock-in”, getting their data house in order, deciding what to do with their structured data, or wrapping themselves around the axle on how job descriptions should change. This says nothing of a fifth budding challenge, which is uncontrolled AI experimentation costs.
• Because the above is observably true and necessary at a certain scale, we posit that mid-market firms who avoid going too deep on “AI-readiness” and cut straight to getting “AI-active” will suddenly have pervasive advantages in their business – whether that be in deal sourcing, asset management, leasing, and the like.

Outcome’s technology strategy has always been focused on getting real estate firms AI-active quickly, thoroughly, and profitably. We work with large institutions as well, but make no mistake: We’re here to bring firms with little, so-called “AI-readiness” from 0 to 100, real quick.

Remarkably, the lack of data infrastructure and a re-engineered organization is a short-term advantage for mid-market firms.

What We Heard at Realcomm

As usual, the largest real estate institutions, consulting firms, and PropTech providers attended Realcomm 2026. We spoke to many of them; three macro themes emerged:

1) LLM Lock-In

If it’s not exactly regret, there’s anxiety that an AI horse was chosen too soon. Many large firms already have existing relationships with Microsoft and thus deployed Copilot; most others have gone to Claude, with other frontier LLMs sprinkled into specific use cases.

• There’s real concern about usability and accuracy for real estate use cases with Copilot. To be clear, we’re not here to slander Copilot. We have strong opinions on everything AI, but this is a ‘reading of the news’ comment.
• For those working with Claude, we say “good idea” (Outcome certainly makes use of Claude and 80+ other subprocessors) – but the rollouts of Claude are fairly contained to specific trial teams as the respective firms sort out user governance, data infrastructure, and meaningful use cases. Once again, this says nothing of costs, which are uncertain for most in these trial periods.
• All of the above lack a familiar, out-of-the-box user interface, which is a clear demand from business teams who still want to manipulate data and perform an action. This has forced some IT teams to use wrappers like Perplexity to offer one. For anyone decrying the future of SaaS, this is an interesting dynamic. We believe there will always be demand from professionals to assert some level of control over their work output; software and web applications will continue to persist accordingly. Excel remains undefeated, after all.

2) A Sprint Towards Strategy, A Jog Towards Execution

Some firms are stuck in the waiting place, leaning on large consulting firms to dictate an AI strategy. There are good reasons for this. Many of the real estate institutions are so matrixed and regionalized that it requires a focused party to learn the business(es); pattern-match the ways disparate teams operate; understand the enterprise tech & data stack; navigate intra-organizational political challenges; and make recommendations that are defensible based on the aforementioned.

That work can take months, if not well over a year, and the ensuing execution still requires that a) The consulting firms either have the technical wherewithal or the right partners to execute a strategy, and b) business teams learn new tools and adopt new ways of working. You could also throw in c), which is that many strategies come with a prerequisite of ‘clean, structured’ enterprise data.

3) Challenging to Adopt, Harder to Buy

If the Outcome team took a tequila shot for every time the word “outcome” (as in: “outcome-based pricing”) was said in panels and on the expo floor, we wouldn’t have made it back home. If there was a new industry buzz term to take away from Realcomm, it was outcome-based pricing. Many PropTech vendors, legacy and new, are dabbling with this as a concept for how they charge real estate firms to use their AI-powered [fill in the blank].

On the surface, this is a win-win proposition for tech firms and real estate firms. The customer only pays for the ‘value’ they’ve incurred by way of usage or a more esoteric measure. Outcome (the AI company) believes in this approach, but with an eye towards creating cost-certainty and control for the customer. This is where we see the plot around outcome-based pricing getting lost.

Even at a large scale, few real estate firms have corporate-level innovation budgets, and even fewer have open-ended budgets. If there’s anything we learned during our time in PropTech, price certainty – in other words, charging clients a fixed fee that they can budget for, even better if it’s billable to the asset – is, in a vacuum, more important than price point. We mention this because we believe the uncertainty of cost (as well-meaning as ‘outcome-based pricing’ is) is exacerbating the issues around enterprise-wide adoption of AI. Having spoken with many CTOs and IT leaders at Realcomm, the question of cost was asked about directly, and spoken about rhetorically, in equal measure. It’s our view that the industry has a long way to go to help large real estate firms buy AI solutions from a pure budgeting standpoint.

A Golden Opportunity for the Mid-Market

In the fullness of time, we believe every real estate institution’s advantage will boil down to the same advantages that have persisted over decades: Investment intuition, taste, relationships, and operating model. That implies every firm will eventually reach a level of AI adoption that makes AI a tool not unlike mobile phones or the internet.

Until then, there’s a huge opportunity for mid-market firms to gain an operating advantage, if for no other reason than how distinctly challenging it is for scaled firms to execute on an AI strategy. Incredibly, the AI adoption hurdles that presented themselves to mid-market firms even 6 months ago are no longer substantive issues:

• They don’t need pre-cleaned, pre-structured data. Platforms to create an ontology around data (wherever it resides, however it resides) exist. Outcome is one of them.
• They don’t need to rethink their organizations and find new ways to work or make use of time; many of them are already structurally understaffed, AI is an answer to scaling an understaffed organization.
• Cost is not an inhibitor. The right partner can make AI-related costs palatable and projectable.

All mid-market firms need to do now is embrace the art of the possible and determine the highest and best use cases of AI for their firm. That is not the same thing as re-architecting their firm or tech stack.

In fact, don’t go changing. Just get AI-active. The opportunity to get ahead has never been more real. The good news: We happen to know a company who help.