The ORIL team spent September 9–10 at PropTech Connect Europe 2026 in London, one of the largest gatherings of real estate leaders, investors, operators, and technology companies in the region. Nine stages, hundreds of speakers, two days of conversation — the kind of event where you learn less from any single session and more from the pattern that emerges across dozens of them.
Events like this matter for a software company that builds real estate products, and not for networking’s sake alone. Sitting across the table from CIOs, asset managers, and PropTech founders gives us a more current picture of what real estate businesses are actually trying to solve than any market report can. It’s one more chance to compare notes with the people who own the problem, not just read about it.
Here’s what stood out, and what it means for teams building or modernizing real estate technology.
What Stood Out at PropTech Connect Europe 2026
AI Is Being Judged on Outcomes, Not Adoption
A few years ago, the question at PropTech events was whether real estate would adopt AI. This year, the question had shifted: is it actually working? One theme we took away from the agenda was that AI is increasingly being treated as part of the technology infrastructure behind real estate operations — not simply as a standalone tool.
The broader market reflects a similar shift. Real estate companies are moving from asking whether AI has potential to asking where it can deliver measurable value in day-to-day operations. The tools aren’t the bottleneck. Getting them to work reliably across a real business is.
Legacy Tech Debt Is Now a Strategic Issue, Not Just an IT One
The Peninsula Stage focused heavily on eliminating legacy tech debt and building scalable portfolio architecture — framed explicitly as a bridge between long-term strategy and day-to-day execution. That framing was notable. Tech debt isn’t being treated as a backend problem anymore; it’s being discussed in the same breath as portfolio risk, cost, and investor confidence.
This tracks with what’s visible across the sector more broadly. Much of the claims, leasing, and asset management infrastructure real estate runs on wasn’t built with modern API connectivity in mind, which means new AI and automation tools often get bolted on top rather than genuinely integrated — creating friction instead of removing it. The firms getting real value are the ones treating modernization as infrastructure work, not a feature release.
Data Foundations Are the Prerequisite, Not the Byproduct
Across the Arora Stage panel “From Data to Decisions: Powering the Modern Real Estate Firm” and multiple Innovation Stage sessions, a consistent thread ran through the agenda: real estate has plenty of data and not nearly enough of it in usable shape. Leases, work orders, valuations, and public records live in disconnected systems, recorded differently by different teams, often shaped by whatever legacy software happened to be in place when the process was set up.
Industry voices working on data standards have started calling this out directly — the argument being that AI is what’s finally forcing real estate to confront fragmentation it had been able to ignore for years. That matches a theme we heard repeatedly in London: firms aren’t short on ambition around AI and analytics. They’re short on the connected, consistent data those tools need to actually be useful.
Sustainability Is Being Underwritten, Not Just Reported
The Sustainability Stage reflected a distinctly commercial framing of ESG and retrofit — linking decarbonisation and smart retrofits not only to compliance, but also to operating costs, asset attractiveness, income, efficiency, and long-term valuations. Better energy performance is increasingly being treated as a business and investment consideration, not just an ESG requirement.
The catch, raised more than once on stage, is that a retrofit business case is only as strong as the building performance data behind it. Without metering, monitoring, and consistent reporting, it’s hard to prove a retrofit paid off — which makes sustainability, in practice, another data problem wearing a different label.
Scaling PropTech Past the Pilot Stage Is the Real Challenge
The Case Study Stage and several Peninsula sessions circled a question that’s become familiar across the industry: what does it actually take to move from a promising pilot to something running across an entire portfolio? The honest answer, echoed across sessions, involves less about finding the right vendor and more about the unglamorous work of aligning people, process, and existing systems around a new way of working.
Data Is Becoming the Foundation for Real Estate Technology
Roman Havrylyuk, ORIL’s CEO, took the Innovation Stage to explore how getting data right can unlock real business value — a topic that felt less like a purely technical discussion and more like an underlying theme across the event. Before adding another AI capability, teams need to understand whether the data behind it is connected, consistent, and fit for the decisions they want to improve.
The core idea is simple to state and hard to execute: AI and analytics are only as good as the data and infrastructure behind them. A predictive maintenance model built on inconsistent sensor data won’t predict much. A portfolio dashboard pulling from three systems that don’t agree on what “occupied” means will produce numbers nobody trusts. Fragmented, inconsistent data doesn’t just slow AI down — it caps how useful it can ever become.
Getting this right starts with basic questions that are easy to skip in the rush to “add AI”: Where does this data actually come from? How reliable is it? How does it move between systems, and who owns it once it gets there? Those questions aren’t exciting, but they’re the ones that determine whether an analytics or automation initiative delivers something real six months later, or just another dashboard nobody opens.
The objective was never “more data” or “more AI” for their own sake. It’s turning data that can be trusted into decisions that hold up — better pricing, better maintenance scheduling, better underwriting, better visibility into how a building or a portfolio is actually performing.
From PropTech Experiments to Measurable Business Value
The agenda’s clearest throughline was the shift from experimentation toward operational, measurable outcomes. That’s a meaningful change in how real estate technology gets evaluated. A tool doesn’t earn its place because it’s new or because a competitor is using it. It earns its place because it fits into an existing workflow, connects to the systems already in use, and moves a number someone can point to — occupancy, NOI, time-to-lease, energy cost per square foot.
This is a harder bar to clear than “does the demo look good,” and it’s the right one. Successful PropTech at this stage isn’t about chasing the newest capability. It’s about solving a real operational problem well enough that people actually use the tool, and building it in a way that doesn’t create a new island of data alongside all the others.
What This Means for Real Estate Technology Teams
A few practical takeaways for CTOs, product leaders, and innovation teams working on real estate platforms:
- Audit your data before you scope your AI initiative. Know where it lives, how consistent it is, and what would need to change before a model or dashboard built on it could be trusted.
- Treat integration as core scope, not a stretch goal. Tools layered on top of disconnected systems tend to create more friction than they remove.
- Tie every new initiative to a specific operational metric. If you can’t name what it should move — NOI, time-to-fill, maintenance cost — it’s worth questioning before you build it.
- Plan for scale from the start of a pilot. The jump from proof-of-concept to portfolio-wide deployment is where most PropTech initiatives stall; it’s easier to design for that jump early than to retrofit it later.
- Treat legacy modernization as a business decision, not just a technical one. Reducing tech debt has direct implications for how fast you can adopt what comes next.
Bringing Industry Insight into the Products We Build
When the industry talks about fragmented data, legacy systems, and AI adoption gaps, those aren’t abstract trends for us to file away. They’re the specific product and engineering challenges we run into building and evolving real estate platforms — across brokerage and listing tools, property data integrations, investment and portfolio analytics, property management systems, construction and smart building software, and energy and sustainability platforms.
That range matters here. A data integration problem in a brokerage platform and a data integration problem in a portfolio analytics tool look similar on the surface but require different decisions about architecture, data models, and what “good enough” data quality actually means for the use case. Understanding the business behind the software — how a leasing team actually works, what an asset manager needs from a dashboard, why a construction schedule slips — shapes those decisions as much as the engineering does.
That’s the perspective we try to bring into every real estate engagement: not just building what’s specified, but understanding why it needs to work a certain way for the business behind it.
Where This Leaves Us
The picture coming out of PropTech Connect London wasn’t one of dramatic disruption. It was steadier than that — real estate technology maturing toward systems that are more connected, better grounded in reliable data, and judged more consistently by the operational results they produce rather than the novelty of the tool itself.
We’ll keep following where this goes, learning from the people building and operating real estate portfolios, and carrying those insights into the products we build with clients.
Building or evolving a real estate technology product? Talk to the ORIL team about your next product, data, or integration challenge.
