Artificial intelligence is no longer a distant concept confined to Silicon Valley laboratories or experimental technology firms. It has arrived squarely at the doorstep of the global property industry. According to Jess Moyer, Chief Operating Officer at Rode Publications & Media, the real estate sector is now moving from curiosity to adoption at a remarkable speed.

Speaking at the Reside Summit 2026, held at the Sandton Convention Centre, Moyer delivered less of a technology presentation than a strategic reality check for an industry grappling with fragmented data, disconnected workflows, and operational inefficiencies.

While the market is saturated with demonstrations of AI-generated images, chatbots and automated content, Moyer’s message was more nuanced: the real competitive advantage will not come from simply having access to AI tools; it will come from how organisations structure their information, workflows and institutional knowledge.

The industry has access, but not yet a strategy
One of the most striking observations in Moyer’s presentation was that the real estate sector has already largely reached the access threshold. “The majority of real estate professionals now have access to AI at work,” she explained.

The problem, however, is that most organisations remain stuck at the experimentation stage. “Most haven’t moved beyond basic chat functions,” she noted. “It’s gone from a question of whether to use AI to a question of how, and most companies still don’t have a good answer to that second part.”

In other words, the technology is already present in most businesses, but very few companies have developed a coherent operational strategy around it.

This distinction matters enormously. “Almost everyone has access,” she said, “but very few have a plan on how to make it work.”

Why the biggest technology players are watching real estate
Moyer cited recent global developments as evidence that AI’s role in property is no longer speculative.

She referenced the launch of a major enterprise AI services company, founded by Anthropic and backed by global heavyweights including Blackstone and Goldman Sachs, that is specifically focused on embedding AI into the operations of mid-sized businesses, with real estate identified as a targeted sector. “When some of the biggest technology and investment players in the world start putting serious capital behind reshaping your industry,” she said, “it’s worth paying attention.”

The implications for property companies are substantial. For years, property technology has largely evolved through fragmented software ecosystems — separate tools for leases, valuations, facilities management, tenant communication, ESG reporting and maintenance systems. AI, however, introduces something different: a reasoning layer capable of operating across all those systems simultaneously.
That, according to Moyer, changes everything.

The industry’s biggest misunderstanding about data
Perhaps the most important section of the presentation centred on what Moyer described as a widespread misunderstanding about “data readiness”. “The most common reason companies say they are moving slowly on AI is that they believe they don’t yet have the data,” she explained.

But in many cases, she argued, the problem is not a lack of data, it is the failure to recognise what data is. “People think data is just numbers in spreadsheets,” she said. “But for AI, data is every document, every workflow, every report, every lease, every board pack, every process and every piece of institutional knowledge.”
This distinction reframes the conversation.

Most property businesses already hold vast amounts of usable information, investment committee papers, valuation reports, lease agreements, ESG disclosures, planning documentation, maintenance histories and operational records.

The real question is whether that information is structured, connected and accessible. “How much critical knowledge in your business exists only in someone’s inbox or in someone’s head?” she asked delegates. “If a key person left tomorrow, what information would they take with them?”

For many organisations, the answer is uncomfortable.

Three categories of AI in property
Moyer outlined a framework developed by global AI and real estate strategist Antony Slumbers, which categorises AI use cases into three distinct categories.

The first category comprises large-scale proprietary data systems, automated valuation models (AVMs), predictive analytics, portfolio risk modelling and energy optimisation systems. These applications require extensive data infrastructure and are typically associated with institutional-scale operations.

However, the second category is where Moyer believes the biggest immediate opportunity lies.

This category focuses on contextual information rather than on structured databases. “That’s your leases, your valuation reports, your investment committee papers, your planning documents, your ESG checklists,” she explained.

These are the kinds of documents most property companies already possess in abundance, and in which AI can immediately begin delivering operational value.

The third category requires virtually no proprietary data. This includes AI acting as a reasoning and strategic support tool — stress-testing assumptions, preparing negotiation frameworks, analysing risks, or helping to interrogate business cases. “This is already being used widely,” she said, “but still massively underutilised.”

Document analysis: The biggest immediate opportunity
Among all the examples discussed, Moyer repeatedly returned to one area she believes has extraordinary short-term potential for the property sector: document analysis. “Real estate is an incredibly document-intensive industry,” she said. “AI can be genuinely transformative here.”

Modern AI systems can now review large volumes of leases, title deeds, valuation reports, planning documentation and legal agreements simultaneously, extracting critical clauses, identifying inconsistencies, flagging unusual conditions and cross-referencing information across entire portfolios.

“You could ask which of your 200 leases contain unusual break clauses and get the answer in minutes,” she explained. “Or which valuation reports repeatedly flag the same risks.”

The implications for operational efficiency are significant. International firms implementing these systems are reportedly achieving labour reductions of up to 60% in certain administrative review functions, while also improving consistency and accuracy. “We’re moving from days of manual analysis to hours,” she said.

Smart buildings and predictive maintenance
Moyer also highlighted the rapid evolution of AI-driven building operations and of predictive maintenance systems.

Rather than operating buildings according to fixed schedules or static design assumptions, AI systems are increasingly able to monitor how buildings function in real time — continuously adjusting systems to optimise energy use, maintenance cycles and operational efficiency.

“The building effectively learns its own behaviour,” she explained.
Predictive maintenance is becoming increasingly important. Rather than servicing systems at arbitrary intervals or waiting for failures, AI can increasingly identify maintenance needs before breakdowns occur.

A ScienceDirect review of peer-reviewed research shows that organisations implementing predictive maintenance strategies have reported operational cost reductions of up to 17%, maintenance cost reductions of 13%, and equipment lifespan improvements of up to 30%.

The rise of Agentic AI
One of the most forward-looking sections of the presentation focused on what Moyer described as the next major evolution in artificial intelligence: agentic AI.

Most current AI systems remain prompt-driven. Users ask questions or issue commands, and the system responds.

Agentic AI changes that model entirely.

Instead of simply responding to instructions, agentic systems can pursue goals autonomously, planning actions, interacting with multiple systems and adapting as circumstances change. “You don’t drive it anymore,” Moyer explained. “It drives itself.”

In practical property terms, this could eventually mean AI systems capable of handling entire operational processes independently, from tenant issue management to contractor scheduling, payment follow-ups and maintenance coordination.

A tenant reports a fault. The system diagnoses the likely issue, appoints the contractor, communicates timelines to the tenant and closes the workflow upon completion — all without human intervention. “That changes the economics of property operations,” she noted.

The integration problem
Historically, Proptech has suffered from fragmentation. Property businesses adopted multiple disconnected software tools, each addressing a narrow problem but rarely communicating with one another. “The industry ended up with isolated silos,” Moyer explained.
What is now emerging, however, is the ability of AI reasoning systems to span those fragmented ecosystems and connect them through common protocols.

This effectively establishes a universal operational intelligence layer.
Rather than a property manager manually transferring information between systems, AI can increasingly coordinate information and actions across multiple platforms. “That’s where things start to become genuinely transformational,” she said.

The risks are real
Despite the enthusiasm surrounding AI, Moyer repeatedly stressed the importance of governance and oversight.

She identified several major risks already facing organisations.
The first is hallucination, in which AI systems confidently generate incorrect information. “You cannot remove the human review layer,” she warned.

She also highlighted algorithmic bias, which is especially relevant in South Africa’s historical context.

“If AI is trained on historically biased data, it can perpetuate those biases in future decision-making,” she explained.
Cybersecurity, integration complexity, and reproducibility were also major concerns.

In regulated industries such as finance, valuation and property transactions, organisations may eventually need to demonstrate how AI-supported decisions were made. “If a regulator asks how a decision was made three months later, you need to be able to reproduce the process,” she said.

Technology is the easy part
Perhaps Moyer’s most sobering conclusion was that the technology itself is no longer the primary challenge. “The technology is ready,” she said. “Organisational readiness is the constraint.”

Successful AI adoption, she argued, depends less on software procurement and more on governance, workflow redesign, cultural adaptation and leadership. “AI cannot simply sit on a dashboard that nobody uses,” she explained. Instead, it must become embedded within operational workflows and institutional processes.

Companies that succeed will treat AI not as a technology project, but as a business transformation exercise.

A defining competitive divide
Moyer closed with a simple yet powerful observation. “AI will not replace professionals who use it,” she said. “But it will replace those who don’t.”

For the property sector, that statement increasingly feels less like futurism and more like operational reality.

Firms that organise their institutional knowledge, integrate their systems, govern their data properly, and embed AI into real-world workflows are likely to become significantly faster, leaner, and more competitive than those that continue to operate through fragmented processes and disconnected information silos.

As global capital, technology giants and institutional investors increasingly focus on AI-enabled real estate operations, the question facing the South African property sector is no longer whether AI will reshape the industry.

The question is how quickly organisations are prepared to adapt before the transformation accelerates beyond their reach.