Income Analytics Webinar "Deploying AI in commercial real estate" Summary

Income Analytics webinar - Deploying AI in commercial real estate investment markets, June 2026

Income Analytics’ most recent webinar explored how artificial intelligence is beginning to reshape commercial real estate investment markets, with a clear message from the panel: AI is powerful, but it is not a shortcut. Its real value comes when it is applied to well-understood processes, supported by trusted data, and used to enhance—not replace—human judgement.

Key speaker insights

Mike LG, Head of Engineering at Oakland, framed AI as “just another data tool, but more so,” arguing that large language models matter because they can turn unstructured information into structured insight at speed. He warned, however, that AI projects only succeed when they reduce cognitive load and allow people to focus on judgement: “AI projects succeed when they’re relieving the cognitive load of humans. Let your employees do the actual job.”

Lars Wouters of Altares - Dun & Bradstreet emphasised that verified data remains the foundation of any credible AI deployment. He cautioned that “a brilliant model on bad data just gives you confident, well-formatted nonsense, just faster,” and highlighted the importance of live, connected, maintained datasets rather than static data points.

Tom Olsen, CFO of Indurent, brought the investor and operator perspective, stressing that AI adoption depends on strong foundations: leadership, process discipline, cloud-based technology, and a single source of truth for data. He said the opportunity is not simply cost reduction, but the ability to scale activity and improve decision-making: “Can you underwrite 10 billion of acquisition per annum using the same size team?”

Main themes

• AI is an enabler, not a replacement for expertise. The panel agreed that AI can speed up analysis, automate repetitive tasks and improve access to insight, but human judgement remains central to investment decisions.
• Data quality is the competitive battleground. Speakers repeatedly returned to the importance of verified, current and connected data. Without it, AI risks creating faster but less reliable outputs.
• Start small and scale carefully. Rather than attempting wholesale transformation, the recommended approach is to begin with clearly defined processes where the data, workflow and expected outcomes are well understood.
• Governance and security must come first. AI implementation needs ethical guardrails, auditability, cyber resilience and clear accountability before organisations scale up deployment.
• The SaaS market is likely to evolve rather than disappear. The panel expected a move away from one-size-fits-all software toward more tailored, integrated systems and agentic workflows.

Takeaways for commercial real estate

1. AI adoption is becoming a business model question, not just a technology question. Firms need to rethink workflows, operating models and decision-making processes.
2. Better proprietary data may matter more than better models. The panel broadly agreed that AI models are increasingly capable and interchangeable, while differentiated, well-maintained data is harder to replicate.
3. Operational efficiency is only part of the prize. AI could allow firms to review more opportunities, make faster decisions and communicate risk more clearly to investors.
4. Real estate has an opportunity to close the information gap with equity and debt markets. Better data discipline and AI-enabled processes could improve transparency and comparability across the sector.
5. The winners will be those that move beyond demos. Production-ready AI requires robust data pipelines, governance, integration and sustained organisational commitment.

A special thanks to our respected speakers for this webinar:

• Mike Le Galloudec, Head of Engineering, Oakland

• Lars Wouters Sales Executive, Altares - Dun & Bradstreet

• Tom Olsen, Chief Financial Officer, Indurent

Thanks to all that joined us, if you missed it, please click here for a copy of the recording.