When I moderated a recent industry panel featuring three artificial intelligence (AI) practitioners, the conversation quickly moved beyond typical hype and into hard-earned lessons with direct relevance for commercial real estate. For commercial real estate professionals, whose business depends on interpreting vast amounts of property, market and financial data, the way AI is actually being adopted is critical.
Marc Mathieu, head of AI strategy at Venn and former head of enterprise operations at Salesforce, Max Child, CEO of AI gaming company Volley, and Joe Bertolami, CTO at Clifton AI with platform experience from Microsoft and Google, delivered a sobering reality check that every CRE professional should hear.
The Consumer Mirage
The discussion began with a fundamental misconception derailing AI adoption across industries. Most professionals assume successful AI implementation looks like ChatGPT — an open text box where you ask anything and get intelligent responses. This assumption couldn’t be more misleading.
Child’s experience building AI-powered voice games for millions revealed the core problem: “Most solutions start demos with an open text box, assuming users know what to prompt. We’ve learned this doesn’t work — users want AI incorporated into their existing workflow context, not isolated in a separate interface.”
The panelists said that they discovered something crucial through hard experience. Consumers experiment with open-ended AI tools for entertainment, but professionals need intelligence woven into how they already work. For commercial real estate (CRE), that means surfacing insights directly inside underwriting models, deal screens and property dashboards, tools that professionals already rely on every day. The ChatGPT model creates false expectations that AI success means learning new interfaces and prompting techniques.
The Integration Truth
What actually works looks nothing like the consumer experience. Successful AI implementation means the technology disappears. As Mathieu said, “It provides what you used to get in the way you used to get it, but far more insightful, with the expectation that it’s going to basically be the same way that you’re used to working, but better.”
Mathieu’s enterprise transformation work reinforced this insight. He’d watched companies fail because they treated AI like traditional software — something you implement and train people to use.
Instead, “AI is not software; AI is not SaaS,” he said. “It needs deep integration, it needs scaffolding, it needs different kinds of work.”
The pattern emerged clearly: successful AI enhances existing workflows rather than replaces them. Intelligence surfaces insights within familiar processes, making professionals more effective at the work that drives value, whether that’s evaluating deals, pricing assets or managing portfolios.
The Voice Horizon
The conversation revealed another complexity layer around interface evolution. Child’s voice-powered gaming success offered a glimpse of what’s ahead, alongside its current limits. While his company proved voice interfaces work for specific applications, the technology isn’t ready for complex professional workflows.
The panelists agreed that voice represents a major shift ahead — I predicted that “50% of interaction with AI is going to be by voice” within five years — but current implementations follow a “walkie-talkie” pattern of taking turns rather than natural conversation.
The breakthrough will come when AI can interrupt, clarify and collaborate dynamically, but that leap from walkie-talkie exchanges to true collaboration is still just out of reach, though not for long.
The Acceleration Curve
Most importantly, the panel revealed hidden acceleration happening behind the scenes. While public attention focuses on major model releases (e.g. ChatGPT 5), real improvements occur in specialized applications and workflow integration. Bertolami described how “agentic capability” (the length of time AI can act independently without human input) has grown exponentially. The time AI can work autonomously before requiring human intervention has expanded from seconds to hours, doubling every few months.
This creates a deceptive dynamic. Progress feels slower than expected short-term because individual breakthroughs don’t immediately transform workflows. But compound effects build rapidly, creating sudden capability jumps that catch industries off guard.
Mathieu captured this perfectly: “We always overestimate the change that’s gonna happen in a year, and we always underestimate the change that’s gonna happen in five or 10 years.”
The CRE Reckoning
For commercial real estate professionals, these insights converge into a critical moment. The industry faces the same transformation these panelists navigated, but with a crucial advantage: the lessons have already been learned elsewhere.
The consumer experience of asking ChatGPT to analyze a property will never scale to professional use. Instead, successful CRE AI will enhance existing deal evaluation, property analysis and market assessment workflows. Intelligence will surface comparable properties, demographic shifts and valuation insights within platforms professionals already use, not through separate AI tools requiring new skills.
The voice interface revolution is coming, but CRE professionals have time to prepare. The real opportunity lies in data integration and workflow enhancement — building scaffolding that will support more sophisticated AI capabilities as they mature.
Most critically, the acceleration curve means delays become increasingly costly. While implementation takes longer than initial expectations, compound improvements create sudden competitive advantages for early adopters. Companies that integrate AI into their property data, market analysis and deal workflows today will have years of optimization advantage over firms waiting for “mature” solutions.
The panelists learned this across gaming, enterprise software and platform development: AI transformation requires sustained investment in data infrastructure, workflow redesign and capability building. But organizations that commit gain exponential leverage over passive observers.
The reality check is clear. AI implementation is harder than it looks, more integrated than expected and accelerating faster than most industries realize. For CRE, this creates both challenge and opportunity. Firms that understand these lessons and begin transformation today will shape the industry’s future. Those waiting for simplicity will struggle to catch up in a market where intelligence has become the ultimate competitive advantage.
The experiment has been run. The results are proven. For CRE firms, the choice is clear: integrate AI into your workflows today, or risk being left behind in a market where intelligence is the ultimate competitive edge.
