Denver Real Estate: Why AI Has Yet to Transform Broker Operations

Written by:

Rudi Davis

Date:

29 Mar 2026

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Three years after artificial intelligence became the latest must-have for real estate brokerages, the promised productivity gains have yet to materialize. The problem is not with AI’s technical potential, but with how the industry conflates occasional tool use with the deep integration needed to change how agents actually work. According to Dave Carter, Vice President of Marketing at Lofty, most agents treat AI as an add-on rather than a core part of their daily routine.

Three years after artificial intelligence became a must-have for real estate brokerages, promised productivity gains have yet to appear. The problem is not AI’s technical potential, but how the industry equates occasional tool use with the deep integration needed to change agents’ daily work.

According to Dave Carter, Vice President of Marketing at Lofty, most agents treat AI as an add-on rather than a core part of their routine.

“Adoption does not equal transformation,” Carter says. He distinguishes between agents who experiment briefly with AI — using a tool like ChatGPT to generate a quick email or listing description — and those who rely on AI throughout their workflow. True transformation occurs only when AI becomes a default part of an agent’s day, not an afterthought.

This distinction is critical for brokerages investing heavily in AI tools to improve margins and scale. Instead, most agents use AI inconsistently, if at all. The result is a widening gap between AI’s potential and the results brokerages see, creating frustration and skepticism just as more advanced systems enter the market.

Why Agents Resist AI

Carter notes a disconnect between how technology vendors design AI and how agents work. Agents generally agree that AI can save time, but the learning curve, behavior changes, and ongoing maintenance make adoption less appealing than sticking with familiar routines.

“Agents have always struggled with technology adoption simply because it feels like a chore,” Carter explains. “It’s that thing you know you should do, but you don’t want to — and you don’t have to.” He compares AI adoption to the challenge of getting sales professionals to consistently use customer relationship management (CRM) systems. Most know that tracking leads and managing pipelines improves results, yet few consistently update databases. Agents want to spend time selling, not managing data entry.

For real estate agents, who typically work as independent contractors, learning a new platform comes at a high opportunity cost. If deals come from relationships and referrals, the incentive to adopt new technology drops further. Even when brokerages require certain tools, enforcing usage is difficult when agents control their schedules and client interactions.

Carter acknowledges that the sweeping change promised by early AI advocates has not yet appeared. “We haven’t seen widespread adoption, and we certainly haven’t seen transformation,” he says.

Early Features Fell Short

A key reason for slow adoption is how AI was introduced to real estate. As industry buzz grew, vendors rushed to add features, often prioritizing novelty over real utility. This led to a flood of “gimmicky features” — tools that might write a social media post or suggest an email subject line, but did little to change how agents worked.

“In the early days, we saw a lot of these gimmicks pop up,” Carter says. These features demonstrated AI’s capabilities but did not save time or meaningfully improve productivity. Agents who tried these tools and found them unimpressive became wary of new AI offerings, making it harder for vendors to gain buy-in for more sophisticated solutions.

This skepticism now blocks adoption of advanced AI systems. Agents burned by early tools are reluctant to invest time in learning new platforms, especially if results are uncertain.

Lofty has tried to avoid this pitfall by developing AI features with clear, practical purposes and measurable results. However, even useful technology faces adoption hurdles if it forces agents to change established habits or add steps to their workflow.

The Push for Autonomous AI

Carter argues that the next phase of AI must reduce reliance on agents by changing their behavior. He envisions “agentic AI” — systems designed to operate autonomously, handling entire processes in the background with minimal human input.

“With the shift to agentic AI, we believe it’s going to be more hands-off,” Carter says. Agents can “set it and forget it,” letting AI handle routine tasks while they focus on client relationships, showings, and negotiations.

He describes a future where agents “can 100% focus on human interactions, client relations, on-site operations — everything they want to do and are good at — while AI manages the rest.” If autonomous AI works as intended, it could finally deliver operational improvements brokerages have sought, provided the systems run reliably without constant oversight.

The key test is whether AI can operate independently. If agents must still monitor or manage workflows, the adoption barrier remains. Only autonomous, results-driven systems can deliver the transformation promised since AI first entered the market.

Lofty’s Agentic AI Solution

Lofty’s newly launched agentic AI operating system represents its solution to the adoption problem. Billed as the first agentic AI platform for real estate professionals, it is designed to manage workflows autonomously, rather than requiring agents to use individual tools or follow step-by-step processes.

Carter presents the platform as a way for brokerages to gain AI benefits without relying on agent engagement. “The hope is that with agentic AI and systems like Lofty AOS, the autonomy gained will remove much of the learning curve and adoption challenge,” he says.

By reducing the need for agents to interact with AI tools regularly, Lofty aims to avoid behavioral barriers that have stalled transformation. Brokerages could see increased productivity and cost savings, even if many agents never fully interact with the AI operating in the background.

Execution and system reliability will determine success. But this approach marks a shift in thinking: instead of forcing agents to adapt, vendors are designing AI to work around existing habits — acknowledging that meaningful change may require less from agents, not more.

Future of AI in Real Estate

The last three years suggest that simply making AI tools available is insufficient to change real estate operations. True transformation depends on whether autonomous systems can deliver tangible results with minimal agent effort.

For now, the industry remains cautious. Brokerages want proof that AI can generate real gains without the adoption issues of the past. If agentic AI delivers as promised, the next wave of technology could move beyond hype to measurable impact. If not, the gap between AI’s potential and real-world value will widen, fueling skepticism and slowing future innovation.