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Most AI tasks should just be automation, agencies warned


By Staff Reporter

17 June 2026 • 1 minute read


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The property management industry is unnecessarily wasting money on AI for tasks that would be better handled by standard automation, a technology leader has flagged.

Blair Dods, head of technology and AI at Momentum Media Group, said many agencies are deploying AI where it is neither necessary nor cost-effective, simply because of the buzzword appeal.

“There are so many things with property management that shouldn't use AI. It should be automation,” Dods told REB director and Managed co-founder Alex Whitlock in The Property Management Excellence (PMX) Podcast.

He argued that tasks like rent collection, payment processing, and reporting should be handled by code written once and reused, rather than routed through an AI model that produces different results each time.

“I think a lot of people are using AI when they don’t need to. So that leads to a hell of a lot higher spend and budget blowouts,” Dods said.

“There’s just no need for it. It should be coded once and used forever, apart from the upgrades."

The distinction matters because the cost structure is fundamentally different. Automation code runs on predictable infrastructure and delivers consistent outputs. AI models consume tokens per query, introduce latency, and produce non-deterministic results – none of which adds value for workflows that require repeatable, reliable outcomes.

Dods said that the industry’s eagerness to label everything “AI” creates a false sense of innovation while driving up operational costs.

“If you’re processing rents or generating standard reports, you do not need a language model for that. You need good, clean automation built on a solid tech stack,” he said.

The warning came as property management agencies face increasing pressure to adopt technology. Dods advised principals to separate genuine AI use cases – data analysis, content generation, pattern recognition – from routine operational workflows that simply need efficient coding.

“The analysis of data? Sure, use AI for that. But the actual processing of that data? That should never be AI,” Dods said.

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