A seasoned tech lead has detailed how agencies can make the best use of AI to benefit their business, and it’s not chasing the latest AI models.
Blair Dods, head of technology and AI at Managed, has suggested that agencies should focus less on model selection and more on building the knowledge infrastructure that makes AI useful.
“It’s not so much about the model itself. It’s about the data you use and what you’re feeding into it, compounded over time from having that data,” Dods told REB director and Managed co-founder Alex Whitlock in The Property Management Excellence (PMX) Podcast.
Describing his own workflow as an example, Dods said he has created a system that captures conversations in every meeting through automated transcription tools. This feeds the output into a connected knowledge base and automatically creates action items and strategy documents across project management platforms.
“Without AI, half of that would be lost,” he says. “After a 40-minute conversation, the amount of value that’s created – it’s now not lost. That’s been the number one game changer for me,” Dods said.
The key principle is compounding context. The longer a system runs, the more it learns about the business, its people, and its priorities. This accumulated knowledge becomes the differentiator between a generic AI tool and one that truly understands an agency's operations.
“The number one thing is start now, if you haven’t,” Dods says.
“It’s all about your data, your context, your history. The more information the system has, the better it performs. It’s not just about crafting the perfect prompt and expecting magic.”
For agencies unsure where to begin, Dods recommends starting with existing data sources – meeting notes, client records, operational documents – and building a structured knowledge base before attempting anything more ambitious.
“If you’re lost and don’t know what to do, just get started. Look at your data sources, see what is valid, put it in there, and get that structure in place first,” he said.
Dods noted that his team now uses approximately 10 different models for different tasks. By matching the right model to each specific use case, they reduced monthly token costs by 95 per cent.
As such, he concluded with an important piece of advice: agencies should avoid implementing a one-size-fits-all approach to AI spending.
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