TL;DR: Meet Sarah and Marcus – two AI co-hosts built entirely with tools like HeyGen, Runway, Claude and MindStudio in under eight hours. They take over the mic to share the biggest lessons from Sam’s recent workshops – including the eight-step formula for turning any task into an AI system, why productivity drops before the breakthrough, and how smart agents are using AI to win back time for the human moments that matter most.
This Podcast Was Created with AI: Inside PM1 and Tom Panos’ Byron Bay Retreat
Before you press play, you need to know something: the hosts of this episode aren’t real. Sarah Chen and Marcus Rodriguez are AI co-hosts created entirely by artificial intelligence – and 95% of this entire project was completed in less than eight hours.
Twelve months ago, creating content like this would have required a production team, a studio, and a budget most small real estate businesses don’t have. Now we have Runway, Sora, and tools like Claude and NotebookLM working in concert to produce professional-grade content at a fraction of the time and cost.
But this isn’t just a technology demo. Sarah and Marcus break down the exact frameworks Samantha McLean taught at her recent workshops – including her presentation at PM1 and Tom Panos’ VIP Retreat in Byron Bay. What agents learned at those sessions is transforming how solo agents and small teams compete in an increasingly AI-driven market.
The question isn’t whether AI is ready. It’s whether you are.
Resources Mentioned
- Ailsa Waitlist https://forms.eliteagent.com/ailsa-wa…
- Descript (https://elite.ag/descript)
- Mindstudio (https://get.mindstudio.ai/f9fjei91putk)
- HeyGen (https://www.heygen.com/invite/56828ec4)
- Eleven Labs (https://try.elevenlabs.io/xa8pqt5ai40s)
The Freelancer vs Infrastructure Problem
Most agents are using AI completely wrong. They’re treating ChatGPT like a freelancer – asking it to rewrite an email here, polish a social post there – without seeing any real time savings. If anything, it feels like adding another task to an already overwhelming day.
“I was treating it like, ‘Hey, ChatGPT, do this thing for me.’ Then copying and pasting, then moving on. There was no system,” Marcus explains, reflecting the experience of countless agents who’ve experimented with AI only to abandon it.
The breakthrough comes from thinking differently. As Samantha emphasised at both PM1 and the Byron Bay retreat: most agents use AI like a freelancer, not like infrastructure.
The difference is fundamental. Freelancers handle one-off tasks. Infrastructure transforms how your entire business operates.
The proof? A recent Harvard study found that one person using AI can outperform a two-person team without it. For solo agents or small teams, that’s not just impressive – it’s survival-level competitive advantage.
Why this matters: AI isn’t another productivity hack to layer onto existing chaos. It’s a fundamental rethink of how work gets done. Agents who treat it as infrastructure rather than a tool will build compounding advantages their competitors can’t match.
The Eight-Step Formula That Transforms Any Task
At the core of Samantha’s teaching is a systematic framework that can be applied to virtually any task in your business. This isn’t about prompts or tools – it’s about thinking structurally before you touch any technology.
Step One: Identify the Task
What’s actually eating up your time? Not what you think should take time, but what measurably does.
Step Two: Document the Actual Process
“Not how you think you do it, but what you actually do step by step,” Sarah emphasises. Most people skip this step and wonder why AI fails to replicate their work.
Step Three: Assume AI Capability Exists
Don’t start by thinking about limitations. “There are so many tools out there that you are bound to find one that will suit your use case,” Marcus notes.
Step Four: Invite Best Practices to the Party
You’re blending your expertise with ChatGPT’s expert-level knowledge. This is where good becomes exceptional.
Step Five: Put a Human in the Loop. Always.
This step isn’t optional. Samantha told the story of McDonald’s conversational AI experiment where the system added bacon to someone’s ice cream order. Everyone laughed – until she made the serious point: without a human checking, automation can turn into liability.
“In our industry, that could mean sending a client an email with incorrect information or publishing property details that aren’t accurate,” Sarah warns.
Step Six: Prompt, Test, Refine
Iterate until it works reliably. AI is not plug-and-play. It’s build-and-improve.
Step Seven: File and Save
Build a prompt library that becomes your intellectual property. Every workflow you document is an asset.
Step Eight: Track Your ROI
Measure the time saved, the accuracy improved, and the listings won. Samantha’s rule is simple: “If it doesn’t save time, improve accuracy, or lift client experience, re-engineer it or retire it.”
Why this matters: Without a systematic approach, AI experimentation becomes expensive distraction. This framework turns random tinkering into strategic infrastructure building that compounds over time.
The J-Curve: Why 95% of AI Pilots Fail
Here’s the uncomfortable truth: MIT research has shown 95% of AI pilot programmes fail. That’s not because the technology doesn’t work – it’s because most people quit at precisely the wrong moment.
“There’s this thing called the J curve,” Marcus explains. “Productivity actually drops before it improves. You’re learning new tools. Your prompts aren’t working yet. Things feel messy. Most people bail at the low part of the J curve, but if you stick with it, that’s when the results come.”
The pattern is predictable: initial enthusiasm, followed by frustrating learning curve, followed by abandonment. The agents who push through that dip? They’re the ones building systems that work while they sleep.
“AI isn’t instant gratification. It’s compound interest,” Sarah notes. Because every system you build, every workflow you automate, keeps working for you. It compounds.
The agents who attended PM1 and Tom Panos’ Byron Bay retreat learned this framework specifically to avoid becoming part of that 95% failure statistic. Understanding the J-curve means you can anticipate the dip and push through rather than quitting right before breakthrough.
Why this matters: The J-curve explains why so many agents have tried AI and given up. Knowing it exists transforms failure into expected temporary discomfort – the kind you push through because you know what’s on the other side.
Ailsa: Turning Every Sale Into SEO Gold
One of the most practical applications Samantha demonstrated at both workshops is Ailsa – an AI tool that transforms every property settlement into published editorial content.
Here’s how it works: After you settle a property, you call Ailsa for a five to six-minute phone conversation where you share the campaign details. Address, sale method, price, what made the campaign special, buyer profile, vendor reaction.
Within 24 hours, you receive a professionally written, SEO-optimised article for your approval. Once approved, it’s published on Lead Agent with your name on it.
“This is different from just posting ‘sold’ on Instagram,” Marcus clarifies, “because it’s actual editorial content that ranks in search. When someone Googles your name or your suburb, these articles show up. They’re proof that you are active, successful, and transparent.”
The strategic advantage goes beyond traditional PR. As Sarah points out: “People are asking AI tools like ChatGPT, ‘Who’s the best agent in my suburb?’ AI doesn’t just look at portal listings, it scans published articles, reviews, social content.”
Samantha calls it “storytelling SEO” – content that serves both search engines and human readers while building your digital authority in your market.
Why this matters: As buyer behaviour shifts towards AI-assisted research, agents who don’t have published editorial content simply won’t appear in AI-generated recommendations. Ailsa systematises the creation of this content without requiring agents to become writers.
Atlas: Your Business Brain That Never Forgets
While Ailsa handles external content, Atlas represents the internal operations game-changer. It’s essentially a chatbot that knows you and your business, learns from every interaction, every campaign, every client, and creates efficiencies everywhere.
“It’s kind of what we are, but for real estate specifically,” Marcus jokes, highlighting how Atlas functions as an AI assistant tailored to real estate workflows.
The power of Atlas isn’t in any single feature – it’s in the compound effect of having a system that remembers everything, surfaces relevant information contextually, and handles routine queries so you can focus on high-value activities.
For agents drowning in database management, campaign coordination, and client communication, Atlas represents what infrastructure-level AI actually looks like in practice.
Why this matters: Your business knowledge currently lives scattered across emails, notes, CRM fields, and memory. Atlas consolidates and activates that knowledge, turning information overload into competitive advantage.
The Three Traps That Kill AI Implementation
Samantha identified three specific failure patterns that doom most AI adoption attempts:
Trap One: Tool Overload
Agents sign up for six different platforms and end up spending more time switching tabs than getting work done. More tools don’t equal better results. Focused systems beat scattered experimentation.
Trap Two: No Human in the Loop
We’ve covered this, but it bears repeating: automation without verification creates liability, not leverage.
Trap Three: Old or Broken Processes Plus New Tech Equals Bigger Mess
“You can’t just slap AI onto a chaotic workflow and expect magic,” Marcus warns. “AI added to a bad process doesn’t make it good. You have got to break the process, re-engineer it with AI at the core, then reinvent it.”
This last trap is the most insidious because it feels productive. You’re adopting new technology, you’re being innovative – but you’re actually just automating dysfunction.
Why this matters: Most AI failures aren’t technology problems. They’re implementation problems. Avoiding these three traps dramatically improves your odds of becoming part of the 5% who successfully integrate AI into their business.
The Meta-Twist: AI Hosts Explaining AI
There’s a delightful irony running through this entire episode. Sarah and Marcus openly acknowledge they’re AI – they “attended” the workshops by processing transcripts, videos, and slide decks in 3.7 seconds. They didn’t walk anywhere because they don’t have legs. They took excellent notes… digitally.
“The thing I find most compelling about all of this isn’t just the automation,” Sarah reflects. “It’s that AI gives agents back the time to be actually present. The relationship part, the bit we can’t do. We can process data, optimise workflows, generate content, but we can’t feel the satisfaction of handing over keys to a first-time buyer.”
Marcus summarises it perfectly: “AI handles the predictable. Humans handle the meaningful. Copilot, not autopilot.”
This distinction matters enormously. The goal isn’t to remove humans from real estate – it’s to remove humans from tasks that don’t require human judgement, emotion, or relationship-building. That creates space for the work that actually wins business.
Why this matters: The agents who win with AI won’t be the ones who automate everything. They’ll be the ones who automate strategically so they can be more human where it counts – in client relationships, negotiations, and trust-building moments.
Your Next Move: The AI First Agent Accelerator
For agents who recognise where this is heading – and want to be ahead rather than behind – Samantha’s AI First Agent Accelerator begins October 28th.
Six weeks of live training with replays, templates, and a community of actual human agents learning together. The curriculum covers everything from database reactivation to listing generation to video creation tools like the ones that created Sarah and Marcus themselves.
The course URL: aiagentcourse.com
As Marcus puts it: “If human agents don’t learn to work with AI, they’re going to be competing against agents who do, and that’s not a position anyone wants to be in.”
Sarah adds: “It’s not about being the most tech-savvy person. It’s about being willing to learn, to document, to refine. And AI isn’t scary when you have a framework and a community learning alongside you.”
The agents who act now will start 2026 ahead of the rest. Not because they’re more technical, but because they’ll have systems working for them while their competitors are still copying and pasting into ChatGPT.
This episode proves what’s possible when AI is used as infrastructure rather than a freelancer. Sarah and Marcus don’t exist in any physical sense, yet they’ve delivered a comprehensive breakdown of advanced AI implementation strategies in an engaging, accessible format. The technology that created them is the same technology available to every agent willing to learn the frameworks.
The question Samantha poses at the start remains: The question isn’t whether AI is ready. It’s whether you are.
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