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That Real Estate Tech Guy

That Real Estate Tech Guy

De : Jordan Samuel Fleming
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Welcome to the only weekly podcast dedicated to the Real Estate Investing Tech Stack, hosted by Jordan Samuel Fleming. Jordan has been heavily involved in building technology tools for Real Estate Investors for over a decade, and is the Co-Founder and CEO of smrtPhone, and all-in-one cloud phone system and power dialer. If you're serious about scaling up your Real Estate Investing business then this weekly podcast is for you! You'll learn from the best as each week Jordan speaks with individual investors who have leveraged technology to scale their businesses, as well as technology companies who build the tools you use on a daily basis. That Real Estate Tech Guy brings together expert insights, advice and the latest technology tips for any investor looking to build their Real Estate Investing business.

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  • The Best Employees Have Clear Boundaries, Your AI Agents Need Them Too

    The Best Employees Have Clear Boundaries, Your AI Agents Need Them Too
    Sep 24 2026
    This week I want to introduce the second part of the framework I've been building across these videos: bounded responsibility. Think about the best person you've ever managed, someone you trusted with real autonomy. That trust almost certainly came from clear boundaries, not the absence of them. They knew what they owned, what information they had access to, what decisions they could make, and exactly when to bring someone else in.I walk through a detailed example: a customer success rep named Mark, given the vague instruction to "keep customers happy, use your judgment." That sounds empowering, but it actually forces Mark to constantly guess, ask permission, and learn the real boundaries through trial and error over time. Compare that to clearly defining his responsibility up front, the outcome he owns, the information he can access, what he's authorized to decide, and where he must escalate, and you'll find his autonomy actually increases. This is exactly the same conversation we need to have when handing responsibility to an AI agent, and I break bounded responsibility down into four elements: role, context, skills, and escalation.Episode Timeline & Highlights[0:00] – Thinking about the best person you've ever managed, and why you trusted them[0:28] – What actually makes someone trustworthy: clear boundaries, not the absence of rules[1:02] – Why AI management isn't an entirely new set of principles from human management[1:36] – Introducing bounded responsibility: being clear on what a worker is allowed to do, not just what they should do[2:16] – Why the word "bounded" matters, and how clear boundaries actually create autonomy[2:47] – Introducing Mark, a customer success rep given a vague, seemingly empowering instruction[3:11] – The problem: Mark has to constantly guess and ask permission on nearly every decision[4:32] – How Mark eventually learns the boundaries anyway, just slowly and informally[4:59] – Why that's actually a failure of design, not a success story about Mark[5:32] – Rewriting Mark's responsibility with real clarity: outcome, access, authority, and stopping points[6:34] – Why this actually increases Mark's autonomy instead of restricting it[7:08] – Introducing an AI customer success agent, Alex, capable of far more than he should be authorized to do[7:37] – Why "what can Alex do" is the wrong question, and "what should Alex be authorized to do" is the right one[9:09] – Confirming this is a labor architecture principle, not something unique to AI[9:50] – Applying bounded responsibility to a previously pulled-apart job: lead qualification[11:36] – A real edge case: what happens when a normally disqualified lead turns out to be worth half a million dollars[11:36] – Introducing the four elements of bounded responsibility: role, context, skills, and escalation[12:39] – Why access to everything isn't the goal, only the information needed for that specific responsibility[13:07] – Escalation as possibly the most important element: knowing exactly where a worker must stop[14:23] – How organizations quietly rely on tenure and unwritten rules to paper over undefined responsibility[15:40] – Why AI isn't creating the need for boundaries, it's exposing responsibility that was always poorly defined[17:25] – The exercise: does your best person perform well because of design, or because of tenure[19:03] – The four questions to ask about any responsibility: outcome, access, authority, and stopping point[20:01] – Why the future of work is designing the work itself, not separate systems for people and AI5 Key TakeawaysClear Boundaries Create Autonomy, Not Restriction — A worker who knows exactly what they own, what they can decide, and where to stop can act confidently without constant check-ins. Vague empowerment actually produces more hesitation and more escalation, not less."What Can They Do" Is the Wrong Question — Technical capability, whether in a person or an AI agent, isn't the same as authorized responsibility. The real question is what they should be allowed to do, not what they're capable of doing.Bounded Responsibility Has Four Elements — Role (the outcome owned), context (the information accessible), skills (the actions and decisions authorized), and escalation (where responsibility ends). Every one of these needs to be defined, not assumed.Escalation Points Prevent the Most Common Management Friction — Most frustration between managers and employees around "you should have handled that" or "you should have escalated that" comes from never actually defining where the boundary sits in the first place.Businesses Often Run on Tenure Instead of Design — If your best people perform well mainly because they've been there long enough to learn the unwritten rules, that knowledge walks out the door when they leave. Designing responsibility clearly protects against that risk, for people and AI agents alike.Links & ResourcessmrtPhone: https://...
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    22 min
  • Why Automating Your Existing Processes Could Be a Mistake

    Why Automating Your Existing Processes Could Be a Mistake
    Sep 17 2026
    This week I want to challenge one of the biggest promises being made about AI right now: automation. It sounds great, take an existing process and make it faster with AI, but I think there's a question that gets skipped way too often. Should this process exist in the first place? When you automate a process without asking that, you're quietly assuming every step, every handoff, and every approval is actually necessary.I walk through a detailed example: a sales discount approval process, where a salesperson has to escalate a discount request to a manager because the manager holds the judgment and authority to decide. You can automate every step of that request and response cycle and make it much faster, or you can ask why the approval exists at all, and realize that once an AI agent understands your commercial boundaries, most of that back-and-forth can disappear entirely. This is the same mistake many businesses made during digital transformation: they digitized their paper processes instead of asking whether those processes should still exist. Pick one process in your own business this week, and instead of asking what you can automate, ask why each step exists in the first place.Episode Timeline & Highlights[0:00] – The big promise of AI automation, and the question that gets skipped[0:31] – Why automating an existing process assumes the process itself is correct[1:44] – Introducing the example: a sales discount that requires manager approval[2:07] – Walking through the current process: request, context gathering, decision, and response[3:06] – What an automation vendor would immediately offer to speed up that process[4:06] – The better question: why does the approval process exist in the first place[4:34] – The real reason: judgment and commercial boundaries live inside the manager's head[5:07] – Introducing AI labor trained on commercial policy, pricing, and customer history[5:40] – Setting clear boundaries: what an AI agent can decide, and what still escalates[6:14] – What happens to the approval process once those boundaries exist[7:29] – Comparing automating a process versus redesigning the work itself[8:01] – The digital transformation parallel: paper forms became digital forms, not new processes[9:33] – Why AI is a new source of intelligent labor, not just a faster way to move information[10:03] – The bigger question: how many of your processes exist just to move information between people[11:26] – Why your existing processes aren't sacred, just how work got done given past constraints[11:56] – Some processes should be kept, some automated, some redesigned, and some eliminated entirely[12:25] – Separating the actual work (a commercial decision) from the process steps built around it[13:01] – Why starting with the work avoids the traps hidden in technology, job titles, and process[13:57] – The uncomfortable discovery: some of what you're about to automate probably shouldn't exist[14:55] – Framing AI as a labor question rather than a technology question[16:00] – The exercise: pick one process, map its steps, and ask why each one exists[17:01] – Why the biggest AI opportunity might be realizing you don't need the process at all5 Key TakeawaysAutomating a Process Assumes It's Correct — Before making a process faster with AI, ask whether the process itself should exist. Every step you automate without questioning it carries forward whatever assumptions created it in the first place.Approvals Often Exist Because Judgment Lives in One Person's Head — A sales discount approval process exists because a manager holds context and authority a salesperson doesn't. Once that judgment can be captured in clear boundaries, much of the back-and-forth disappears.AI Can Own a Decision Within Defined Boundaries — Rather than routing every request to a person, an AI agent trained on your policies can approve straightforward cases automatically and escalate only what falls outside the boundaries you've set.Digitizing a Process Isn't the Same as Redesigning It — Many businesses made this mistake already during digital transformation, turning paper processes into digital ones without ever asking if the underlying steps still made sense.Your Processes Aren't Sacred — They're simply how your business learned to get work done given the people and systems available when they were created. Some deserve to be kept, some automated, some redesigned, and some should disappear entirely.Links & ResourcessmrtPhone (sponsor): https://www.smrtphone.ioThe AI Workforce: https://thefutureworkforce.aiThat Real Estate Tech Guy: https://thatrealestatetechguy.comThanks for tuning in to this one. If this got you second-guessing a process you were about to automate, that's exactly the point, pick one this week and ask why each step actually exists before you make it faster. Head over to thatrealestatetechguy.com for all the episodes and some great discounts on the tech we talk about. More...
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    18 min
  • Why Automating Tasks Isn't the Same as Redesigning a Role

    Why Automating Tasks Isn't the Same as Redesigning a Role
    Sep 10 2026
    This week I want to go deeper into an idea I touched on before: your job descriptions might actually be hiding how your business really works. I use a detailed example, a salesperson named Jennifer, and follow her through an entire day to show that "salesperson" isn't really one job. It's a bundle of completely different kinds of work, administrative tasks, predictable execution, pattern recognition, and high-value human judgment, that all got stitched together simply because one person had to do all of it.Once you stop asking "how much of Jennifer's job can AI do" and instead pull the bundle apart into its individual responsibilities, initial contact, qualification, scheduling, nurture, negotiation, closing, you can make a much smarter decision about how each piece should actually be executed. This is part of what I call work architecture, the first section of my AI Labor Architecture framework in my new book, The AI Workforce. Try this yourself: pick one important person on your team, forget their title for 30 minutes, and just follow the actual work that crosses their desk.Episode Timeline & Highlights[0:00] – Where would you even start explaining how your business works?[0:35] – Why a job description shows how work got bundled, not what the work actually is[1:04] – Introducing Jennifer, a top salesperson, and her simple-sounding job title[1:32] – Following Jennifer through a full day: a lead comes in and she makes contact[1:58] – The research and qualification work: comparing a lead against past successful customers[2:25] – The scheduling grind: calendar links, availability, and the back and forth to book a meeting[2:57] – Preparing for and running the meeting, then handling objections when they don't buy right away[3:20] – The nurture and follow-up work that keeps an opportunity from disappearing[3:32] – Pricing, approvals, and contract negotiation on the way to finally closing the deal[4:04] – Breaking down the four different types of work bundled into "salesperson"[5:12] – Why intelligent work has always required a person, and how that shaped the modern job[5:41] – Jennifer's job title as simply the container used to organize the work, not the work itself[6:16] – Why "how much of Jennifer's job can AI do" is the wrong question to start with[6:49] – Pulling the job apart into its individual responsibilities: qualification, research, scheduling, and more[7:30] – What it looks like once each responsibility can be assigned independently[8:04] – The real shift: not replacing Jennifer, but no longer treating Jennifer as the architecture[8:40] – Why most businesses will preserve the same work bundles even after adding AI[9:46] – Why the assumption behind those bundles has now changed[10:17] – The exercise: pick someone important and follow their actual work, not their job description[10:52] – Why the real job usually isn't captured in the written job description at all[11:25] – What makes your best people better: the judgment and context nobody ever documented[12:01] – The risk of only automating obvious tasks and calling it a redesign[12:43] – Realizing a "job" might actually be six or twelve separate responsibilities[13:50] – Separating the work, the responsibility, and the execution once again[14:23] – Revisiting Jennifer: an incredible closer spending half her week on work that doesn't need her[15:20] – Where this shows up everywhere: managers as human routing systems, founders making decisions they should have handed off years ago[15:41] – Introducing work architecture as part of the AI Labor Architecture framework[16:27] – The 30 minute exercise: forget the title, follow the actual work[17:37] – Where to find The AI Workforce and get notified when it's available5 Key TakeawaysA Job Title Is a Container, Not the Work Itself — "Salesperson" bundles together administrative tasks, predictable execution, pattern recognition, and high-value judgment into one title, simply because a person had to do all of it. The title hides how different those tasks actually are.Stop Asking "Can AI Replace This Person" — That question treats a job as one unit. The more useful question is what the individual responsibilities inside that job actually are, so each one can be assigned to whoever or whatever executes it best.Job Descriptions Capture Activity, Not Judgment — What makes your best people valuable is usually the context and judgment they've built up that was never formally written down: knowing what to ignore, when to break the process, which customer needs a call instead of an email.Talented People Often Carry Work That Doesn't Need Them — A skilled closer spending half her week on scheduling and data entry isn't a personal failing, it's an architecture problem. Once the bundle is pulled apart, that mismatch becomes visible.Try the 30 Minute Exercise on One Important Person — Pick someone on your team, ignore their title, and track everything...
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    19 min
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