This week I’m going solo to talk about the pattern underneath almost everything your business does: receive, execute, hand off. Handing work to the next person or AI agent isn’t enough, because they also need the context to continue it, and I show why with an AI customer success agent named Maya and a customer success leader named Sarah.

We get into why access to your CRM, Slack, and email is not the same as context, how Sarah’s call with an at risk customer creates new context that has to travel back to Maya, and why a handoff and true continuity are two different standards. I also connect it to infrastructure architecture, the third pillar of the AI labor architecture framework, and close with an exercise you can run on one handoff this week. If you’re an operator or investor designing a workforce of people and AI agents, this one is for you.

Episode Timeline & Highlights

[0:00] – Almost all work follows one cycle: receive, execute, hand off

[1:31] – Meet Maya, an AI customer success agent who spots a customer at serious risk of leaving

[2:35] – Maya hands the at risk customer to Sarah, a customer success leader, with no background

[3:35] – Sarah has to rebuild the story from the CRM, support system, billing, email, and Slack

[4:54] – Why context is not more information, a bigger context window, or access to everything

[6:20] – Access to the CRM is not the same as context, only the raw materials to build it

[7:32] – The better question: what does this worker need to understand to execute this responsibility?

[8:06] – Sarah calls the customer and creates new context: a business change and a promise by Friday

[9:31] – When the work returns to Maya, a CRM status change is not the same as context

[11:45] – This isn’t an AI problem, it’s a work problem: meaning gets left behind every time work moves

[12:13] – Why so much context lives between your systems or inside your people’s heads

[14:14] – Good infrastructure gives each worker the right context for their bounded responsibility

[16:37] – Infrastructure architecture as the third pillar of the AI labor architecture framework

[18:37] – The difference between a handoff and continuity

[19:39] – The exercise: pick one handoff and stand in the shoes of the worker receiving it

[21:30] – People inside your business have become part of the infrastructure, compensating for the friction

5 Key Takeaways

1. Handoff Is Not Continuity: Moving work to the next person or AI agent means little if they have to rebuild the story before they can act.
2. Access Is Not Context: Giving a worker access to the CRM, Slack, and email only hands them raw materials to build understanding themselves.
3. Execution Creates New Context: Every decision, conversation, and commitment made along the way has to travel forward with the work.
4. Bounded Context Beats Total Access: Each worker needs the context that fits their responsibility, not the entire history of the organization.
5. Follow One Handoff: Pick a single handoff in your business and look at what the receiving worker has to search for, ask, and reconstruct.

Links & Resources

• smrtPhone: https://www.smrtphone.io
• That Real Estate Tech Guy: https://thatrealestatetechguy.com

If this made you think about how work changes hands in your own business, pick one handoff this week and stand in the shoes of the person receiving it. You’ll probably find someone quietly carrying context around in their head. More high-signal conversations coming next.

This week I'm going solo to ask a question most business owners skip. You probably run a CRM, a billing platform, a support desk, and 10 or 20 other systems, so how does work actually move between them? I break down why those systems are great at storing information but terrible at moving the next piece of work, and why that gap gets exposed the moment you add AI agents to your workforce.

I walk through a real world example with an AI customer success agent named Maya, who does her job perfectly and then hits a wall when the work needs to go to a person. From there we get into shared execution, why people like Sarah have been the invisible connective tissue in your operations for decades, and a simple exercise to find where work stalls in your own business. If you're an investor or operator thinking about AI agents, workflow automation, and business infrastructure, this one is for you.

Episode Timeline & Highlights

[0:00] – Why most businesses run 10 to 20 systems that store information but don't move work

[1:06] – The real question: how does work move through your business after a status changes?

[1:40] – Why the answer is usually manual: emails, Slack posts, and copying data between systems

[2:57] – Why people have always been the ones moving work between your CRM, billing, and support tools

[4:38] – Why AI agents expose an infrastructure gap that businesses were never built to handle

[5:13] – Meet Maya, an AI customer success agent who spots a customer at risk of leaving

[6:52] – What happens when Maya hands off the work: a task, an email, a Slack message, or a ticket?

[8:23] – The constraint moves: AI agents become faster than the infrastructure around them

[10:36] – Why moving data between systems is not the same as moving work

[12:20] – Why shared execution between people, AI agents, and systems only works if work can move

[13:28] – Islands of execution, and how Sarah's invisible work fills gaps in your architecture

[16:10] – The infrastructure questions to ask: what triggers movement and who owns the work next

[17:06] – The exercise: pick one piece of work and follow it across your organization

[18:12] – The test: if the people moving this work disappeared tomorrow, would your systems know what to do?

[19:37] – Why context has to move with the work, not just a notification

Key Takeaways

1. Storing Is Not Moving: Your systems record what happened, but they rarely move the next piece of work on their own.
2. People Are the Connective Tissue: Salespeople, project managers, and coordinators have been manually carrying work between systems for decades.
3. AI Agents Expose the Gap: A capable agent still stalls when its output has to cross a responsibility boundary by email or Slack.
4. Move Work, Not Just Data: Syncing fields between systems is not the same as keeping execution going as work passes between people, agents, and systems.
5. Context Travels With the Work: The next worker needs to know what happened, what was tried, and why the work moved, not just a "please handle" alert.

Links & Resources

• smrtPhone: https://www.smrtphone.io
• That Real Estate Tech Guy: https://thatrealestatetechguy.com

If this made you stop and wonder where work sits waiting in your own business, pick one process this week and follow it from start to finish. You'll probably find your own version of Sarah. More high-signal conversations coming next.

This week, Jordan goes solo again to talk about a category of work that never shows up on an org chart: the checking, chasing, reminding, and reconciling that keeps a business running even though nobody assigned it to anyone. He walks through a hypothetical operations manager named Sarah, the kind of person every business has, who ends up compensating for a dozen invisible gaps a day without anyone realizing it's even happening.

Jordan argues this "invisible work" might be one of the biggest blind spots when it comes to finding real opportunities for AI, because if you only look at job descriptions and process maps, you'll never see it. The real question isn't whether an AI agent could do Sarah's job. It's why that work exists in the first place, and once you can see it, you can finally decide what should happen to it.

Episode Timeline & Highlights

[0:00] – Opens by describing a category of work that doesn't appear on any org chart, job description, or process map

[0:26] – Describes this as "the work between the work": checking, chasing, remembering, reminding, coordinating, and moving information around

[1:07] – Points out most businesses only think about work in terms of formally defined departments like sales, customer success, finance, and operations

[1:39] – Introduces a hypothetical operations manager, Sarah, described as indispensable because she knows how everything actually works

[2:11] – Walks through Sarah's day: she notices an incomplete customer step nobody asked her to check

[2:35] – Sarah catches a data mismatch between two systems and has to figure out which one is correct

[3:04] – Sarah recalls a customer's unusual arrangement from memory because the information isn't reliably documented anywhere

[3:28] – Sarah tracks down a stalled task, discovers two people assumed the other was handling it, and gets it moving again

[4:22] – Imagines Sarah disappearing for two weeks and the cascade of missed follow-ups and dropped context that follows

[5:28] – Argues Sarah isn't just doing her job, she's compensating for gaps in how the business is architected

[6:22] – Names this pattern "invisible work" and explains why it has to be recognized as work before it can be redesigned

[8:20] – Points out Sarah's actual job description almost certainly doesn't mention remembering missing information, chasing failed processes, or reconciling systems

[9:16] – Warns against just automating the compensation instead of fixing the underlying structural problem

[10:13] – Distinguishes between invisible work that is legitimate and should be formally assigned, and invisible work that only exists because something else in the business is broken

[13:08] – Offers a simple exercise: find the person everyone goes to when something gets stuck, and write down everything they actually do instead of their job description

[14:47] – Closes with the idea that some invisible work will go to people, some to AI agents, some will be shared, and some should disappear entirely once it's actually visible

5 Key Takeaways

1. The checking, chasing, and reconciling your best people do all day is still real work, even when it's not in anyone's job description.
2. Before handing invisible work to an AI agent, ask why that work exists in the first place.
3. Some invisible work is legitimate and should be formally owned; the rest is just a symptom of something else being broken.
4. Automating the compensation just makes a structural problem run faster, it doesn't fix it.
5. Find your "Sarah" and write down everything she actually does in a day instead of her job title.

Links & Resources

• smrtPhone: https://www.smrtphone.io
• That Real Estate Tech Guy: https://thatrealestatetechguy.com

If you've got a Sarah in your business, this is the week to actually write down everything she does instead of just being grateful she does it. More high-signal conversations coming next.