AI Made This Podcast - Notebook LM October 2025

Notebook LM has had some significant updates over the last few weeks

Have a listen to the latest iteration of a Notebook LM created podcast based on ReThink marketing materials

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Chapters

• 0:09 Why Efficiency Really Slips
• 0:25 The Three-Level Work Study Framework
• 1:45 Level 1: Team-Wide Efficiency Map
• 3:12 Level 2: Task Activity Breakdown
• 5:05 Level 3: MTM And Micro-Movements
• 6:36 Spotting Waste In Plain Sight
• 7:32 The Leadership Paradox Emerges
• 9:28 Managers Drowned By Ops And Comms
• 11:09 Role Overlap And Structural Fixes
• 12:19 Turning Data Into Better Staffing

Transcript

SPEAKER_01: 0:05

Welcome back to the deep dive. Okay. Today we're tackling something really fundamental for well, any business really. How do you get a real handle on operational efficiency and productivity fast? Aaron Powell Exactly.

SPEAKER_00: 0:17

And we're not talking about your personal to-do list apps here.

SPEAKER_01: 0:20

No, no. This is the Siri stuff. We're driving into the science of work study. It's like industrial engineering. We're really focused on how labor and processes actually work or sometimes don't work. Trevor Burrus, Jr.

SPEAKER_00: 0:31

Right. And the sources we've looked at, they really detail the modern approaches. You see this a lot in sectors like retail, hospitality, call centers, logistics, places with high volume, lots of moving parts. So the mission today is It's about uncovering how time is really being spent, not how people think it's spent or how the procedures say it should be spent, but the reality on the ground. We want to pull out the actionable insights from that data.

SPEAKER_01: 0:56

Aaron Powell And just as a little teaser, one of the big things that jumps out is that productivity problems, they're often not about lazy workers. They're baked into the structure.

SPEAKER_00: 1:05

Oh, absolutely. And specifically, we found data showing many managers, especially outside those big flagship locations, spend, I mean, shockingly little time actually managing people.

SPEAKER_01: 1:16

Aaron Powell We'll definitely get into that leadership paradox. That sounds crucial, especially for keeping good people.

SPEAKER_00: 1:22

It really is. Big retention issue often. But first, I think we need to understand how they figure all this out, the methodology. Trevor Burrus, Jr.

SPEAKER_01: 1:30

Right. You mentioned it's structured.

SPEAKER_00: 1:32

Yeah, it usually breaks down into three levels, kind of like zooming in, you start broad, then get more and more granular.

SPEAKER_01: 1:37

Okay. Let's start broad then. Level one, what's the big picture it gives a manager?

SPEAKER_00: 1:41

Aaron Powell So level one is what we call the team-wide efficiency study. Think of it as the top-line diagnostic. It's usually done over a decent period, like a full seven-hour shift or workday.

SPEAKER_01: 1:51

Aaron Powell And it's looking at the whole team.

SPEAKER_00: 1:53

The whole team, yeah.

SPEAKER_01: 1:54

Yeah.

SPEAKER_00: 1:54

It's capturing what activities are happening when customer demand hits, basically creating a baseline map of where resources are going.

SPEAKER_01: 2:01

Aaron Powell So it's more than just clocking in and out. What kind of picture does it paint?

SPEAKER_00: 2:05

Aaron Powell Well, yeah, it's not just hours worked. Critically, it splits that time out. How much time is spent genuinely interacting with customers? How much on essential back office or operational tasks?

SPEAKER_01: 2:17

Aaron Powell And the third bit.

SPEAKER_00: 2:19

And this is often the eye-opener. How much time is non-value add or NVA? Basically, downtime, lost time.

SPEAKER_01: 2:26

Ah, okay. So that straightaway shows you where there might be Slack or maybe where people are just waiting around.

SPEAKER_00: 2:32

Aaron Powell Precisely. It gives you that internal picture first, but then comes the benchmarking.

SPEAKER_01: 2:36

Aaron Powell Right, comparing it to others. That must be powerful.

SPEAKER_00: 2:39

Aaron Powell Hugely. The L1 data gets compared against this massive database from similar businesses, similar sectors. Suddenly you're not just guessing. Aaron Powell So you can answer questions like, how much downtime do we have compared to the average or the best in class? And crucially, how much stretch are our teams under? Are they constantly rushed off their feet? Or is there capacity? It takes the emotion and opinion out of it.

SPEAKER_01: 3:04

Okay, that makes sense. So you've got the big picture from level one. Where does level two take you?

SPEAKER_00: 3:08

Level two gets more specific. It's the task activity study. Now we're zooming in on core tasks end-to-end.

SPEAKER_01: 3:14

So like thing it takes to process an order or check a guest in.

SPEAKER_00: 3:18

Exactly. But it's not just the total time. The key here is breaking that task down into its individual steps. The elemental level.

SPEAKER_01: 3:28

Why break it down so small?

SPEAKER_00: 3:29

Because that's where you find the opportunities. You see exactly which step is taking too long, which part could maybe be automated, or you know, maybe a step that could be cut out altogether.

SPEAKER_01: 3:38

Aaron Powell Do you have an example of where that really makes a difference?

SPEAKER_00: 3:40

Oh, definitely. Think about warehouses, logistics, picking items. The studies consistently find something like half the time spent picking isn't grabbing the item.

SPEAKER_01: 3:50

Aaron Ross Powell What is it then?

SPEAKER_00: 3:52

It's traveling, just walking or driving the forklift between locations.

SPEAKER_01: 3:55

Aaron Powell Right. So the problem isn't necessarily the picker speed.

SPEAKER_00: 3:58

Aaron Ross Powell Exactly. It's the warehouse layout, maybe the route they're given, the sequence on the pick list, the fixes in the design, not just telling someone to hurry up.

SPEAKER_01: 4:07

Okay. That's level two. Then there's level three going even deeper.

SPEAKER_00: 4:11

Yep. Level three is the deep dive movement analysis. This is often where they use MTM methods time measurement.

SPEAKER_01: 4:18

Aaron Powell MTM. Sounds technical.

SPEAKER_00: 4:20

Aaron Powell It is. It's like putting the process under a microscope often uses video analysis. You break down the work into these tiny, standardized human movements like get put, easy reach, turn, really small actions.

SPEAKER_01: 4:34

Aaron Powell And why on earth would you need that level of detail?

SPEAKER_00: 4:36

Aaron Powell It's perfect for tasks that are super short, really repetitive, done maybe thousands of times a day. Think fast food, assembly lines.

SPEAKER_01: 4:45

Aaron Powell Ah, okay. Where shaving off even a tiny bit of time adds up massively.

SPEAKER_00: 4:49

Aaron Powell Precisely. Saving a second on a task done 10,000 times a day, that's huge savings over a year.

SPEAKER_01: 4:54

Can you give a concrete example, something relatable? Aaron Powell Sure.

SPEAKER_00: 4:57

Quickserve restaurants are classic. NTM analysis looked at making burgers. They found that installing a simple hands-free sauce dispenser saved measurable time. Because before the worker had to put the burger box down, pick up the sauce bottle, dispense, put the bottle down, pick the box back up. That little put-down pickup cycle, it adds up. The hands-free dispenser eliminated it.

SPEAKER_01: 5:16

Wow. Just changing the dispenser.

SPEAKER_00: 5:18

And the MTM data quantified the exact time saved, which made justifying the cost of the new dispensers really easy. It's hard data.

SPEAKER_01: 5:26

That's fascinating. And I guess this kind of analysis, levels two and three, also flags the obvious waste, right? The stuff we probably all see.

SPEAKER_00: 5:33

Oh, yeah, absolutely. That's a core output. Things like double handling stock, picking it up, putting it down, picking it up again later, adds time, risks damage.

SPEAKER_01: 5:43

What else?

SPEAKER_00: 5:44

Or you see this everywhere. Self-checkout tills. They spit out receipts, and maybe 80% of people just leave them there. Why are we printing them? It's ink, paper, machineware.

SPEAKER_01: 5:55

Good point. Or using handwritten lists for things.

SPEAKER_00: 5:57

Right. Paper fill-up lists on a shop floor instead of using a handheld device linked to a central system. These things seem small, but they're measurable inefficiencies, structural waste.

SPEAKER_01: 6:07

That level of detail is amazing. But okay, let's shift gears a bit. Because you said the biggest insights often come down to the people, especially leadership. You called it the leadership paradox. Here's where it gets really interesting.

SPEAKER_00: 6:18

Yeah, this is often the most surprising finding for businesses. We're talking now about the role study. It's like a day-in-the-life observation, but very structured. Tracking managers, specialists, understanding how their time is actually spent.

SPEAKER_01: 6:33

Compared to how the company thinks it's spent or intends it to be spent.

SPEAKER_00: 6:36

Exactly. And look, everyone agrees the difference between a good store or a good branch and a great one, it's usually the local leader, the manager. Trevor Burrus, Jr. Sure.

SPEAKER_01: 6:44

That makes sense.

SPEAKER_00: 6:45

But the data shows that many businesses, completely unintentionally, build systems and processes that actually stop their managers from leading effectively. They get trapped.

SPEAKER_01: 6:56

Trapped doing what? Yeah. What does the data actually show about how managers spend their time, especially in, say, smaller branches or stores?

SPEAKER_00: 7:02

Aaron Powell Okay, brace yourself. The numbers are pretty stark. For managers outside the really big flagship locations, some were spending as little as 1%.

SPEAKER_01: 7:11

1% of their day.

SPEAKER_00: 7:12

1% of their time on actual management activities, coaching, developing people, strategic thinking, that kind of thing.

SPEAKER_01: 7:20

So what were they doing?

SPEAKER_00: 7:21

Aaron Powell The average across the board was only about 15 to 20 percent on people management. The other 80-85%. It gets sucked up by operational tasks. Covering brakes, stocking shelves, dealing with immediate firefighting. They just become an extra pair of hands.

SPEAKER_01: 7:37

Wow. So the most experienced, probably highest paid person on site.

SPEAKER_00: 7:40

Aaron Powell is often doing the most basic operational work. It's hugely inefficient from a cost perspective. But worse, think about the team. They're not getting coached, not getting developed. It kills morale and career progression.

SPEAKER_01: 7:54

Aaron Powell Yeah, I can see that. And it's not just the hands-on stuff, right? What about communication?

SPEAKER_00: 7:58

Aaron Powell That's another big time sink. We did a study on area managers and the people who look after multiple stores, found that around 15% of their entire week was just spent reacting to emails, teams messages, WhatsApp groups, just this constant barrage of digital communication.

SPEAKER_01: 8:12

So they're just constantly interrupted.

SPEAKER_00: 8:14

Constantly. Even if the tools were meant to help, the sheer volume prevents them from doing the deep work, like having proper focus coaching sessions with their store managers. They're just fighting digital noise.

SPEAKER_01: 8:24

And you also mentioned something about admin time, like Parkinson's law.

SPEAKER_00: 8:28

Oh, a perfect example. There was a fast food chain. They decided managers should have dedicated admin time on Mondays. Off the floor, focus on reports, analysis, good intention, right?

SPEAKER_01: 8:40

Sounds sensible.

SPEAKER_00: 8:41

Initially, maybe it took them, say, three hours, but because Monday was blocked out for admin, the work just expanded. It filled the whole day.

SPEAKER_01: 8:50

So they spent all of Monday on admin, even if it wasn't needed.

SPEAKER_00: 8:54

Pretty much. It became this entrenched habit, a whole day away from the team, away from the operation. And even when data showed the actual admin needed way less time, it was really hard to break that pattern. The structure created the inefficiency.

SPEAKER_01: 9:08

Aaron Powell That makes the case really strongly for looking beyond just one role, doesn't it? Looking at the whole structure, the hierarchy.

SPEAKER_00: 9:14

Absolutely. That's where you use the data for bigger structural decisions. You use internal benchmarking first. Comparing the same role across different sites, is the team manager job in store A basically the same as in store B? Or is one person carrying way more responsibility, maybe doing tasks that should belong to a different role? It checks for consistency.

SPEAKER_01: 9:34

Okay. And then the really revealing part must be the role overlap.

SPEAKER_00: 9:37

Aaron Powell That's often where the big savings are found. The analysis lays bare where different roles are essentially doing the same things, redundancy.

SPEAKER_01: 9:44

Aaron Powell And you have actual numbers on this?

SPEAKER_00: 9:46

We do. For instance, data consistently shows that in many retail or hospitality setups, a manager and their assistant manager spend, get this, 68% of their time doing the exact same tasks.

SPEAKER_01: 9:57

Aaron Powell 68%. Wow. So more than two-thirds of the time you've got two different management salaries paying for the same work.

SPEAKER_00: 10:03

Aaron Powell Effectively, yes. You've got duplication baked into the structure. And it gets even higher sometimes between a team leader and a supervisor, often around 78% overlap. That's huge. It is. And that's the kind of objective data businesses need to say, justify flattening the structure, maybe going from four layers of management to three, and crucially, implementing really clear role profiles so everyone knows exactly what they are responsible for and what they're not.

SPEAKER_01: 10:30

Okay. So let's pull this together. We've gone from the big picture down to tiny movements, looked at managers' time. What does this all mean for someone listening? How do they use this?

SPEAKER_00: 10:40

Well, I think the real power of work study done properly is that it takes the guesswork and frankly the politics out of decision making.

SPEAKER_01: 10:48

It's objective.

SPEAKER_00: 10:49

It's objective. It gives you a solid data-driven reason for making changes, sometimes tough changes. It lets you build accurate workload models, sometimes called rebudget systems, so you can match your staffing levels precisely to when your customers actually need them.

SPEAKER_01: 11:03

So it's about efficiency but also effectiveness.

SPEAKER_00: 11:06

Exactly. The goal isn't just to cut hours or make people rush, it's about freeing up time. Maximizing the time your team spends on the things that actually make a difference to the customer, the things that make your business unique. You really can't. Understanding how time is used, how processes flow, it's fundamental to staying profitable, staying competitive. It's the foundation.

SPEAKER_01: 11:35

Absolutely. So just to recap quickly, we looked at the three levels: the team study for the big picture, the task analysis for workflows, and the MTM deep dive for those high frequency actions. Right. And probably the biggest takeaway for many organizations is that leadership paradox managers needing to be freed up from operational tasks to actually lead.

SPEAKER_00: 11:54

Definitely a major finding. So maybe here's a final thought for you to chew on based on that data.

SPEAKER_01: 11:59

Go for it.

SPEAKER_00: 11:59

If we know that, say, a manager and assistant manager are spending 68% of their time doing identical tasks and area managers are drowning in emails, ask yourself this. Is your biggest productivity bottleneck really about how fast people work? Or is it simply about a lack of clarity? Maybe the biggest win comes not from speed, but just from clearly defining who does what.

SPEAKER_01: 12:20

Clarifying roles and responsibilities based on data.

SPEAKER_00: 12:23

Exactly. Could that clarity unlock more value than anything else? Something to think about.