A (0:00)

Good morning, good afternoon, or good evening, depending on where you are in the world. And welcome to this week's episode of the AI Lean Edge. My name is Brian Bricker and I'm coming to you from my office here at AI Lean Solutions. And I'm very excited about today's show. We're going to talk a little about how far technology's come in the last 40 years. And you don't want to miss today's crazy visit to 1985. We'll also be talking about the current state of AI, how you shouldn't listen to the naysayers who claim that AI investing is experiencing a bubble like the.com one that decimated the early Internet. And I'm going to try and get you to fully understand that not only are you underestimating how AI and robotics are about to change our entire existence, but that it's going to happen fast and you need to be prepared for it. And of course, this wouldn't be the AI Lean Edge without talking about lean thinking. Today we'll be talking about finding the waste in your small business. There's a funny thing about waste. Most of us think we'd know it if we saw it like trash in a bin or hours lost scrolling social media. But in business, waste doesn't always look like waste. It looks like effort. It looks like meetings. It looks like hard work. It hides behind good intentions, polite habits, and the stories we tell ourselves about how things are done around here. And that's what I want to talk about today. Because if you're a small business owner, you're not just battling competition or cash flow. You're battling the invisible weight of waste that's been quietly draining your energy, your profits, and your team's morale. When people hear the word waste, they sometimes picture physical stuff, materials defects, or inventory. And that's definitely part of it. But the kind of waste that's really killing your business is mental waste, decision waste, communication waste. It's the wasted time between tasks, the wasted motion between people, and the wasted confidence that comes from doing things the same old way because it feels safer than changing. If that sounds familiar, don't worry. We're going to unpack this together, step by step. Because spotting waste isn't about blame. It's about clarity. And when you combine that clarity with modern AI tools that learn your habits, anticipate your needs, and streamline your workflows, suddenly your business starts to breathe again. So sit back, grab a coffee or your favorite adult beverage, and let's get into it.

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Chaos Out Clarity in Cut the noise and dial in the wind A Eileen Edge. Stream it, dream it, lean it tight Everything just works. Hey, Eileen Edge say goodbye to the sludge Run it clean, run it mean.

A (3:01)

Welcome back to the AI Lean Edge. I am still Brian Bricker. And if you're a small business owner looking to get some time back in your life, you are in the exact right place. Before we get into identifying and fixing waste in your business, let's talk for a few minutes about technology and the current state of AI. As you know, over the past few weeks we've been talking about technology advancements from 40 years ago, back in the good old year of 1985, and today I thought we'd put a twist on it just to see how much tech has changed in the four decades since we were in high school. And you're not going to believe what I found for my consulting work here at Aileen Solutions. I got a new HP laptop right here at Costco for about 1200 bucks. Costco. So basically I bought it at a grocery store and took it to the register jammed between some steaks and a year's supply of laundry detergent. It's a great little lightweight computer with 32 gigs of RAM, 6 gig graphics card, and a 1 terabyte hard drive. I can toss it in my briefcase, carry it around my office from meeting to meeting and sit in bed with it to do emails before going to sleep for 1,200 bucks. Now, what was the equivalent to this laptop in 1985? You're not going to believe this. In 1985, RAM cost roughly $300 per megabyte. So a 32 gig system like this would have cost in 1985 $9.6 million just for the RAM. So now let's get into the graphics card. I've got six gigs of virtual RAM on this for the graphics card workstation. Graphics setups back in 1985 were definitely primitive compared to today. But a high end frame buffer with less than one megabyte of memory would have cost around $40,000 40 years ago. So scaling that up even modestly to a 6 gigabyte GPU would have easily cost between 10 and 20 million dollars, assuming you could have even built it at that time. So then finally a one terabyte hard drive in 1985, hard drives cost about $100 per megabyte. So one terabyte in this laptop right here in 1985 would have cost $100 million. This is insane to think about. So if you could somehow build a computer with today's specs back in 1985. First, it would have only fit in a gargantuan government facility and would have required immense hardwired electrical power. Yet mine fits in a backpack and runs six to eight hours on a battery. The total cost difference, well, what cost me $1,200 today at a grocery store would have cost the equivalent of $350 million in 1985. And much of what my laptop has was not even possible four decades ago. Think about this. My laptop outperforms every supercomputer on Earth in 1985, all of them combined. And I was able to just grab it off a shelf at the same store you buy shampoo and rotisserie chickens. This is wild. So think about that for a minute and really let it sink in. It's only been 40 years and everything with technology is just going faster now. So let's look at the current state of AI and based on what we see in the computer differences between 1985 and now, I think we've got an amazing future in store for us. Okay, so based on all current assessment standards and evaluations for AI, here are the current top five models. Number one, we've got Claude Sonet 4.5 and Claude Opus 4.1. They are excellent for general performance, reliable writing and strong safety features. Number two, Gemini 2.5 Pro from Google. It's a state of the art long context system with multimodal vision that ranks top tier in text and vision arenas. Number three, OpenAI's GPT5 or 4.5 or the O3 family. They're strong on coding and math reasoning, they're competitive in vision and they're very prominent near the top of live leaderboards. They number four, Quinn three Max. It's a fast improving open ecosystem contender now appearing in the top cluster in public evaluations. And then finally number five rounding out our top five current Grok 4 from Xai Elon Musk's AI. It's a standout in general IQ and math heavy tasks, signaling rising competition in pure reasoning ability. So what's changed most this year? So the three shifts that define 2025 so far in AI world one reasoning models went mainstream. Deliberate chain of thought and tool augmented models pushed past earlier ceilings in math and science contests and in software tasks. Number two, multimodality became routine. What this means is the top models read, write, see visually and increasingly plan their own actions. With Gemini 2.5 and OpenAI's latest 4 and 5 tier leading the vision and Long content context work number three. Benchmarks have been diversified so communities are now blending human preference voting with hard exams to reduce overfitting and hype on AI progress. So basically what this means is that the way that they're evaluating AI models are not only based on hard exams and and human knowledge tests, but also based on human experience interacting with those AIs and feedback from with this. Stanford's AI index also flags the rapid spread of AI across industries and the need for better unbiased measurements. So the benchmarks are becoming a larger topic as they're trying to figure out benchmarks that, that that can continue to evaluate the AIs as they as they advance. So solid field studies show near term productivity gains where AI assists humans, especially novices. Customer support agents with a generative copilot resolved more issues per hour and needed supervisors less so consulting style tasks also sped up. These are real repeatable gains and at the same time broad labor Data in the US shows no sudden economy wide job shock. Yet multiple new analyses find AI exposure is high, but displacement to date, which means job loss to date is limited compared with the hype. But it is coming. Be aware of that, you know, and plan for it, learn about it and it's something that is going to impact our society. So looking at the near future, which means the next 12 to 24 months, experts are split on the timelines, but agree on an acceleration of AI throughout our society and throughout our industries. Some Experts predict a 50% chance of human level AI systems within five to 10 years, while others are arguing we're within 12 months of it right now. Either way, expect dramatic leaps in science, drug discovery, code generation and autonomous agents that operate software and lab tools independent of human handlers. The World Economic Forum projects significant task reshaping across most roles by 2030, with augmentation often beating outright replacement. More simply put, every job will be impacted by AI in the next few years, with some saying it'll be the single largest societal shift in human history with entire industries moving from human to AI power almost overnight. Although I personally think AI will initially be more about enhancing human worker ability, we absolutely need to be prepared for potential seismic shifts in the job market. And it's looking more and more like colleges and universities are quickly becoming irrelevant. There's a huge spike in high schoolers choosing trade schools over traditional higher education because it'll be much longer before AI and robotics moves into construction H Vac, electrical and plumbing jobs. And they are definitely in tremendous need right now. And for the foreseeable future, especially trades necessary to build the massive AI data centers that are coming online or being planned almost weekly right now. Markets are racing ahead too, which raises systemic risk. Some analysts are warning of circular financing and a potential bubble as chip vendors, clouds and labs cross subsidize one another. But unlike the dot com bubble, AI growth is real and can actually prove the valuation being attributed to it. So what you think of as a bubble and what you saw in the dot com bubble and is not going to be repeated, although you're going to hear it on the news constantly right now. AI and robot tech will transform the world, even if the financing does get wonky in the meantime. So in terms of the midterm arc of this, what it means for society. So think of it in three waves that are going to overlap. First of all, there's going to be copilots everywhere. Most desk jobs will pair humans with a task aware assistant that can draft, check and file. Expect faster throughput, fewer routine errors and a premium on judgment and taste. Field results already are pointing this way. Number two, synthetic experts. Reasoning models that pass hard science exams will start running lab notebooks, skimming literature and proposing experiments. Breakthroughs will concentrate in well instrumented domains like chemistry, materials and biology. But oversight will still be essential. Number three, autonomous systems under guardrails. So agents that operate computers and equipment will demand new safety standards. Mainstream journals now frame AI safety as applied engineering that must scale with any deployments. So there's real disagreement on the ultimate risks to AI. Some are pointing to significant human extinction odds and urge strong regulation. Others, and I've definitely fall in this camp, see incredibly high benefit and think the risks are manageable with the right designs. So what's the bottom line on AI today? I'd say well the front of the pack is definitely tight and you know, depending on the day or the week or sometimes even the hour, you know, they're, they're all shifting for that, that top five space. The benchmarks are getting harder necessarily and the gains are moving from demos into day to day work. Near term, the biggest wins come from pairing people with models to do practical tasks better and faster. Midterm, the spillover into science code and autonomous work will absolutely dwarf the Internet era and the industrial revolution. Prepare for co pilots, invest in data quality and controls and plan for volatility in both capability and capital. But ultimately we're about to witness the most amazing, disruptive and excessively successful period in the history of society. Sound a little dramatic? I don't think I've even Done justice to what's coming. Buckle up, stay positive, and prepare to enjoy the ride. Okay, now on to our lean thinking topic for today. Finding and resolving waste in your small business processes. A few years ago, I worked with a small logistics company that handled regional deliveries for several manufacturers. They weren't inefficient by traditional standards. In fact, they were proud of how busy everyone was. Phones were ringing, drivers were moving, dispatch was tracking shipments. It all looked productive. But underneath that noise was chaos. Emails went unanswered, orders were double entered, drivers would call for clarifications. That should have been clear in the system when we finally mapped it out. They were wasting nearly 22% of their total staff time just trying to reconcile information. Not doing work, not serving customers, just fixing the same errors over and over again. That's waste. Not because anyone was lazy, but because the system itself was unexamined. In lean thinking, waste isn't about blaming people. It's about understanding the process. And the reason small business owners struggle to see waste is because they're too close to it. When you've built something from scratch, every system feels personal. I know. So the first principle of spotting waste is detachment. You have to step outside your own system long enough to look at it objectively. Think of your business as a living system, an organism with inputs, outputs and energy flowing through every part. Some processes hum along smoothly, others grind and sputter. Now imagine you could see those inefficiencies like heat on a thermal camera. Bright orange, where effort's being wasted. Cool blue, where things are flowing. That's what Lean and AI together allow us to do. AI gives you data visibility. Lean gives you context. Without Lean, AI just automates bad habits. Without AI, Lean stays stuck in manual analysis. Together, they let you see patterns you'd otherwise miss. But before we talk about what AI can do, let's talk about what you can do. Starting today. Lean identifies seven classic categories of waste known as muda. Overproduction, waiting, transportation, over processing, inventory, motion and defects. These apply everywhere, from manufacturing floors to coffee shops, real estate offices, or even consulting firms. Overproduction in a bakery might mean making too many pastries. In a consulting business, it might mean writing a 10 page proposal when the client only asked for a quote. Waiting. That's approvals, signatures and decisions that never come. Transportation. That's your data bouncing between tools and inboxes with no added value. Over processing, doing more than the customer actually needs, like running extra reports just to feel thorough. Inventory waste can be unsold products, but it can also Be unused data, unfinished ideas, or someday projects clogging your to do list. Motion is both physical and mental. The wasted movement between tasks, or the mental switching from one thing to another all day long and defects. Well, that's anything that requires rework. The invoice you resend, the client you have to follow up with twice the common thread. Waste is anything your customer wouldn't pay for. Now let's connect that to mindset. Most business owners aren't wasteful, they're overwhelmed. They've accepted chaos as the price of doing business. But chaos isn't proof of growth, it's proof of noise. And when you shift from doing more to doing better, you start to see your business as a set of interconnected systems, not a list of tasks. That's systems thinking. It says everything is connected. How you manage email affects how you handle customers. How you start meetings affects how you finish projects. Every habit feeds into a larger flow. If you want to spot waste, don't just look for mistakes. Look for handoffs. The moment between steps. It's in the pause after a decision when no one knows who owns the next move. It's in the weekly meeting that no one questions anymore. It's in the duplicate spreadsheet that just seems safer to keep. Once you start seeing those gaps, you can't unsee them. And that's where AI becomes powerful. AI isn't magic. It's just a mirror. It reflects how your business actually operates, not how you think it does. When you plug in AI tools, whether it's analytics, a chatbot, or automation, you're teaching a machine how your systems behave. And what it reveals is often uncomfortable but incredibly valuable. AI doesn't lie. It reveals. It shows where you repeat yourself, where steps bottleneck, where time disappears. And that's the liberating truth. AI doesn't just save time. It exposes what was being wasted all along. Let me tell you about one of my clients. She ran a small accounting firm. Great people, loyal clients, lots of paper. When she first heard about AI, she said, well, we. We don't really have time to learn this. And I told her, that's exactly why we start here. Because if you don't have time to learn how to stop wasting time, you'll never have time. So we begin with process mapping. Not fixing anything, just seeing it, just looking at how it operates as of right now, in real time, in reality. And right away, the pattern started to appear. Every accountant had their own filing system, different naming conventions, different different folder structures. AI didn't fix it, but it revealed it. And once they saw the variation, they standardized it. Then they used a simple AI tool to automatically tag and classify every document. Three months later, the team had reclaimed nearly 40 hours a month. That's not a technology story, that's a clarity story. Clarity builds confidence. Once you can see waste, you stop taking it personally. You realize the system is performing exactly as it was designed to, and that design just needs improvement. And that's the essence of lean thinking. Continuous improvement. Not perfection, not control, just attention. The same applies to AI. You don't have to code, you just have to think. AI rewards curiosity. The more you explore, the more it shows you the hidden friction in your business. And I'll say this again because it's worth repeating. AI and lean aren't about replacing people, they're about freeing people. When you remove waste, you don't just save money. You give your team back their focus, their creativity, and their sanity. And that's the kind of return on investment that changes everything. Which does bring me to my book, Smarter, Leaner, Faster. How AI and lean thinking can transform your small business business. If you're finding value in what we're talking about today, or in this podcast in general, this book takes you deeper into how Lean and AI work together to help you design smarter systems, spot hidden waste, and grow sustainably. I wrote it for small business owners like you. Not coders, not engineers, but people who want their business to finally start working for them, not against them. You can find Smarter, Leaner, Faster on Amazon or at www.aileansolutions.com. and while you're there, grab some free resources like the top 10 AI tools for small Businesses. Let's close with this. Waste isn't always a pile of scraps or an empty slot in the calendar. It's in your routines, your meetings, and your email threads. AI can't define your purpose or care for your customers. That's on you. But it can free you up to do that work better. When you combine lean clarity with AI intelligence, you stop chasing efficiency and start designing it. And that's the edge. The AI lean edge. I'm Brian Bricker. Thanks for listening. If you're getting something out of these podcasts, please follow, like, comment and share with other small business owners so we can help as many people as possible. Until next time, keep learning, keep improving, keep experimenting, and keep leaning into AI. Run it.