WLEI - Lean Enterprise Institute’s Podcast cover art

WLEI - Lean Enterprise Institute’s Podcast

WLEI - Lean Enterprise Institute’s Podcast

By: Lean Enterprise Institute
Listen for free

This Season's Biggest Deal | £0.99/mo for 3 months + £10 Audible bonus

£5.99/mo after 3 months - terms apply. Cancel monthly
Summary
 The official podcast of the Lean Enterprise Institute.Copyright 2018-2025 All rights reserved. Economics Management Management & Leadership
Episodes
  • Past, Present, and Future of Lean and AI

    Past, Present, and Future of Lean and AI
    Jul 8 2026
    Gene Kim, DevOps leader and co-author of Wiring the Winning Organization, joins Tyson Heaton, LEI Executive Director of LeanTech/AI, and Art Smalley, Toyota veteran and LEI advisor, for a lively, enlightening, and wide-ranging conversation about artificial intelligence. They discuss how AI is forever changing the work of individuals and organizations, and explore the good, bad, and ugly of AI today and tomorrow. One thing they all agree on: the use of AI is picking up, even among lean practitioners. Up to 80% of people in the lean community I suspect still have little to no understanding of AI (“What’s a model?”), estimates Art. But there are examples that the other 20% across industries have started running with it. Tyson mentions, “incendiary events,” like an interaction with a hackathon, that are fueling some of this momentum as individuals return to their work with new capabilities and an appreciation for the technology. “I've coded with chief medical officers, chief science officers, CEOs, CFOs, people in like revenue cycle management and healthcare,” says Gene. “And just to see these people all solving problems by themselves, you know, as they're using coding agents. It's just amazing to see.” Discussion highlights from this often-amusing podcast include: AI finds things that humans alone will not: At a personal and corporate level, AI is uniquely useful at analyzing and finding desired information in any form (data, images, etc.) within unstructured data, says Art. “One of the big angles of this LLM play into functions out outside of ops — whether you're in purchasing HR, legal, finance ,ops, whatever — they're all sitting on mountains of unstructured data... Apple, of all companies, has this problem, too. There's unstructured data, structured data, and types, and suddenly we now have a way to ingest a lot of that and make it searchable in a way that was not before.” Like lean standardization, AI removes waste from work that can open space for creativity: “Folks who are new to lean and standardization, they see a standard as potentially restrictive and limiting the creativity and limiting the potential,” says Tyson. Mature lean users, however, recognize that standardization removes constrains and allows them to create and experiment to produce better results. “A decent chunk of [knowledge work] is basically pattern understanding, pattern synthesis, and pattern projection to get work done,” he adds. “And if you can take and offload that or pair with AI, which it's really good at, it unleashes your creativity.” Negative reaction to improvements made possible with AI: Gene tells the story of an individual who would do many hours of Excel work each day on a highly specific “horrendous” task, and then AI applied a macro that could do it for her. “Her reaction was anger. She's like, ‘How dare you? I didn't ask for this.” AI removed something she held “near and dear.” Tyson says a path to overcome this natural human reaction to someone’s work being removed is a better foundation of understanding of value-add vs. non-value add vs. incidental work from the customer’s perspective. The fear of AI taking jobs is real for some individuals and in some companies: Art relates AI improvements back to early lean improvements, when removing non-value-added piece of work from someone’s work was viewed as taking away their job; they did not recognize the lean improvement was freeing them to think of the other ways they could be adding value to the organization. Tyson says it’s critical to help rewrite people's identity as creators and improvers rather than lathe worker or spreadsheet specialist. “It's also kind of a tragedy that so many people and so many organizations aren't afforded the ability to remake their identity as an improver of their own work processes and workflows because of either the leadership structure or the behavior, the cultures of the organization.” Overcoming resistance to AI: Pair coding/programming (when two people code together) and learning by doing together has real value in getting individuals to embrace what’s possible with AI, says Art. “If there's a respected person in the organization, sometimes there's peer learning that occurs. There's different ways people get influenced, different directions. And peer coding seems to me to be a very natural way of transfer of information that's often mutually beneficial, and you get a better result, learn by doing, low risk, high reward.” He adds the interest in AI goes from incremental to exponential. Gene agrees, and points to examples of increased activation among individuals learning it and just doing it with respected members of their technical community. “We're now in a universe where it is easier to just do the experiment than it is to sort of like ...
    Show More Show Less
    1 hr and 14 mins
  • Better Thinking Faster: A conversation about solving problems just in time

    Better Thinking Faster: A conversation about solving problems just in time
    Jul 13 2026
    Time is relative, especially when it comes to lean problem solving. The LEI trio of Josh Howell, Mark Reich, and Art Smalley explored how the speed of quality problem solving really depends on the level of the organization in which the problem exists and the types of problems and thinking patterns in place at that level. In this first podcast of two, they examine what “better” and “faster” means in the lean world and how that manifests itself. There’s often a debate among management circles calling for fast problem solving and change vs. slow, deliberate decision-making. Art, Toyota veteran and author of Four Types of Problems, says, “We’re all trying to think better and think faster, respective to our level and the speed that governs it. But it’s overly simplistic to just say ‘think slow.’ You want to, all things equal, arrive at a better end result faster. In reality there are different types of problems, different patterns of thinking, and different mental gears that can be used.” Art discussed different types of problems and the different styles of work used in Toyota to address them. As outlined in his book, they are: Type 1 troubleshooting problem, such as responding to the andon signal at a frontline workstation with a 45-second cycle time, needs to be solved safely and quickly, which will often occur countless times each day because these problems involve a known problem and known solution. Type 2 problems are harder to solve and have unknown root causes and solutions, which require a more analytical mode of thinking. Type 3 problems are actually continuous improvement efforts that seek to establish a new, better target condition. Type 4 problems represent truly innovative new solutions (think hybrid powertrain technologies) that often require years of trial and error. The problem-solving work moves from convergent thinking, known standards, and highly skilled execution at the frontline to middle management using the eight steps of Toyota Business Practice that can take days or weeks, to innovation, which is slower, more deliberate, divergent, and exploratory. Josh looks back to his time at Starbucks and the problem-solving spectrum, contrasting the challenge of trying to transform a company of some 350,000 employees (“That’s going to take some time, and certainly the executives don’t want it to take too much time. It can’t take forever.”) compared to experiencing problems in a store during peak service time when there’s a new customer every 30 seconds. “The way that we were taught from our TPS and lean teachers to improve that situation, improve our performance for the customers, was to really dig into the details of what happens inside those 30 seconds.” Speed is only one facet of lean problem solving. Remember, the Toyota slogan is “Better Thinking, Better Products,” and so quality has to be part of problem-solving work. Mark (LEI Chief Engineer Strategy, author of Managing on Purpose, and also a Toyota veteran) says the quality of problem solving requires developing the capabilities of those doing the problem solving as well as the patience to take sufficient time to examine problems and come to better solutions: “We want to build the capability in the organization for leaders, for people, not just leaders, but for people at every level, to be able to produce good quality work. What’s unique about Toyota is that’s true in any function. We want good quality accounting work. We want good quality planning work. We want good quality work, of course, at where the products made and developed. And I think that does take time to build that capability to produce good quality, whatever function you’re talking about.” The trio also discuss the: Cyclical nature of processes in which problems occur, be that a seconds-long cycle on the line, an annual cycle for formulating corporate strategy, or a decades-long span for replenishing capital equipment. At all levels, the process repeats but the problems will vary, such as market changes impacting the strategy development cycle one year to the next. Use of process cycles as learning: From frontline to the executive suite, it’s not just about fixing the current problem and getting back to work, but taking the time and data to come up with better solutions and standards for future work. “There’s so much here to look closely at, to analyze, to scrutinize,” says Josh. “There’s so many details that we can question, and inside all of those details are potential solutions, potential improvements, kaizen points that we can make things better.” Need to involve as many people as possible in addressing problems — the work of problem detection, notification, response, and solving. Considering the number of problems that exist ...
    Show More Show Less
    40 mins
  • Sebastian Fixson and Jim Morgan on Why Human Judgment will be Essential for Product Development in the Age of AI

    Sebastian Fixson and Jim Morgan on Why Human Judgment will be Essential for Product Development in the Age of AI
    Jul 27 2026

    In this episode of the WLEI Podcast, we speak with Jim Morgan and Sebastian Fixson about organizational implications for product development and what building your team’s product development capabilities looks like in the age of AI. Morgan is the author of Designing the Future and has spent over 30 years in industry as a product development leader including serving as a global engineering director at Ford Motor Company during the product-led revitalization under CEO Alan Mulally. Sebastian Fixson is Founding Faculty Director of the DBA Program and Professor of Innovation & Design at Babson College and author of The Innovation Navigator: Transforming Your Organization in the Era of AI.

    The conversation explores:

    • What people-first leadership means in practice
    • Why particular structures, processes, and practices are needed in product development to help people do their best work
    • The trade-offs inherent in functional and product organizations
    • The organizational and product development implications of new technologies including generative AI
    • How today’s entrepreneurs are thinking about product development and how to invest in developing the next generation of developers
    Show More Show Less
    27 mins
adbl_web_anon_alc_button_suppression_t1
No reviews yet