Podcast Summary

Podcast: Artificial Intelligence Podcast: ChatGPT, Claude, Midjourney and all other AI Tools

Episode: Can AI Learn to be Lean with Chad Bareither
Host: Jonathan Green (A)
Guest: Chad Bareither (B), Lean Operations and Continuous Improvement Expert
Date: January 5, 2026

Episode Overview

This episode explores the intersection of artificial intelligence and lean operations, asking: Can AI actually help us work “leaner” or does it risk amplifying inefficiency? Guest Chad Bareither, a consultant specializing in lean methodologies for manufacturing and service businesses, joins host Jonathan Green for a candid discussion. They challenge common assumptions about AI, especially the belief that adding more tech always produces better results.

The conversation spotlights how the ease and affordability of AI can unintentionally fuel business bloat, create unnecessary complexity, and distract teams from real value creation. The episode is especially relevant to small and medium-sized business owners and leaders trying to implement AI without falling into the “busy work” trap.

Key Discussion Points & Insights

1. The Illusion of AI-Driven Efficiency

[00:00–03:08]

• Jonathan introduces the central dilemma: While AI promises to save time and reduce work, many find themselves simply taking on more tasks with no decrease in stress.
• "It's like a goldfish. You put it in a bigger bowl, it keeps getting bigger. So no matter how much efficiency we get, we still want to work those extra hours." (A, 01:03)
• Chad draws a parallel to manufacturing: Just as companies once “threw people at problems,” now they’re “throwing AI at problems” without understanding the underlying issue (02:01).

2. The Problem with More Options (and Tools)

[03:08–06:31]

• The hosts discuss “shiny object syndrome”: Adding tools is easy and tempting, but rarely does anyone stop to identify the specific problem a tool should solve, or its real business value.
• Jonathan compares it to buying every gadget in a workshop but mastering none, leading to shallow knowledge and subpar results.

3. The Need for Strategic Thinking (Start with 'Why')

[06:31–10:57]

• Chad emphasizes starting every engagement with a fundamental question: “Why does this business exist?” and “What, specifically, are we trying to accomplish?”
  • He introduces the concept of treating any new AI solution as a virtual team member—with a clear job description and measurable expectations.
• Memorable quote:
  • “A manufacturer wouldn’t just up and say, no market research, no direction, we're just going to start making something and hope someone buys it.” (B, 07:57)
• He describes the Deming Cycle (Plan-Do-Check-Act) as essential for AI adoption.

4. Chasing the Wrong Metrics

[10:57–17:45]

• Jonathan warns about the dangers of chasing “vanity metrics,” such as views or follower counts, that don’t translate into real business outcomes.
  • “I had a post go viral—400,000 views on LinkedIn... lowest ROI of any post I’ve ever done. For something to go viral, it has to appeal to the masses, which means it doesn’t appeal to your ideal customer.” (A, 11:34)
• He highlights how efficiency gains from AI are often undefined, self-reported, or not actually measured.

5. The Emotional Toll of Over-Adoption

[17:45–22:28]

• Both speakers describe how adopting too many tools at once can overwhelm teams, create frustration, and lead to negative self-talk—especially when the promised efficiency doesn’t materialize instantly.
• “Everyone else [online] seems to be adopting this stuff so fast—is there something wrong with me?” (A, 13:30)
• Chad notes, “What’s really going to eliminate your business is not AI—it’s bad scoping and weak process.” (B, 18:24)
• He shares a story about a utility company: Giving field teams new laptops without proper training just resulted in “bad data faster” (19:53).

6. Why Less is More (Niching Down & Focusing Effort)

[22:28–29:03]

• Quality beats quantity—especially in content and marketing. “Easy” AI tools can flood the market with generic material, but it rarely brings value or attracts ideal customers.
• Jonathan analogizes to writing books in too many genres or expanding into unrelated business lines—if your ideal customer wouldn’t buy them all, you’re running multiple businesses by accident.
• Chad urges leaders to write down goals and define what makes a good use of time before jumping into tool adoption.

7. Behavioral Change is the Real Challenge

[29:03–33:22]

• Chad notes that technological change is easy compared to human change. Even small process tweaks, like rearranging a car assembly order, can slow teams for months as they relearn habits.
• “If you can’t remember to hit record on a note-taking AI, you’re definitely going to struggle with bigger changes.” (A, 27:04)
• Leaders must consider the organization’s “change metabolism”—how much change can actually be absorbed and managed at one time.

8. Know Your Why and Document Your Goals

[33:22–37:27]

• Too many businesses skip the business planning process, especially solo founders or SMBs launching digital projects. This leads to confusion, lack of alignment, wasted money, and missed opportunities.
• “If you don’t know the result you want, then the formula doesn’t work out the gate.” (A, 40:16)
• Jonathan shares that businesses with clear, written goals almost always achieve more—because they track progress and can reverse-engineer what activities matter.

9. Lean Thinking Applied to AI

[37:27–41:35]

• Lean is about both continuous improvement and respect for people. Chad worries that rapid AI adoption sometimes “takes the human out of the loop,” risking both efficiency and employee engagement.
• “If you don’t have a process documented, you can’t improve it. Otherwise, you’re just blindly throwing AI darts everywhere.” (B, 38:07)
• AI should be used to amplify good processes, not bad ones. “AI is just an accelerant—if you have a bad process, you just get to do it faster.” (A, 39:14)

Notable Quotes & Memorable Moments

• On rushing into AI:
  • “We gave everyone in the field Toughbooks with very little training. And you know what we got? We got really crappy data a lot faster.” (B, 19:53)
  • “There’s nothing more wasteful than doing efficiently that which should not be done at all.” – referencing Peter Drucker (B, 20:21)
• On vanity metrics:
  • “I had a post go very viral last year... It had 400,000 views... and I got 12 followers from it. It was horrible. For something to go viral, it has to appeal to the masses, which means it doesn’t appeal to your ideal customer.” (A, 11:34)
• On the cost of change:
  • “Try changing a habit, right? Try changing someone else's habit—really hard... If you change the order in which you assembled the car, it would be such a big deal... That’s a lot of unprogramming to do when you want to change a behavior.” (A, 27:13)
• On measuring results:
  • “If you don’t know what success looks like for your team, it’s so hard to hit. ‘I’ll know it when I see it’—that means I have to keep guessing. No one is good at that.” (A, 34:31)
• On focus:
  • “If the same customer won’t buy all four of your products, then you have four different businesses.” (A, 40:34)

Timestamps for Important Segments

• 00:00–03:08: The promise vs. reality of AI-driven efficiency
• 06:31–07:57: The importance of starting with “Why” before adopting tools
• 10:57–13:30: Pitfalls of chasing vanity metrics; efficiency is not always measurable
• 17:45–22:28: Consequences of overwhelming teams with too many tools/processes; AI’s emotional toll
• 19:53: Story of digitizing field operations and failing faster
• 29:03–33:22: Behavioral change, change saturation, and the myth of effortless adoption
• 37:27–39:14: Lean thinking, respect for people, and AI’s role as an accelerant

Takeaways for Business Leaders

• Don’t deploy AI just because you can. Start with clear goals and know exactly what problem you are solving.
• Treat every AI tool as a new team member—define its job and how you’ll know it’s succeeding.
• Avoid “more for more’s sake”—more content, data, and automation isn’t always better. Quality and focus win.
• Measure what matters—have concrete metrics for success BEFORE deploying new tools.
• Anticipate and support the human side of change. The hardest part of digital transformation is often human, not technical.
• Document your processes and goals. If you can’t measure progress, you won’t know what needs improvement.
• AI amplifies your existing processes—make sure you’re not accelerating the wrong ones.

How to Reach Chad Bareither

• Website: bereithergroup.com
  • Free download of Chapter 1 of his book and contact info
• LinkedIn/Instagram: Bereither Group is active on these platforms for further engagement

Episode content summarized and quotes attributed to the speakers in line with timestamps. Ads, promos, and closing remarks excluded for clarity and focus on value-added discussion.