The challenger's guide to lean AI transformation - Part 1
Big companies can afford to waste money on AI and you can't; that constraint is your biggest advantage. In this piece, I explore how an ambitious underdog became a global powerhouse by refusing "the luxury of waste"... plus the eight wastes of AI transformation and the debt that must be paid to see an ROI from AI.
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It’s easy to forget that the biggest companies in the world were once scrappy, ambitious underdogs. Take Toyota in 1950 in post-war Japan, on the brink of bankruptcy and crippled by hyperinflation and labor strikes. To survive, Toyota needed to scale.
Eiji Toyoda, then Managing Director, arrived at Ford’s River Rouge complex in Dearborn, Michigan to learn how they churned out 7,000 cars a day compared to Toyota’s 40.
Eiji spent three months studying Ford’s best practices. What he saw was the luxury of waste: warehouses full of unused parts, workers standing idle, lines that kept running even when errors had infected the whole stream.
Ford’s strategy was simple: throw money at the problem until enough cars rolled out the other side. Eiji knew that Toyota couldn’t copy it and stay alive, so he returned to Japan and tasked its head of production, Taiichi Ohno, to find a new way to scale.
If necessity is the mother of invention, constraint is certainly the father. Ohno’s brainchild, the Toyota Production System (TPS), ruthlessly eliminated waste, streamlined processes, and built quality into the process at every single step.
The underdog eventually won. 25 years later, Toyota became the #1 imported car brand in America. 50 years later, Toyota toppled Ford as the market leader, which was now forced to copy Toyota’s way to survive. Oh how the mighty can fall…
As a challenger-brand exec today, you are standing on the same factory floor, only the technology has changed.
You don’t have an enterprise balance sheet to fund random AI experiments. You can’t write off mistakes like the rumored $500,000 bill that an ungoverned AI agent racked up at one business in a single day.
The most recent research on AI ROI points to a truth that Eiji Toyoda discovered 75 years ago: achieving company-wide ROI from AI comes from first stripping out complexity, not automating it.
This guide is the roadmap for the ambitious-underdog executives who refuse to accept the waste of the giants, and who choose to build a lean, high-velocity AI engine instead.
The 8 kinds of waste in AI transformation
Taiichi Ohno’s greatest contribution to manufacturing wasn't a tool; it was a way of seeing. After his own trip to Ford’s factory, he trained his managers to see the not-so-invisible tax bleeding factories dry: Muda, or waste. Ohno defined waste as any activity that eats up company resources without creating value that a customer wants to pay for.
He spotted seven distinct types of waste; later practitioners added an eighth. Today, nearly every business adopting AI is committing these exact same operational sins. But instead of wasting steel and rubber, they’re wasting capital, data, and human talent.
The 8 Wastes of AI Transformation
It all adds up to one debt
Every point of friction, delay, and ballooning cost in your organization stems from a single source: complexity debt.
Complexity debt is the pile of extra steps, handoffs, overlapping systems, and protected mini-kingdoms that no one ever volunteers to give up. Adding AI into this environment doesn’t fix anything; it just amplifies the mess.
88% of companies are using AI, but only 6% are seeing a meaningful impact on company-wide profits (McKinsey). The winners are paying down their complexity debt before adding AI. Research by BCG, PwC, McKinsey, and Deloitte reveals three habits shared by these market leaders:
They start with value. Instead of widespread experimentation, they make a few big bets on the biggest opportunities to improve growth and profitability, and they track the financial impact all the way to the P&L. (BCG, PWC)
They redesign the work end to end. They rethink how work moves across functional departments, removing friction first before adding AI to speed it up. Research by BCG, McKinsey, PwC, MIT Sloan and Deloitte all say this is the single biggest predictor of AI ROI.
They ditch the silos. Companies with horizontal models show measurably higher performance gains in speed, accuracy, visibility, and collaboration (PWC) because they’re organized to create value, not functional outputs. These leaders recognize every part of the business is mutually dependent on each other (Deloitte).
None of this is new, by the way. It's the sequence Toyota worked out when it couldn't afford Ford's waste: focus on value, map and streamline every step required to deliver that value, and delete what’s left over.
The Lean approach to AI transformation
The “lean” term was popularized in the 1990 book "The Machine That Changed the World" by Womack, Jones, and Roos who studied the Toyota Production System and translated its core ideas for Western audiences and other industries. Those audiences promptly missed the point.
TPS was designed to maximize value and respect human potential, but Western leaders saw Lean as a weapon to slash budgets, reduce inventory, and fire workers to boost short-term profits.
Lean also became a project with a start and end date instead of a mindset of continuous improvement. Without daily routines, a deeply rooted culture, and real executive buy-in, improvements vanished within months. It became "something we did once" rather than how the company functions.
Sound familiar? The companies using AI as an excuse to fire employees are now realizing their error and hiring them back (yet another example of waste!). And AI transformation isn’t something you do once; it’s a mindset and culture that constantly reinforces a new way of creating value – faster, smoother, and 10x more profitable.
In Part 2, I’ll cover how ambitious underdogs can use the 5 principles of lean AI transformation to leapfrog competitors and protect against AI natives.
Back to you
Take the Eight Wastes list to your mid-level managers and ask them how many they see -- even if you haven't adopted AI yet. This is the complexity to be cleaned up before you make investments, so you can see an ROI.
PS. The lean approach is exactly how AI natives work: they get laser focused on the value to be created, then build capabilities to own that value. Most companies lack that level of clarity -- let's solve yours fast. I'm running a small number of one-hour Value-First Strategy Sessions at super-discounted charter pricing. You get an AI-native-level clarity on where to place your bets; I get feedback and a testimonial if you are so moved. First one is free, second at $250, and each consecutive one will increase by 40% until I hit the ceiling (likely between $3k - $5k for emerging-market challengers). DM me for details.
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Sources:
McKinsey: State of AI in 2025: Agents, Innovation and Transformation
McKinsey: From adoption to impact: Three horizons of AI transformation
BCG: CEOs Are Starting to See Value from AI. Now Comes Execution.
Deloitte State of AI Report 2026
PwC’s 2026 Digital Trends in Operations Survey
