A recent LinkedIn post stopped me in my tracks:
“The irony of chasing 10–15% productivity gains through AI when teams are burning 50–80% of their capacity on low-value work and/or multi-tasking is ripe.”
AI’s potential is real, but without discipline it risks the fate of countless past “productivity initiatives”: early wins that fade, leaving inefficiencies untouched. Worse, as competitors adopt similar fixes, organizations risk a Red Queen’s race, running faster only to stay in place. Even more concerning, inefficiencies may be baked into AI systems themselves, embedding flawed processes into agents or creating new burdens for humans as they work around poorly designed solutions.
We’ve Been Here Before
A company has a productivity problem. They bring in an expert, the problem gets “fixed,” and leadership buys some time. But a few months later, the gains are hard to see.
Or the pattern takes another form: a new tool is adopted, a fresh program is rolled out, or a high-profile initiative is launched. Early results look promising, initial gains are visible, spirits are high. But months later, the shine wears off. The root issues remain, and the organization moves on to the next big thing, dragging its inefficiencies along.
Why? Because nothing else changed. The management system wasn’t strengthened. Leadership behaviors didn’t evolve. The organization’s thinking stayed the same. The capability to sustain, replicate, and extend the improvement was never built.
This is the cautionary tale of the flavor of the month and every half-baked lean “transformation.” And it is exactly why the Lean Transformation Framework (LTF) exists.
The Lean Transformation Framework is the product of decades of observation and practice across industries worldwide. It provides a simple but powerful architecture for building adaptive, learning organizations. By addressing five core dimensions: purpose, process, capability, management systems, and mindset, it ensures organizations lock in gains, develop people, and create resilience in the face of change.
The LTF ensures we address all the critical dimensions of change, not just “the work” or “the tools.”
The Opportunity: Augmenting, Not Replacing, Capability
Imagine you’re working with a team that has a clear training gap. Traditionally, you’d schedule a workshop, spend hours explaining Training within Industry (TWI), build a job breakdown sheet, and run practice sessions. Now, you can coach the training lead to use an AI assistant:
- Describe the situation and objectives.
- Dictate the job steps verbally.
- Ask the AI to produce a draft job breakdown sheet, with major steps, key points, and reasons for the key points tied to quality, safety, and efficiency.
In minutes, they have a usable artifact to refine. You’ve cut the time from “zero structure” to “something tangible to coach against” from hours to minutes. That is acceleration, not because the AI replaced you, but because it let you skip to the high-value part of the cycle: feedback, refinement, and practice.
Why the LTF Matters More in the Age of AI
When organizations integrate tools, lean or AI, without the full LTF lens, three patterns repeat:
- Capability without support: People know the tools, but the management system doesn’t sustain them.
- Tools without purpose: Improvements aren’t tied to a real business problem.
- Change without reflection: Thinking stays the same, so old habits reassert themselves.
AI can amplify all three mistakes. It can make it faster to generate “solutions” that don’t solve the right problem, or to implement changes that create local optimization while damaging the whole value stream.
The LTF questions force discipline:
1. What is the value-driven purpose? Or what problem are we trying to solve?
Define the specific problem, connect it to a clear value proposition for the customer, and ensure every improvement aligns with that purpose.
- Help: AI can analyze customer feedback, market signals, and operational data to sharpen understanding of the real problem to solve.
- Hazard: Without leadership context, AI may push teams toward trends or surface-level productivity gains that fail to create customer value. Purpose is a deeply human endeavor, and turning it over entirely to algorithms risks trapping organizations in an undifferentiated churn of following the averages.
2. How do we do and improve the actual work?
Look directly at processes and workflows, identifying and implementing improvements that support the value-driven purpose.
- Help: AI can quickly generate data summaries about errors, simulate workflows, and identify bottlenecks, giving teams a head start on improvement.
- Hazard: Overreliance on data and AI outputs can drive local optimization, improving a local challenge while damaging end-to-end flow. There is no substitute for going to the gemba: direct observation of the work provides critical context that AI will inevitably overlook. When leaders observe respectfully, they also build trust and open the door to partnered changes with those who do the work.
3. How do we develop the capabilities we need?
Build the skills, knowledge, and resources necessary to perform and improve the work effectively.
- Help: AI can accelerate learning cycles by providing on-demand explanations, practice scenarios, and personalized feedback to build skills faster.
- Hazard: If leaders mistake AI tools for capability themselves, they risk skipping the harder work of developing people who can think, adapt, and improve. Rapid individual acceleration can also create “capability islands” where people advance in isolation. Without structures to share and integrate learning, AI may fragment teams, especially in hybrid or remote environments, leaving individuals isolated rather than collaborating.
4. What management system and leadership behaviors are required?
Put in place the systems and leadership practices that enable and sustain the new way of working.
- Help: AI can support leaders by generating clear visualizations of performance, surfacing patterns, enhancing problem-solving, and helping manage standard work. It can even free leaders to spend more time developing people rather than chasing data.
- Hazard: If organizations assume AI can replace leadership by providing analysis, answers, and feedback, they risk reducing management to a purely transactional function. The role of servant leadership becomes even more critical in an AI-enabled workplace: knowing each person as an individual, building trust, and supporting growth cannot be delegated to algorithms.
5. What basic thinking, mindset, and assumptions drive this change?
Challenge and shift the fundamental beliefs about how work is done, moving toward a lean mindset focused on value, respect for people, and continuous improvement.
- Help: AI can capture and summarize conversations, highlight themes, and surface signals leaders might otherwise miss, providing useful input for deeper reflection.
- Hazard: Without deliberate dialogue, reflective listening, and feedback loops, organizations risk amnesia — quickly forgetting what was learned. AI may create the illusion of learning without the integrative shifts in thinking and behavior that sustain transformation.
If we can answer these questions continually and rigorously, we can integrate AI in a way that advances humanity, speed, and stability.
A Healthy Path to AI-Enabled Lean
From what we’ve seen so far, three principles stand out:
- Experiment in context: Dedicate individuals from each workgroup to experiment with AI tools. Give them a value-stream map and a clear value proposition so their tests are connected to end-to-end flow, not just local optimization.
- Use LTF to guide rollouts: Never integrate a major AI tool or process change into a primary workflow without answering the LTF questions. Treat it like any other major transformation.
- Build capability, not layoffs: Use freed-up capacity to create AI-enabled kaizen teams that move across the organization removing waste and improving workflows. Take your strongest problem-solvers from improved processes and give them bigger challenges.
The Risks We Can’t Ignore
AI will:
- Multiply the number of plausible solutions in circulation.
- Make it easy for anyone to generate “good-looking” answers without deep context.
- Shorten plan cycles, but the study and integration cycles remain just as hard.
The result? Without discipline, we will see more fragmentation, more rework, and more misaligned initiatives, faster than ever before.
The Takeaway
That LinkedIn post comes back to mind: “The irony of chasing 10–15% productivity gains through AI when their teams are burning 50–80% of their capacity on low-value work and/or multi-tasking is ripe.”
Chasing small productivity boosts without addressing the waste already in the system is a missed opportunity. Before you apply AI to your work, study the work, remove waste, build capability, remove dependencies. Then use AI to accelerate.
AI doesn’t change the fundamentals. The organizations that win with AI will be the ones that:
- Keep human capability at the center.
- Anchor every adoption in the value-driven purpose.
- Use the Lean Transformation Framework to integrate technical, managerial, and cultural change deliberately and reflectively.
We’re bullish on AI. We’re putting it in our coaches’ hands. We’re experimenting with prompt design, embedded learning tools, and AI-assisted problem-solving. But the goal isn’t to “do lean faster.” It’s to learn faster, reflect deeper, and sustain longer so our partner organizations don’t just survive the AI wave, but ride it to stronger, more human, more resilient systems.
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