Why Lean Will Win Where BPI Failed (With Lessons for AI)

I recently came across one of the clearest explanations of why Business Process Reengineering (BPR) failed—and why Lean continues to succeed. It connected past mistakes to what many organizations are about to repeat with AI. [The Reengineering Is Coming  – Lean Enterprise Institute]

At its core, BPR didn’t fail because the problems weren’t real. It failed because leadership lost touch with the work and the people doing it. As Tyson Heaton notes in the article, “the people closest to the work know the nature of the work.” And yet, when organizations reached a breaking point, they didn’t turn to those people. They brought in outsiders, mapped processes that employees could have described years earlier, and applied radical solutions that often resulted in large-scale layoffs.

Those outcomes weren’t inevitable. They were the accumulated cost of years without continuous improvement. Over time, small inefficiencies compound: handoffs break down, workarounds become standard, and leaders begin managing from dashboards instead of understanding how work actually flows. Eventually, the gap becomes too large, and the only perceived option is dramatic intervention.

Lean offers a different path.

• Instead of waiting for a crisis, Lean starts with understanding. We go to the work, map value streams end-to-end, and involve the people doing the work in improving it.
• The changes are often incremental at first, but over time they fundamentally reshape how the organization operates.

What I’ve consistently seen is that when you stabilize and improve processes, you don’t eliminate people—you unlock them. Capacity that was spent chasing errors, fixing broken handoffs, and compensating for poor design gets redirected toward solving real problems. People evolve from task executors into problem solvers. That’s where Lean wins—not just in efficiency, but in capability building.

This becomes even more important as AI adoption accelerates.

The real divide emerging is not between companies that use AI and those that don’t. It’s between organizations that understand their value streams well enough to apply AI to real problems, and those that layer AI onto processes they haven’t examined in years. Without that foundation, AI simply accelerates existing dysfunction.

We are already seeing this play out. Many AI pilots promise transformation but deliver limited impact because the underlying processes are unclear or broken. It’s the same pattern we saw with earlier waves of technology—automating inefficient work instead of improving it.

This is why Lean is not optional in an AI-enabled future.

In the Lean AI work we are doing:

• We purposely start with real operational problems—cycle time delays, rework, unclear ownership—and then determine where AI can meaningfully enhance human capability.
• We are not applying AI broadly and hoping for results. We are applying it deliberately to problems we understand.
• By doing this, we avoid the conditions that led to BPR. We don’t let problems accumulate to the point where radical reengineering becomes necessary.

For leaders, this is the real takeaway.

Lean is not just a set of tools. It is a leadership orientation that requires staying connected to the work, developing people, and continuously improving the system. Organizations that operate this way don’t need periodic “transformations.” They evolve.

The reengineering wave may come again, driven this time by unmet expectations around AI. But organizations have a choice. They can wait for the gap to widen and accept the consequences—or they can do the harder work now.

That path is Lean.

Note: This blog does not reflect the views of my employer.