LNS Research

The average tenure of a COO is now just a few years. At the front line, turnover has always been a challenge. But leadership turnover at the top is a different kind of problem, and it's one that doesn't get nearly enough attention in conversations about organizational culture.

A company's culture takes time to build. It builds through consistency, through leaders who show up the same way long enough for people to really trust what they're seeing.

This seems especially true at manufacturers with multiple locations, varied legacy systems, and years of "we've always done it this way." The problem is that most organizations are cycling through senior leadership every two or three years.

Typically, each new leader comes in with a new strategy, a new way of doing things, and a new set of priorities. As such, the culture underneath never gets the chance to set. You end up, as one of our research partners put it recently, forever in building mode.

So, the question becomes: how do you change the leader without changing the culture?

Some organizations have tried to solve it by defining specific value and behavior sets, and elevating people who genuinely align to them.

Johnson & Johnson, one of our World's Most Productive Companies, is probably the most cited example of this done well. Their credo, written in 1943, is still guiding leadership behavior today. But it requires enormous discipline to sustain, especially when the pressure to bring in someone who will shake things up is high.

Our research keeps pointing to the consistency gap of leadership changes as one of the most underappreciated drivers of cultural dysfunction in manufacturing organizations. The front line feels it every time.

Most operations leaders, if you asked them, would probably estimate their data teams spend most of their time on analysis. That feels right, given what their functional name implies. But our latest research says otherwise: 70 to 80% of the data team's time goes to cleaning and preparing data just to make it usable.

Few industrial organizations have named this as a problem yet. They may have an AI initiative or a data initiative. Sometimes they have both, but usually run by different teams. And this data foundation problem sits somewhere underneath all of it, somewhat unnoticed and definitely unresolved.

What we're finding is that the Leaders have gotten ahead of this. Among the top 18% of companies actually scaling Industrial AI with measurable results, 62% have actively implemented Industrial DataOps practices. Among Followers, that number drops to just 35%.

Digging deeper still, at the bottom of the maturity curve, we find that Leaders have data governance on the roadmap almost without exception. Conversely, a significant portion of Followers still don't.

The industry has been down a version of this road before. "Data is the new oil" sent manufacturers into a decade of cloud migrations, data lake build-outs, and aggressive collection initiatives. What came out the other side was petabytes of ungoverned raw data, technology solutions searching for problems, and pilots that never made it into production.

Nobody pulled off what Toyota did with TPS; nobody pulled off what Motorola did with Six Sigma. The data just kinda sat there.

LNS Research Analyst Vivek Murugesan's latest research makes the case that we're at a different moment now: capital moving in, product roadmaps maturing, and a vendor landscape that has moved considerably in just the past few months.

If your AI and data strategies are still two separate conversations, you'll want to check out Vivek's full blog, linked in the first comment below.

One of the more persistent myths in industrial transformation is that if you get one plant right, the rest will follow suit. Master the model... export the blueprint... scale across the network. It all sounds so logical. In reality, things seldom work that way.

What we constantly hear from operators is that every plant is different in ways that matter. The equipment is different. The legacy processes are different. The leadership is different.

Moreover, and perhaps most importantly, the culture is different. A framework that worked in one facility doesn't transfer cleanly to the next, no matter how good the design.

This isn't an argument against having a framework; quite the contrary, in fact. What this really is is an argument against mistaking the framework for the work.

The companies that scale successfully tend to be the ones that treat each plant as its own transformation, informed by what came before but not dictated by it. They take lessons learned and adapt them, rather than rinse and repeat.

There's a useful parallel in how lean manufacturing spread through industry. The companies that adopted TPS wholesale and called it done generally didn't get the results that Toyota did. The ones that took the principles, internalized them, and built their own version of them did, however.

This same dynamic is now playing out with industrial transformation and AI.

There's also a universal truth when it comes to replication: you can't make it your own if you're too busy copying the original.