LNS Research

LNS Research LNS Research is the leading research and advisory firm to the worlds largest industrial companies.

LNS Research provides advisory and benchmarking services to help Line-of-Business, IT, and Industrial Automation executives make critical business and operational decisions. LNS research focuses on providing insights into the key business processes, metrics, and technologies adopted in industrial operations. LNS Research’s current coverage areas include: Enterprise Quality Management Software (EQM

S), Manufacturing Operations Management (MOM), Asset Performance Management (APM), Industrial Automation 2.0, and Sustainability.

The average tenure of a COO is now just a few years. At the front line, turnover has always been a challenge. But leader...
05/20/2026

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 analysi...
05/15/2026

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 s...
05/13/2026

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.

Ask most manufacturers what they want from industrial transformation, and the answer sounds reasonable enough: find what...
05/05/2026

Ask most manufacturers what they want from industrial transformation, and the answer sounds reasonable enough: find what works, standardize it, and then scale it across the network. Sounds clean, logical, and efficient, right?

The plants, however, have other ideas.

Of course, every facility carries its own history. The equipment was chosen at different times (some, quite long ago) by different leaders with different priorities, and it shows.

Some companies have tried to impose top-down standardization all the way to the equipment level; we have even seen some try to go remarkably far down that rabbit hole. But most find it becomes an endless project that never quite delivers and has to start over every time there's an acquisition or a technology refresh.

What we're seeing work better is an entirely different kind of standardization. Instead of trying to make the equipment itself uniform, you standardize how you interact with it. This builds the layer that lets different machines, systems, and data sources speak a common language to the business above them, without requiring everything underneath it to be identical.

Sure, it's less glamorous than a uniform factory floor, but it's also much more realistic.

The goal has never been about making every plant look the same. It's about being able to see, understand, and act across all of them with trust and transparency.

Making every plant identical and being able to operate across all of them aren't the same problem, and a lot of transformation programs run into trouble when they're treated as if they are.

Most manufacturers deploying AI are focused on what it can generate. That's the natural starting point, and it makes sen...
04/30/2026

Most manufacturers deploying AI are focused on what it can generate. That's the natural starting point, and it makes sense.

But the more interesting opportunity, at least in the industrial space, can often be found on the other side of that equation.

That might sound like a knock on the technology. It isn't. It's actually a pretty solid design principle.

When you use AI to generate something and then run a second system against it to pressure test the output, check it against your original requirements, and flag inconsistencies where they exist, something shifts.

The two systems working against each other tend to get you somewhere more reliable than either one working alone. Usually, the more you run it, the better it gets (provided you don't over-engineer it).

What we are seeing more and more of at LNS Research is that manufacturers who are getting the most traction out of AI aren't always the ones using it to generate answers.

Rather, they're the ones using it to interrogate answers, to ask whether something holds up before it moves forward in a workflow.

In an environment where the tolerance for error is low and the cost of getting it wrong is real, that's not a consolation prize. Most of the value we're seeing isn't in what AI generates. It's in what it catches.

There's a mismatch at the heart of most industrial AI deployments that doesn't get examined closely enough. Manufacturin...
04/22/2026

There's a mismatch at the heart of most industrial AI deployments that doesn't get examined closely enough.

Manufacturing is built around precision and repeatability. However, generative AI, by its nature, is probabilistic. That's not a flaw, it's just, it is what it is.

But those two things are not in the same neighborhood, and glossing over that gap is where a lot of projects get into trouble. It gets even more complicated when you factor in that manufacturing isn't just a single decision; it's a sequence of them.

Think about something as routine as a batch release process: an AI flags an anomaly in raw material testing, a second system assesses whether it affects downstream quality, and a third determines whether the batch meets spec for release. Each step depends on the one before it.

If each system is highly accurate but not perfect, those imperfections don't cancel each other out. They stack. By the end of a five or six-step sequence, your cumulative reliability might look very different from what it looked like at step one.

None of this means AI doesn't belong in manufacturing. It absolutely does, in some form or another. But deploying it without understanding where that risk lives is how you end up with outcomes that are hard to explain, and even harder to defend.

The real issue isn't can AI do this. It's where does probabilistic output create acceptable risk, and where does it create unacceptable risk. Those are two different things that require very different approaches.

Industrial data problems didn't start with AI, and they won't end with it either. Most manufacturers are sitting on year...
04/21/2026

Industrial data problems didn't start with AI, and they won't end with it either. Most manufacturers are sitting on years of operational data that never quite reaches the place where it could actually change a decision. AI raises the stakes on closing that gap considerably.

These old data problems persist, but now with new consequences. LNS Research's just-released 2026 Industrial AI Data Platforms Solution Selection Matrix is the follow-up to our Advanced Analytics edition. This time, we examine the data layer that determines whether your AI investments have anything real to stand on.

In the Data Platforms SSM, Analyst Vivek Murugesan evaluates eight vendors: Braincube, C3 AI, Cognite, Palantir, Quartic AI, Sight Machine, SymphonyAI, and TwinThread. Sure, they come from different backgrounds, have built different architectures, and are targeting different parts of the industrial market. But what they do have in common is a serious attempt at solving the same data connectivity, quality, and contextualization challenges manufacturers have wrestled with for years... now with considerably more capable tools at their disposal.

One of the key findings from our latest Industrial AI SSM research is that most mid-to-large manufacturers will need two to three types of platforms, not one. The assumption that a single vendor can solve the full industrial AI stack is still driving a lot of buying decisions, and it's still getting companies into trouble. Use the SSM to help you build and evaluate your vendor shortlist.

Moreover, it's time to rethink your data instincts and resist a common pitfall. Fixing your data before launching your analytics initiative sounds logical, but it isn't practical. If you wait for your data to be fully fixed, you'll be waiting forever. Data problems have to be solved alongside analytics work, not before it.

And when evaluating vendors, the Solution Selection Matrix flags something we hear often from operations leaders: time-to-value and the ability to deliver insights at scale matter considerably more than how elegant the underlying model is.

To read the LNS Research Solution Selection Matrix on Industrial AI: Data Platforms, click the link in the first comment.

There's an assumption baked into a lot of industrial AI thinking that autonomous is somehow the more advanced option; th...
04/15/2026

There's an assumption baked into a lot of industrial AI thinking that autonomous is somehow the more advanced option; that it's where you end up when you've really figured things out.

But it's worth pushing back on that thought. Because it actually gets the relationship backward.

Think about how most automation got built in the first place. Someone watched a skilled operator, documented what they did, and eventually codified it into a system. At some point, the rules became clear enough to hand it all off to a machine.

AI plays a different role.

What we're seeing at LNS Research is that AI tends to do some of its best work in situations where the rules aren't fully defined yet, and where there's still ambiguity to navigate. It helps organizations learn what's actually happening in a process, and that learning is genuinely valuable, often more valuable than the automation that follows it.

In that sense, AI isn't the destination. It's how you get to automation faster and with more confidence than you could before.

What this all means in practice is that reaching for AI because it feels like the modern answer can sometimes be the wrong call. What some of those situations truly need first is a better understood process and a more reliable system to run it.

So, bottom line, before you leap, it's best to know which one you're actually trying to solve.

We surveyed 300+ manufacturers about quality, and the results were a little uncomfortable.The data told us that most com...
04/14/2026

We surveyed 300+ manufacturers about quality, and the results were a little uncomfortable.

The data told us that most companies, despite clear advances in technology, people, and processes, still treat quality as something that takes place near the end. This generally looks like teams finding problems before product ships out.

But that's not really quality, is it? That's just inspection with a fancier org chart.

What we see at LNS Research is this: the manufacturers that are pulling ahead are the ones that have moved quality upstream. Their operational leaders have built it into product design and supplier conversations; into decisions that happen long before anyone touches a production line.

One Chief Quality Officer we spoke with said something that really resonated: "We stopped measuring defects. We started measuring decisions." Talk about reframing the conversation with a simple ah-ha moment.

If your quality team is still measured on things like reject rates and audit scores, you're measuring how well you're catching problems. But you aren't measuring whether the problem should have existed in the first place.

In his latest research, LNS Research Senior Analyst James Wells looks at five things the companies getting this right are doing differently. LNS Research members, you'll find the link in the first comment below.

A major pharmaceutical manufacturer we work with recently told us about a lesson they say took them a while to learn. We...
04/09/2026

A major pharmaceutical manufacturer we work with recently told us about a lesson they say took them a while to learn. We think it's important to share because it applies across manufacturing, especially in the current AI era.

When the manufacturer wanted to automate a highly manual production process, they prepped their workforce well for what was coming. On paper, they did everything right: they explained the technology, went over the timeline, and shared the rationale.

Despite their best efforts, psychological safety didn't improve. In fact, it got worse.
The problem wasn't the communication. It was that nobody in that workforce could see themselves in the future being described. They heard "we're building robots" and what landed was "my job is gone.“

So, they changed their approach.

Instead of more communication, they focused on putting people in the picture. Specifically, helping people understand not just what was changing, but where they fit in what was changing. When the message became "we're automating this, and here's the training that will help you do the work that comes next," the response drastically changed.

It sounds simple in retrospect, but it's actually easy to miss when you're focused on getting the detailed information out and the implementation. It can derail even your best efforts to accurately anticipate how well the message will land.

As AI continues to reshape manufacturing, the same dynamic is playing out on plant floors everywhere. The technology story is easy to tell; the harder part is making sure people can find themselves in it.

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