Rockwell State of Smart Manufacturing Report 2026: What Executives Should Actually Believe [Review]

How's it going, everyone? Welcome to the next episode, in which we're going to look at the Rockwell Automation State of Smart Manufacturing Report. And if you're unfamiliar with industry reports, generally speaking, a lot of companies will either do them internally or hire out a company that will survey different individuals and ultimately put together some data and information that is relevant for the industry.

So you will see this from Rockwell, you will see this from Siemens. I had looked at a Copia report earlier this year, and ultimately, I am trying to, just as you should be, to understand what is happening in the industry, what you need to pay attention to, whether you're looking to advance your career, whether you're looking to augment your current manufacturing facility, whether you're looking to hire different individuals for specific roles to deploy some of these solutions.

And ultimately, these companies release these reports, so obviously they can reach out and sell you their own products, but also they're trying to educate the rest of the industry at-- as to what the trends are. So it's always interesting to analyze and at least reflect on some of this data. So today, I'm going to be giving you some of my opinions based on what I'm seeing in the industry, but also discussing some of the numbers that they are presenting, discussing some of the people they have interviewed based on my personal experience.

So if we switch over to the State of Smart Manufacturing Report, as you'll notice this is the eleventh annual report that Rockwell Automation has put out. This is accessible for free. I will have a link down in the description if you would like to download the report and obviously take a look at it yourself.

We're going to start off with a note from the chairman and CEO, Blake Moret. And here we have, "Leading in the execution era: Defining the new performance fundamentals. We are pleased to bring you the findings from our eleventh annual State of Smart Manufacturing research. This year's findings show increasing complexity as the market continues to introduce new challenges.

From geopolitical tensions to supply chain issues, from skilled worker shortages to rising costs, industrial companies are navigating uncertainty. What stands out in this year's results is not only the pressure we face, but the response. Leaders are no longer th-" Treating digital transformation as an initiative, but as in the operating system for the modern industrial enterprise.

Across eleven years of this report, we've watched transformation evolve from pilots to strategic priority. This year marks a shift from adoption to execution. We believe intelligent autonomous systems will fuel the future, driving operational improvements and empowering people to pursue greater innovation while creating more resiliency, agility, and sustainability.

Manufacturers aren't just adopting technology, they are mastering how they execute with it. They are building operations that anticipate conditions, automate decisions, and unlock real-time insight. Intelligence, autonomy, resilience, and adaptability are the new performance fundamentals. They're investing not only in AI and advanced technology, but in the secure interoperable environments that make intelligence actionable.

And they're elevating teams with tools that amplify judgment and accelerate learning. The execution era demands more than tools, it demands alignment across systems, teams, and decisions. It will require operations and systems designed to close the gap between data and decisions to prepare workforces for a new era of partnership between human insight and machine precision.

The data shows that fewer respondents were in pilot mode, and more respondents are now actively using smart manufacturing technology at scale. This proves integrated technology paired with empowered people isn't just an advantage, it's the defining characteristic of industry leadership. I hope this report inspires you to envision what is possible.

So obviously, this is a high-level message. There's going to be a lot of different keywords. Without supporting data, it is difficult to confirm or deny if this is actually happening. I think that we'll continue to scroll through to understand what is happening at the plant floor level based on this specific report.

That being said, there's a few key points that I do want to mention. So number one, there is a clear message of empowering humans with technology. There's going to be different viewpoints depending on whom you're having these conversations with, whether or not AI will completely replace the workforce versus is it going to augment some of those decisions.

And I am definitely the subscriber that AI will help in the decisions, but we still need humans in the loop that can get it to the repeatability and confidence that we need in the manufacturing space. So let's go to the second page. This is going to be our table of contents, introduction, insights, digital transformation imperative, defining the next industrial frontier, the path forward, demographics, and firmographics, page eighteen.

So the way I like to proceed with these reports is I always like to look at the demographics first, and the reason for that is that it gives me some context as to the data that's being presented and who that data was derived from. So for example, when we looked at the Copia report a little bit earlier this year for the two thousand and six period, we noticed that a lot of the people questioned were more IT leaning and more again, manager and director level.

In this case, let's click on this link to find out who were the respondents. And again, if you're not familiar with these reports, basically the Rockwell or the company that's helping Rockwell put this together will send out a survey to these individuals and then based on these response, responses will compile the data for the report.

And as we can see here, so number one, company location, thirteen percent is US, five percent is Canada. There's some misalignment in the numbers. I'm not a huge fan of that. There's already a very poor attention to detail, but that's a side note. We do have thirty percent Americas, forty-one percent European, and twenty-eight percent is Asia Pacific.

We have managers as the largest job role. We have then director, seventeen percent C-suite. We have sixteen percent department head, whatever that means, and then eight percent is going to be VP and SVP. Annual revenue. So we'll notice that it's naturally leaning towards smaller to medium manufacturers.

Again, it is difficult to obviously derive any conclusion, but it tells us that the respondents in very large companies, right? So again, maybe you've seen my profile. I have worked for P&G, I have worked for Kraft Heinz. It is very difficult to get any of these insights because a lot of it is proprietary information, so to get these individuals sometimes to answer these surveys is going to be relatively difficult.

It doesn't tell us how many people total have responded to the survey. Again might be relevant at least to see if this was two hundred people, two thousand people, or twenty thousand people, right? Because obviously that's going to give you a larger number of confidence. We have nineteen percent high tech, electronic, semiconductor, fifteen percent metals, ten percent auto, ten percent CPG, food and bev, home and personal care.

So this is the industry that I'm Very intimately familiar with. Obviously, manufacturing in a broader sense is not difficult from an automation standpoint, but I would say that a lot of my experience has been in CPG, food and bev, and closely related industries, life sciences and pharmaceuticals and medical devices as well.

We have some representation of oil and gas, chemical, mining, aerospace pulp and paper, water, wastewater, very small percentage. Surprising. So this is the breakdown of at least the report we are getting. How important that information is, again, it's very difficult to figure out, but at least we're looking at managers and directors predominantly, and we saw the breakdown of the industries.

Let's go to the first page of the report and it says, "More than fifteen hundred leaders..." So there we go. We have a number, and we see here that it's fifteen hundred and sixty. "... Contributed to the eleventh year of research for the State of Smart Manufacturing report this year. Sixty-two percent of survey respondents were decision-makers, an increase from last year.

Their responses reveal an industry operating under sustained pressure, where global risks are accelerating the urgency for transformation. Even among organizations not yet adopting smart manufacturing, seventy percent plan to invest in the next twelve months, signaling strong forward momentum. These are just a few of the important insights gathered through the feedback from these decision-makers from seventeen of the top manufacturing countries, more than half representing countries with over one billion in revenue."

All right, so we also see that this report is from Rockwell Automation in association with Sapio Research. So this is the company that conducted the research. I am not personally familiar with them, so I have nothing good or bad to say. " And plan to start your journey alongside the research findings to help you turn insights into action."

I'm assuming that this is a link to some of the products or services that Rockwell is selling.

Hi, my name is Vladimir Romanov. I am the founder of Joltech as well as Solis PLC. With a background in electrical engineering and an MBA, and over a decade of experience leading projects in manufacturing and industrial automation, I help engineers, managers, and manufacturers make smarter technical and business decisions, modernize their operations, and build stronger careers.

If you're serious about manufacturing, automation, and staying ahead in the industry, subscribe and join the community

" Executive Insights. Respondents enter two thousand and twenty-six amid sustained operational pressure. The findings in this report identify the factors most closely associated with improved performance and leadership outcomes.

Ninety percent of manufacturers say they need digital transformation to stay competitive." Again, so these are very high-level statements. Everyone wants to digitally transform. When you go to the actual plant floor, you will still see, I would be willing to bet whiteboards and paper That is used to track different operations.

Digital transformation is now a baseline requirement. Manufacturers are no longer debating transformation, they are executing it. The pace of industrial technology and rising operational complexity have turned digital transformation into a strategic condition for competitiveness. Thirty-four percent cite energy, workforce, and cyber as top challenges.

External pressure has become a multi-front battle. The industry is no longer facing one dominant threat, but a synchronized cluster of cost, labor, cybersecurity, and economic pressures. Leaders are responding by building operations that can absorb multiple disruptions at once. So this becomes an interesting percentage or number, right?

So again, we have seen some of the industries that are represented in this report. I would be, again, willing to bet that you can break this down further, and that's why I think information that is a little bit broken down would be more interesting. I can tell you, for example, with a lot more certainty that energy is a big factor for all the data centers that are being brought up.

And the reason why is because they have a huge cost and bills associated with using water, using electricity that is-- that has a very substantial impact on their operations. Versus I have run energy projects for food and bev and CPG, and for the most part, it is a very small percentage of their P&L or profit and loss statement, in which instance they're a little bit more careless, I would say, when it comes to their energy cost.

And it is not to say that they don't absolutely want to be more sustainable, it just means that I would, again, probably for them, put more emphasis on workforce and cybersecurity as opposed to the maybe data center companies and other industries would put a lot more emphasis on energy. But in any case, this is an interesting number.

I believe based on what I am seeing, the cybersecurity concerns are going to continue to rise. The OT space has been relatively slow to adopt technologies coming from the IT side, and thus there's a lot more threats as well as risks, which ultimately mean a higher premium for insurance to make sure that the facilities operate without any disruptions.

Thirty-four percent of operations are AI-augmented today, rising to fifty-four percent by thirty-thirty twenty-thirty. AI has crossed the tipping point from pilot to production. AI and machine learning now sit at the core of quality, cybersecurity, and optimization That the fight is shifting from who is using AI to who can scale it responsibly and reliably.

So again, I'm a little bit surprised by this number. I consider myself a power user of AI, leaning primarily towards Anthropic and Cloud Code as most, I would say, power users. That being said, when I have been to multiple facilities this year and last year, what I have noticed is that at best, they are using Microsoft Copilot, as many of us know, is just good at writing better emails.

So I would be extremely surprised that thirty-four percent of the respondents, and again maybe I just haven't spoken to all these people, are using AI for actual manufacturing, right? So for me, if your quality departments have access to Microsoft Copilot for writing a better email and being faster again, maybe at responding to some of those emails, that is not actually at the core of your departments, right?

Cybersecurity, again, there's quite a few tools in the manufacturing space that are using AI and are doing a better job, I would say, for threat detection. In this case, it's hard to say if this is operational. Is this purely IT, right? So if you've deployed a software from a known vendor that, for example, uses AI for gamification of cyber threat emails for your employees, is that really empowering manufacturing with AI and machine learning-enabled threat detection?

It's questionable. So in any case, forty-eight percent of manufacturers rank AI/ML as the top outcome driver. AI delivers the biggest business outcomes. When leaders assess which smart manufacturing features and capabilities drive the biggest business impact, AI and machine learning surpass every other capability.

Again, we haven't seen which other capabilities were maybe asked about on the survey. Obviously, AI and machine learning are top of mind, so everyone is talking about them. Forty-three percent of collected data is used effectively. This is... So I glanced a little bit at the report before starting to film this video, and this number is extremely surprising.

Forty-three percent of collected data is used effectively. I have been to many facilities, and for me, I would say that the amount of collected data, right? So we need to understand the bigger picture here, and I'm going to just shift to myself so I can basically gesture a little bit better. And I have walked through this on a whiteboard multiple times, but you have your field devices that generate data.

That could be your robotics. This could be your sensors. This could be even outputs, right? So this could be your actuators. This could be your valves. They create data at the field level. That data is then passed for control systems purpose into your PLC and DCS systems. These systems are going to primarily, again, run a state machine.

They're going to open valves. They're going to start processes. They're going to go through a sequence. They're going to monitor failures. They're going to initiate alarms. That data then needs to be extracted and stored. Usually, it's going to be a historian, but it could also be going through a scalar layer, which then, puts it in a database.

We're not going to spend a lot of time understand the basics, but at that point, I am assuming that the data is, quote-unquote, "collected" as they're implying in this specific report. So the data is, if it's historized, if it's in a SQL database, or if it's in a time-based database, it is going to be collected.

Now, used effectively is a very interesting way of stating this because used effectively can mean different things to different people. And of course, if you're just generating a report, for example, based on the data collected, to me, that does not mean it is used effectively. Simply creating a synthesized piece of information based on field information is not using that data effectively.

For me, the definition of being used effectively would generally mean that someone's looking at that data and someone is using that data to make the process or the business better as a whole, right? And what do we mean by better? It could be better productivity, better quality, but ultimately impacting the core OEE or overall effectiveness-- overall equipment effectiveness metrics.

So in other words, for me to say that collected data has been used effectively, number one, it needed to have been historized or put on a database, and number two, it needed to have actually had impact on the process. It is not sufficient to have that data stored. And for example, if you've worked in regulated industries, you know that there's going to be regulations around storing information for a specific period of time on your batching process.

That could be five years, seven years. It could be for quality purposes. So to me, that's not necessarily used effectively. Just because you're storing that data and it generates or can be accessed in three to five years, to me, that is not used effectively. But in any case, we're not going to argue about the semantics.

I'm simply surprised how large of a number this is. I have been in many facilities, and in most cases, the data basically dies at the PLC layer. So a lot of that information is used to control and actuate the process, but most of it is, first of all, not collected. But in this case, we're arguing about collected data used effectively.

What I have witnessed is that a lot of data is historized, but very little of it is actually used in a meaningful way, meaning that we do have reports, we do have access to the database, we even have dashboards, but how many of those dashboards are used to make the process better? Again, in my opinion, I was expecting to see maybe five percent, maybe seven percent based on what I have seen.

Organization continue to collect more data but lack the ability to use it effectively. Until this gap closes, AI and autonomy will underperform their potential. So this is a very interesting statement. So for me, in order for AI to use the data, it needs to have access to it. So in this case, we're giving a metric based on collected data.

So my question would be is why does this gap need to close versus the gap of actually getting the data that is not collected, right? So this is a bit of a, I would say, a strange sentence to write next to this information because the gap for me is collecting additional data as opposed to using the data effectively.

But again, it obviously depends on the situation. Twenty-eight percent of operating budgets are dedicated to industrial technology. Investment is no longer exploration, it's execution. Technology budgets reflect commitment. Organizations are no longer providing feasibility. They are modernizing at scale.

Again, it's very difficult, so twenty percent of operating budgets, I think this makes sense. I have not looked at many profit and loss statements for specific manufacturing companies. I would assume that this is in the right ballpark. We have here a statement that says, "Technology budgets reflect commitment.

Organizations are no longer providing feasibility. They are modernizing at scale." I would really like to see the previous year, or I guess maybe even the last five years' percentages based on operating budgets to conclude that, yes, there is growth in operational or technological spending, or no, there isn't necessarily a growth.

Number two is I know that there's a lot of companies that are investing billions of dollars into North American-based infrastructure, new facility, new production lines. So how much of this is actually digital transformation versus bringing in copies of same facilities, same lines, versus bringing in something new?

Forty percent of manufacturers reported their workforce was reskilled last year. The workforce is transforming in real-time. Smart manufacturing is rewriting roles, skill sets, and expectations. Reskilling is essential, not an aspirational initiative. Again I would like to see some more information on this side.

Sending someone to a training on Microsoft Copilot or a module, for example, on SAP is not reskilling to me, so it is very unclear what they mean by this, right? And of course, there's always different changes in roles. I spoke to some of my ex-colleagues from Procter & Gamble. Instead of calling it process controls and instrumentation systems, information systems engineering, they call it something else, right?

So there's going to be different titles, but ultimately the work remains the same. Forty-six percent experienced a cyber incident in the past year. Cyber risk is raising as operations become more connected. As manufacturers adopt more digital and connected technology, cyber risk continues to increase, particularly at the intersection of IT and OT.

Experiencing cyberattacks has become the new normal with almost half of respondents saying they experienced an incident in the last twelve months. So again, they haven't necessarily explained what kind of incidents, what constitute-- constitutes an incident. If I'm an employee and I download, for example, a malicious software from the the web and my laptop goes down and I need to go and reformat that Using some IT services, is that a cyber incident that we have experienced, right?

So what classifies a cyber incident for the context of this question is very ambiguous. Cyber risk continues to increase, particularly at the intersection of IT and OT. I really dislike the use of word particularly here because that is implied, right? So as we connect more devices, we're sending data from the manufacturing plant floors into the IT space, so obviously it will be at that intersection, right?

So the OT side historically has been, I would say, a lot less protected, the I-- than the IT side. But now that we need data, now that we need access, there's going to be different measures in place to make sure that's going to be secure. The Digital Transformation Imperative survey respondents have entered a new operating reality.

Nine out of ten organizations now said they need digital transformation to stay competitive in the face of rapid technological change. I would agree with that statement. Manufacturers are operating in an environment where rising costs labor constraints, market volatility, and supply chain fragility make transformation unavoi-- unavoidable.

For the second year in a row, nearly a third of operating budgets remain dedicated to adopting industrial technology. The data shows why respondents report operating under sustained volatility driven by cost, labor, cybersecurity, and supply chain pressures. Industrial and connected technologies have become core investments toward continuous execution force transformation.

Smart manufacturing is moving from pilots to production. This year, only eighteen percent of respondents said they were in a pilot phase with smart manufacturing technologies, while fifty-nine percent reported these tools are used actively to support operations. Manufacturers are targeting digital transformation efforts at measurable outcome, improving quality, reducing cost, reducing risk, and increasing OEE.

This is exactly what I've mentioned. I haven't seen the slide yet, but I am I would say happy to see that they are in alignment with what I've mentioned a little bit earlier. What do you see as the biggest external obstacles to your organization's growth over the next twelve months?

Select all that apply. Supply chain disruption, raw material volatility, inflation, economic instability, rising energy costs, cybersecurity risks workforce shortages and skill gaps. So personal assessment, right? Where would I expect these to fall? So cybersecurity, I believe this is going to continue to rise.

I, again, cannot emphasize this enough based on what I see in OT and IT organizations. I think that there's a ton of cybersecurity risk. There is a lot of capital that needs to be invested on the OT side to make sure that the infrastructure is correctly sending data into IT to then be leveraged for any of these transformations, tools, technologies.

Workforce shortages and skill gaps, again, I'm having a lot of conversations with many different plants that are struggling to find engineers, technicians, operators, people that can not only use what's currently out there, but also have ideas on how to make things better, improve them, and ultimately be really good at driving that change.

Rising energy costs, again, I think that this is a factor in very specific sectors. I have seen over the last couple of decades this always being a topic of conversation, but it has not received the attention it probably should have. Inflation, economic instability, I think that this is a given. Raw material volatility, again, there's obviously geopolitical situations that may increase this risk.

Supply chain disruption, same as we've seen during COVID. I think it's still top of mind. Quality, cost, and risk reduction remain core drivers. Across industries and regions, respondents report a consistent set of objectives driving digital transformation. Year over year, the primary outcomes org-organizations seek remain focused on improving quality, reducing costs, and reducing exposure to operational risk.

These priorities persist regardless of Industry segment, geography, or organizational maturity, indicating that the underlying reasons for transformation have remained stable. Top outcomes targeted by transformation efforts: improve quality, reduce cost, reduce risk. To achieve these outcomes, respondents are prioritizing investments that increase flexibility, intelligence, and resilience across operations.

So the question that they ask is: What is your organization's current status regarding investment in following industrial technologies? Percentages reflect those who reported they have already invested in those that plan to invest in the next year. Cobots, very surprised to see this, right? So we see a lot of cobots in trade shows.

We see a lot of different videos that are very exciting when it comes to cobots. Again, my general feel is that it's still very early on to be investing in cobots when it comes to manufacturing for actual operations. I still believe that the traditional robots make a lot more sense depending on the application.

But again, maybe for certain R&D environments, it could be interesting. AMRs, AGVs, sixty-nine percent, that makes sense. There's a lot automation happening. We're seeing a lot more tools from other companies that are much cheaper than they used to be. Digital twin simulation emulation, not extremely surprising, but I would say I thought that this number would be lower.

I do see companies try to simulate and create digital twins, but I would not call them as successful as maybe this percentage portrays. Robotics, seventy-four percent, this makes absolute sense. A lot of manufacturers greatly benefit from the ROI on the robotic side. Of course, if you're unable to find workers, some of these cells can be replaced.

AI and machine learning, eighty-three percent. So again, I think that this question is only answered with the selection criteria. I'm assuming it was a multiple-choice question, so everyone is going to say that, "Yes, we are investing in AI and machine learning and plan on investing in the next year."

Defining the next industrial fri-- frontier. A new operating model is taking shape across industrial operations built on intelligence, adaptability, and secure connected systems. These next five capabilities are the core enablers that will transform digital investment into autonomy, resilience, and sustained performance in the execution era.

Capability one, AI and automation are shaping intelligent operations from automation to self-optimizing systems. This is a very, I would say, bold claim and a very interesting... I hope that they define what this actually means. Artificial intelligence is rapidly advancing operational intelligence across manufacturing environments.

Respondents report widespread plans to use AI and machine learning to support core functions such as quality, cybersecurity, and process optimization, signaling a shift from automation as a fixed capability toward intelligence that augments day-to-day operations and enables more adaptive self-directing systems over time.

Again, this is, I would say, a high-level statement. Everyone is absolutely trying to use AI and machine learning in a very general sense. What I have seen, having talked to many different manufacturers and many companies in our space, is that most are still using the off-the-shelf tools, and we'll call them a AI and ML investment into our workforce.

I've mentioned this a couple of times earlier, but to me, writing better emails because you now have access or you have paid for your employees to have access to Microsoft Copilot is not advancing operational intelligence. The second comment I will make is that, again, we have seen a number based on how much data is utilized.

From what I have seen, there's still a ton of data to be collected and properly historized. And number two, a lot of the data that is collected and historized has yet to be utilized by AI in a way that makes sense. I would also separate AI and machine learning into almost two different categories. So both AI and machine learning, as I would say acronyms or AI/ML as acronyms, have existed for many years, if not decades.

What is the distinction today is that we have what I would call traditional machine learning algorithms that have advanced significantly, used primarily, for example, for anomaly detection, used for machine vision, right? And this is where you're going to see some of the showcases of, let's say, NVIDIA GPUs that can process images a lot better so that we can have pick-and-place robots work with different for example, items, as opposed to the li- large language models that are used, again, in this context, I would assume as AI, to analyze large amounts of data that humans in the past were not as good at analyzing and come up with basically different ways of using and leveraging that data for, again, in this case, operational intelligence.

But as they've mentioned a little bit earlier, it all boils down to bettering production, bettering the business, and improving OEE across the three metrics. The shift is measurable. One-third of operations today are AI-augmented, and respondents respond they expect this number to surpass fifty percent in the next four years.

How do you plan to drive positive business outcomes over the next five years? Select all that apply. AI/ML, increased automation, employee skill and training, securing IT and OT architecture, and better use of real-time data. So wh- what I do notice here is that these numbers are relatively close. So for me, it seems that there is no consensus that, for example, one bucket is significantly larger than another.

What I'm also not super fond of is that when we talk about increased automation, for me, that almost reads as increased automation as more productivity or just additional production line or is it increased automation as a result of using AI and machine learning, right? The same goes here, better use of real-time data.

Does that also coincide with increased automation and use of AI and ML to then better use real-time data? There, there's a bit of a, I would say, ambiguity from this answer. So I would want to almost if I were designing the survey, I would be building this as very different buckets. So I like this employee skills and training, right?

So AI can be used to build the skills and training, and of course, that can drive positive business outcomes. And so I would rephrase this question as to how do you plan to use AI to basically allocate the budgets to improve your operations? So just saying I'm gonna use AI/ML," doesn't make a lot of sense.

Increased automation, again, are we talking about some of the pre-engineering work? Are we talking about improving your systems integrators and the relationships with them, perhaps? So there's a lot of nuances when it comes to this thought. By twenty twenty-seven, fifty percent of business decisions will be augmented or automated by AI agents for decision intelligence.

By twenty twenty-seven, organizations that emphasize AI literacy for executives will achieve twenty percent higher financial performance compared with those that do not. I do believe that makes sense. This is from Gartner That's fine. Capability number two: operational intelligence is a competitive advantage, turning data into coordinated action.

So this is the comment that I made a little bit earlier. We are using now AI to create action. So this is actually usable data that is driving business impact. So here we're talking about survey respondents have more data than ever, but only a fraction becomes usable intelligence. In fact, forty-three percent of the collected data is used effectively.

The real competitive dividend isn't data collection, it's the ability to connect, contextualize, and act on data across systems. Yes that's exactly what I said. I hadn't even seen this page, but that is exactly what I was saying, so I do the fact that they maybe emphasized this point on this specific slide.

Operational intelligence is the architectural layer that mar- makes AI autonomy, resilience, and workforce enablement possible. The issue isn't data availability. I-- Again, I disagree with this. From what I've seen, data availability is absolutely an issue for many manufacturers. It is still very important to understand that when you walk the manufacturing floors, a lot of the data basically dies at the control system layer.

It's operash- operationalizing data consistently and at scale. When data flows reliably across operations, decisions accelerate, performance improves, and competitive advantage flows. Again, I would like to see some numbers as to what this actually looks like. But the question is: what do you see as the biggest internal obstacle to your organizational growth over the next twelve months?

And it says ability to effective execute capital projects to expand capability, capturing, understanding, interpreting, and using data, internal budget constraints. Once again, the numbers are so close that I would be reluctant to say that this was a conclusive answer, right? So budget constraints, obviously everyone would like to have access to more capital.

Capturing, understanding, interpreting, and using data, everyone would like to have access to more data. This is the tale as old as time. I think that now, more so than ever, once we capture the data, we can actually understand it better. Whether that lies on the capturing side, on the interpretation side, or usage side, this is where the nuance is, right?

And so I've talked a little bit earlier about this, but I want to reemphasize the point. There is a lot of data that is being generated by the field devices. That raw data needs to be then stored, right? So basically connect, collect Store and visualize. Those should be the core tenets for every organization.

Now, connecting is a question usually of technology. Which protocols are you using? Is that OPC? Is that MQTT? Which platforms you, you're using? Do you only have, for example, Rockwell, or do you have a melting pot of different technologies? You have Siemens, you have Beckhoff, you have Opto 22, right? So you need to solve a technical challenge.

Number two is collect, right? So collect, once again, those are going to be your protocols, but they're also going to be the software that resides on top of your control system to be able to retrieve that information. This is where some of the challenges on the cybersecurity come in. You're connecting now from the OT side into an IT environment, therefore the considerations are very different from some of those projects.

Then we have the store. Once again, is that going to a historian? Is that going to, to a timescale DB inside of the IT environments, inside of cloud? So it's now more of a technical and personnel store challenge. And the last, of course, is visualization, right? And so there's a discussion to be had about visualization for the sake of seeing the reports and visualization for the sake of driving change.

And those are becoming, I would say, AI-augmented, but more so still human-based challenges. You need people with vision. You need those that understand the current operations, but also have a desire to impact the future state to make the business better. So the investments or what do you see as the big- biggest internal obstacles could be all of those factors, but spread across the different segments depending on what the maturity of the organization is.

Let's move on to capability three, cyber risk rises with connectivity. Securing systems as operations become more connected. Survey results show as manufacturing operations become more digital and integrated, cyber exposure is increasing across IT systems, enterprise networks, and IT and OT integration points, the areas respondents most often identify as vulnerable.

These environments are where data control and intelligence converge as organizations scale autonomy and advance digital capabilities. Cyber incidents remain the clearest signal that resilience must be designed for scale, not just response. Forty-six percent of survey respondents report experiencing a cyber incident in the past year, reinforcing that security is no longer episodic.

It is an operational requirement as systems become more digitized, autonomous, and AI-supported. Resilience depends on visibility, secure and trusted architecture, and the ability to recover quickly when disruption occurs. As manufacturers scale AI closed-loop control, advanced analytics, and autonomous workflows, one principle becomes clear: security is the prerequisite for autonomy.

Without a secure, integrated foundation across the IT and OT environments, advanced capabilities cannot scale safely, and autonomous systems cannot operate with confidence. So one of the big factors and I've had this discussion with multiple VPs of larger organizations, is not only the fact that they are concerned with cyber threats.

What is happening right now is we now have regulations that have been applied in the EU, European Union, as far as 2025, that regulate and basically penalize companies that go down for a significant period of time in specific verticals. What that means is if you're a large food supplier for a specific region and you get a ransomware attack that brings down your facility for several weeks, that's going to have a significant impact on the population, which basically means that you're now more liable for making sure that everything operates as expected, and thus the insurance premium against the cyber attack that may happen have been rising.

The same is going to be applied in North America in 2027. Again, I have not checked if there's been any changes on that front, but I believe that is still when it is expected. So going back to my point, when I'm talking to vice presidents and directors and even CISOs On the manufacturing side, the message is very clear.

It is not just not being or protecting against cyberattacks, it is also the fact that proactively a lot of the insurance premiums have been increasing. Therefore, to reduce that premium, they need to have a posture that makes sense. One simple example is if they still have Windows XP machines on the plant floor, or they have PLC 5s or SLX 500s, right?

So old automation equipment that is no longer patched, no longer supported, no longer or is not placed behind firewalls, then it becomes a problem, and thus they are going to pay more. So in this case, we have where do you believe the organization is most vulnerable to a cyber incident today? And it says the integration points between an IT and OT are most vulnerable to cyber incidents just behind IT systems enterprise networks.

So again, depending on the architecture, we're not going to get into a highly technical conversation here. I have certainly seen a lot of dubious, I would say IT/OT DMZs, OT networks, and firewall implementations. This is a much more technical discussion. The-- My general consensus at this stage is that the OT folks are mainly concerned with making sure that production runs, and IT folks are generally concerned with cybersecurity and threat protection.

That being said, the moment that they step over each other, meaning that IT wants to stop operations so that they can provide more cybersecurity, they immediately get yelled at. And conversely, when OT is unable to reestablish the production of the line because either a patch needs to go through, they immediately transfer blame onto the opposite organization.

So these are very difficult conversations. I have seen IT directors get fired for basically not understanding the requirements of OT and thus disrupting what ultimately pays for the salaries of most individuals in manufacturing, and that is product out the door. Capability for the workforce is being redefined, equipping people with a more adaptive, intelligent future.

Manufacturers are actively reshaping roles, reskilling talent, and using AI to augment, not replace their people. Transformation is already underway, with ninety-three percent of respondents expecting to reshape their workforce as smart manufacturing advances. This is not incremental change, it is structural move toward dynamic adapt-adaptable roles.

Reskilling is now a core capability. Organizations are investing in people to keep pace with technology. Technology drivers efficiency, but people drive outcomes. The organizations that succeed will be those that invest as much in workforce transformation as they do in digital capabilities. I'm a huge supporter of investing in your people.

I believe that people that not only go to these trainings but also get new ideas and novel ways on how they can apply some of these technologies are going to be some of the best advocates for your business in all senses of that word. Now, what I'm a little bit concerned with having done a variety of different projects, is that there is a lot of discussion of, let's say, sending individuals to a training that's going to apply to, quote-unquote, " operational systems of the future."

When I walk into plants, I after-- I often see and hear comments about the fact that they're still running PLCs and HMIs from the nineteen eighties. I had to run a DOS emulator as as far back as a couple of months to be able to connect to a system that is basically running... So it's a DOS emulator inside of a Windows NT machine that connects to an HMI system.

So if you're going to train your people to use the technology of the future but then not invest into digital transformation, they will simply-- well, number one, they'll probably go somewhere else to leverage some of their skills because they will get poached by a competitor. But ultimately, you need to do both.

You need to empower people with the training, but you also need to give them the right tools and access to the right capital so that they could implement the changes they have learned within these training modules. By increasing your use of smart manufacturing technology in the next twelve months, which best describes changes to your workforce?

Repurpose existing work- workers, hire for different roles, hire for existing roles, outsource. Again, are we going to see the same level of firing waves as we've seen in software? Probably not. So I would assume a lot of people that have deep OT expertise and knowledge will simply have to use that to learn some of the other tools maybe on the IT side, OT networking, so on and so forth.

Capability five, competitive differentiation. Capabilities that distinguish higher performing organizations. Performance is no longer determined by who has the technology. By the way, I haven't made a comment about this earlier, right? But the em dashes very clearly indicate And again, I could be wrong, there's-- there hasn't been an excessive use of em dashes, but there has been at least a couple that I've seen.

But this basically signals that AI was used to write this report. Again, I don't know how much Rockwell paid for putting together these slides, but ultimately, it's possible that they just collected all the data from the survey, asked AI to reflect what has been inside of the dataset, and it came up with these insights.

"Performance is no longer determined by who has technology. Nearly everybody does." I again am very skeptical of that comment. " Respondents report a number of factors are all necessary in conjunction to achieve a competitive advantage. Technology, a skilled workforce, innovation, speed, and product quality were reported as top challenges to outsourcing competition in the next year, underlying the importance of alignment across strategy, people, and platforms.

In every region, respondents reported adopting and using AI as their number one strategy to manage external obstacles to success. Beyond that, the regions have different priorities that reflect distinct regional pressures." Again, this is a very interesting comment, AI as their number one strategy to manage external obstacles.

That is, I would say, number one, bold, but also surprising. How is your organization mitigating external risks? North America, hiring new or different types of talent or upskilling existing talent, consulting with experts on regulatory compliance issues. Latin America, evaluating critical suppliers.

Europe, Middle East, and East and Africa, we have increasing research and development innovation, managing cybersecurity risks with security controls and countermeasures. Asia-Pacific, digitizing business through technology adoption and automation. So interesting insights. Of course, they're slightly different.

I don't know if I would expect it-- them to be different. I do a lot of work in North America, a little bit in Europe, some in Middle East, not a whole lot in Africa. Again, interesting perspectives besides that. Obviously, we have a problem with hiring new or different talent in North America, and then there's regulations, I've talked a little bit on the se-cybersecurity side, that is going to be important.

The path forward: insight to execution. Eight steps to drive value and achieve success. Prove value versus technology. Technology works fine and prioritize specific digital use cases that solve manufacturing and operational issues. I would say yes. Again, it is still very difficult for systems integrators I believe that we have made tremendous advancements.

I wouldn't call that technology is very easy to implement. There's still a lot of money and a lot of time needed to deploy systems on a capable basis in manufacturing. Investments with a short-term payback. Transformation stalls when ROI is slow. Build rapid, steady flow of value to drive adoption and self-funding.

Again, everybody wants faster ROI. Can you shorten the time to execute? Yes, it depends, right? We've seen a lot of innovation even on the software side where you have different modules. So you can basically pick a specific set or a specific module to integrate and then drive value and then integrate something else, right?

So you're demonstrating incremental value as opposed to needing to, for example, take seven years to integrate SAP before you've switched over to that new ERP system. Plan for scalability to deliver desired outcomes at scale. Plan for optimal set of technologies with integration to existing backbone.

Focus on common work process across the enterprise. Again, this is nothing new. So I'm surprised by some of these statements, but it's just, eight steps. Foster enterprise collaboration. Siloed solutions are a dead end. Enterprise OT and IT digital connectivity and collaboration unlock exponential value.

This is a very bold statement. I have been in manufacturing for many years, and I can tell you that collaboration is very difficult, barring impossible to achieve. What I have seen mostly is that one or the other will dominate depending on the cycles, depending on the projects, depending on the budgets.

And so I don't think that there is a drive to collaboration. There is basically drive to mutual understanding and coexistence is how I would best frame it. Learn, iterate, and improve. Long-term planning helps, but flexibility can mean missed opportunities. Keep an eye out on your digital vision while learning and adjusting your strategy and execution to build on proven value as it emerges.

Communicate progress and success. Momentum matters. Spread the word beyond impacted group to build and maintain excitement for what's possible. This is a, I would say, a very understated but very important message. And the reason why I want to stay on this for a couple of minutes. As I grow in my career, I believe that Success, but also enthusiasm for certain projects and initiatives is sometimes even more important than the initiative itself.

And what I mean by that is that you will have individuals inside of your organizations that are truly driven, that want to solve the problem. They obviously there's going to be some guardrails within an organization. You cannot be reckless. You cannot recommend something that doesn't make sense. But there is that enthusiasm in certain individuals that wants to make the business better simply because it is in their nature, it is in their blood, whether that's because they are a savvy engineer, a contributor, it could be someone on the quality side.

And the moment the organization creates more barriers in front of them, they lose that drive, right? What I mean by that is if you're looking to get data out and you send your employees on the digital transformation team to a conference where they learn about different solutions, right? It could be on collecting, it could be on connecting existing equipment, it could be deploying new automation tools.

They come back, they create a business case, which again needs to make sense, but they create a reasonable business case of how they believe they can have an, a positive impact on the organization. And once they start having those conversations, they are met with roadblocks, meetings with IT pushback from quality departments, a lot of paperwork to get approval on what seems to be a small initiative.

They will immediately just fall into the ranks and become this slow-moving department that isn't allowed to make changes. And that's why momentum matters. Spread the word beyond the impacted group to build and maintain excitement for what's possible. Very important message. I like the fact that they've put it on this report because I see way too many organizations, again, we've looked at this data before.

They will send the individuals for training. They will give them the tools. And then when it comes to the moment of actually doing the work and delivering the value for the business, the excitement is going to be killed within the organization because they are unable to build the momentum and create the excitement for what's possible.

Define and apply governments, governance. Protect sustained value. Embrace new ways of working. Include adherence to process and data standards. There's going to be more and more of these conversations, but of course extremely important. Number eight, equip and champion people. To get ROI from digital, empower people beyond introduction of new technology.

Skills and mindsets that support new ways of working are key to success in driving self-service. A huge believer in that. I really like that message. Learn more about our respondents. We already talked through this, and of course, you can look at more of the Rockwell Automation tools based on this last slide.

Thank you so much, everyone. If you have any comments, if you have any questions, if you believe that some of my takes are incorrect and you are-- you have a different opinion, definitely would appreciate a comment. Let me know what you think. Obviously, feel free to download the report. Let me know what your thoughts are, and I will see you next time.