In this episode of Great Question: A Manufacturing Podcast, ARC Advisory Group research lead Inderpreet Shoker brings the data and follows up her recent three-part Smart Industry series on connected worker platforms with more advice on making the most of these promising solutions that involve many variables to succeed in smart factories.

Below is an excerpt from the podcast:

Scott Achelpohl: Hello everyone and welcome to another episode of Great Question: Aa Manufacturing Podcast, this one by Smart Industry. I'm Scott Achelpohl, SI's Head of Content, and I'm joined for this episode by Inderpreet Shoker, who is our colleague in the manufacturing technology sector and is research director at Boston area based ARC Advisory Group. ARC is a leading technology research and advisory firm that focuses on industry, manufacturing, energy, and infrastructure. It specializes in tracking developments in exactly Smart Industry's verticals of coverage, manufacturing OT, IT, and engineering technology. So we'll say hello to Preet and thank her for coming on the program.

Inderpreet Shoker: Thank you, Scott. Thank you so much for having me. You know, the topic of digital worker or connected worker, it's very near and dear to my heart because this is a rapidly changing technology space and it's a relatively new space in the industrial sector as well. So a lot of exciting things are happening here, and it's absolutely my pleasure to be here and share and discuss this technology sector with you.

SA: Before I ask my first question, I will summarize the first of your articles, which appeared on September 17 on Smart Industry. You talked about the increasing complexity of the job for new age manufacturing workers, and you pointed out that not just the work is more complex, but where workers get their knowledge is frankly kind of all over the place. Different systems, paper binders, spreadsheets, etcetera. However, you also pointed out that connected worker solutions offer ways to digitize this knowledge and link the assets and systems employees need to do their jobs. 

This is what I'll ask, however. Do these solutions come straight from the vendor with these abilities? If not, how much of the customization is up to the manufacturers, the organizations, the utilities, IT, or OT department to optimize these connected solutions so they are worth the price an organization pays for them?

IS: That's an interesting question, Scott. I really wish it was the case that you would just buy something and it magically works. And our end users would love that as well. The solution providers would love that. But sadly, that's not the case. And I think we try to emphasize it in our articles as well, because end users need to have their expectations straight. A lot of the time, you know, we can expect a lot and then with that expectation, if we don't put the hard work in that is needed to make a tool a solution, we are not going to see the results. So vendors, you know, in this space, they provide the tools. These tools focus on various different capabilities: digital worker instructions, knowledge capture, collaboration. 

But these are just tools. To make these tools successful, to make these tools a solution that are adopted in your organization, definitely a lot of customization is needed. Because these tools, they don't know how your plant runs, how the different equipment runs, what are the different processes. There are templates that a lot of these solution providers do provide with the solution, and those are helpful. But then of course, a lot of work goes in by the IT department, by the OT department. And whenever we talk to the end users, a lot of times what we hear are some of the common things, that IT will usually take care of the architecture, cybersecurity, role-based access, devices, and so on. And OT, they are the people who bring in the knowledge expertise, I would say, because they know how the plant is run, what are the different procedures. They bring the operating context, they bring the safety needs. And then these two departments work together to customize the solution for the plant needs. 

And Scott, interestingly, you mentioned is it worth the price. Definitely all the effort that goes in, it's worth the investment. But yes, the goal should be to give a worker the right instruction, right context, at the right time, so definitely a combination of technology as well as workflow design as well as integration is needed.

SA: There's never an out-of-the-box solution. It always has to be customized to anything that any particular manufacturer is doing. They don't all do it the same way, even plants that are part of the same company.

IS: It depends upon the application as well. Certain connected worker applications, say collaboration, remote collaboration, pretty much like how we do audio calls or video calls, it's very similar. So in some cases, you may use that tool, the solution with very little integration, with very little customization, but most of the time when you go into more complicated applications such as, you know, training applications or digital work instruction, then you need to put in more effort into the customizations, the integrations and so on.

SA: Listen, as a follow up, what prep would you recommend an organization do before asking their employees to interface (there's an IT word for you) with these solutions. And here's another word for you. We talk a lot about data. What kind of shape does their data need to be in?

IS: Scott, there is one thing that I always talk about whenever it comes to, you know, getting ready for any kind of technology adoption and not just connected worker technologies. I think a lot of the times end users are looking at a technology and then take that as a first step. I think the first step should be to start off with a problem. Look where are the issues in your plant or in your facility, what are these problems and then dissect these problems and see what are the technologies that are out there that can help you solve these problems? So I think instead of thinking of technology first, first thing I think we should do is that think of the problem first. 

And it's also important to work with the frontline workers, with the plant workers, with the operators, with technicians, supervisors, subject matter experts, because these are the people who know where the problems exist. What are the processes that slow down the whole plant? What are the variations from shift to shift? And what are the areas where we rely too much on one or two people for their expertise? So these are the different things that we should think before we think of technology. 

And another thing I often hear from end users is that they think of the problem, they bring in the technology, and then they never paid attention to change management or training. These are some of the areas we a lot of times forget about. So start focusing on these beforehand as well. Don't think if you have a successful pilot when you are scaling and after that, we'll think of training and change and think of these things beforehand. 

And Scott, you mentioned about data. That's also another question we often get from end users that, okay, I want to start with this technology investment, but my data is not ready. And a lot of time what we hear from forward-looking end users who have been successful with technology implementation is that there is never a good time to start with the technology thinking that my data is ready now. There'll probably never be a time where your data is 100% ready. And the good thing about these solutions is that you can start off even when your data is not perfect. But yes, definitely do pay attention to some of the basics. So make sure that your procedures are current, make sure that your data is clean, you are consistent with asset names, asset tags, wherever the information needs to be linked, it's linked to the right sources. Clean up the duplication. So these are some of the basic efforts that I think definitely companies should pay attention. 

But if I can add on to this, Scott, I would definitely like to highlight the greater emphasis on AI adoption now. And if your company is also looking at AI and how you can adopt AI into various different applications for various different use cases, then I think we need to pay more attention to the data part of it, not just clean integrated data. The data has to be contextualized because AI needs to understand if, say, one equipment is faulty, something is off with that, we need to understand what are the process conditions, we need to understand what happened in the past and what were the solution at that time. So context has to be there to get meaningful results out of AI. So definitely, get the basics right and if you're thinking of the future of adopting AI, then start cleaning your data now.

SA: That pivots perfectly into part two of your series. And we welcome that part two, because it talked all about AI. I would say in your second part, by the way, that appeared on Smart Industry on September 25, you delved into the AI layer, what you call the AI layer of connected worker solutions, how AI can build on their capabilities. I think you said a lot of it, standardizing procedures, capturing task data, connecting workers to experts, interpreting operational context, and overall helping workers to make better decisions. That's verbatim from your story. But I'll ask this, Preet, how can connected worker solutions utilize AI to go beyond chatbots for the worker standing at a at a plant workstation, for example. Just tell us what more is there than AI dashboards?

IS: You're right, Scott. A lot can be done with AI. And I think chatbots are becoming very, very common. So that's actually a good thing that end users are having experience with chatbots and also realizing how useful these can be. But again, having just an interface to do your searches is one thing, but to get meaningful results, I think it depends upon how strong is your data foundation, how well you have contextualized your data. But the goal should be that we want to give our workers right information at the right time, answers that they can trust. So definitely there's an opportunity for chatbots to become more than just chatbots, to be intelligent assistant to the workers out if they're in the field in the plant. That's definitely improving, a lot of end users are focusing in that area and as well as a lot of solution providers are trying to improve the quality of the chatbots. So that's an area where there's ongoing improvement. 

Another area I think where AI is proving to be very helpful is to help capture worker expertise. Some tools, you know, and connected with worker AI have been used for a long time now. You know, we see a lot of capabilities around computer vision, voice tool that captures if a worker is saving any data using voice tools, which is very helpful in the field because a lot of time workers are using both of their hands. So to fill forms, to input any information, voice tools are very helpful. Computer vision is very helpful to record equipment conditions, what is going on, to record procedures, processes as well. But what now AI has done, primarily generative AI, is converting all this information that is in your audio form or your video form to actually convert that into knowledge. So creating procedures, creating training sessions is much more easier than how it used to be in the past. 

Creating content, that is another area where things have improved tremendously. I have been covering this area from the last 10 years or so. And 10 years ago when I would do a briefing with a solution provider and they would highlight how training sessions are created or digital work instructions are created, those things were very helpful. But even though you had drag and drop kind of tools, a lot of tools that would help you create content, it was still a lot of manual effort where you are typing instructions, where you're bringing instructions into the picture, you're creating your program. It used to take a long time for end users to get started on the digital worker instruction journey just because of that. But now with AI, one can easily scan, take a picture of instructions, take a picture of checklist, record a procedure, and it is automatically created into a training session or digital instructions. So I think the adoption is becoming much more easier. Getting started on this journey is becoming much more easier because of the AI tools. 

And another good thing about AI is that now technology providers are embedding it into their existing solutions. So it's not like end users have to learn something completely new. It's already there within the existing solution that you already have in your plant. And you know, Scott, I think it's clear from end users as well that they want something just beyond AI chat bots. I think we did highlight this in our article as well that one of the ARC surveys that we did last year around maintenance productivity, a majority of the end users highlighted that guided repairs with checklist, safety steps is the most valuable AI-driven capability that they feel. So definitely I think end users want something more out of AI now. And if I take another minute, I think we need to start getting ready for agentic AI as well.