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How AI and the Augmented Connected Workforce is Revolutionizing the Packaging Industry

Join Chris Kuntz for an interview Packaging Insights on how AI and connected worker technology can help the packaging industry overcome the skilled labor crisis.

The packaging industry has been hit by the low availability of skilled workers, but for Chris Kuntz, VP of Strategic Operations at Augmentir, AI systems offer the solution. In this interview with Joshua Poole from Packaging Insights, Chris explores how AI and the Augmented Connected Workforce could revolutionize the packaging industry and how Augmentir’s AI-powered connected worker solution supports optimal efficiencies in manufacturing. He also discusses the importance of effective regulatory frameworks for AI.

This transcript has been edited for clarity and length. View the original video interview on the Packaging Insights website here.

packaging industry connected workforce

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Joshua Poole: Hello, everyone. My name is Joshua Poole, and I am the editorial team leader at CNS Media, the publisher of Packaging Insights. I am very pleased to be joined today by Chris Kuntz, who is the Vice President of Strategy at Augmentir, and who is here to talk about the benefits of AI in relation to the packaging industry.

So welcome to you, Chris.

Chris Kuntz: Thank you very much, and thanks for having me, Joshua.

Joshua Poole: So, Chris, AI systems are expected to really transform the wider society but in relation to the packaging industry, to what extent could they revolutionize operations there?

Chris Kuntz: The reality is, to a huge extent. The impact centers around the manufacturing workforce – the people that are part of manufacturing. Historically, the application of AI, artificial intelligence, and machine learning, in manufacturing anyway, has focused on automating repetitive lower-level processes, that replace humans in the factory. Today, what we need to think about, and what we focus on here at Augmentir, is how we can use AI to augment the human workforce. And so, AI, again, used throughout the industry, its served great application from a predictive maintenance, machine failure standpoint, energy efficiency – things like resource utilization and even supply chain visibility and quality control.

And those applications of AI in manufacturing will continue to provide value. But the reality is people are still needed in paper mills, on the factory floor in the areas of safety, quality, and maintenance. There are jobs that just require that humans are there. And that’s not going away any time soon. But what we are faced with, and what many manufacturers are faced with, is these workforce challenges of the aging workforce, the retiring workforce going away. They’re walking out the door with a vast amount of knowledge that is essential to operate factories and plants. Pre-pandemic we had an emerging workforce coming in that maybe didn’t have the necessary skills, but today post-pandemic era, there’s a massive job shortage. There are no workers coming in, and so manufacturers are forced to look at a pool of less-skilled workers to perform tasks that they may not be initially qualified for.

So, it is not just that the skilled labor is going out, it’s just that we don’t have any skills coming in. And so, every manufacturer is faced with a massive labor shortage and as a result a massive shortage of skills required to operate successfully any given day on the shop floor. And that’s really where we think the value is going to come from an AI standpoint, and it’s kind of transformative when you look at historically the application of AI in manufacturing.

Joshua Poole: So, you mentioned the industry is really struggling to overcome the lack of a qualified workforce. How can AI overcome this problem across the industry?

Chris Kuntz: One of the great things about artificial intelligence, and our history as a company, and one of our previous companies was focused on collecting data from connected machines and then using that data and analyzing that data with AI to understand how to make those machines operate better and improve those machines.

From a human standpoint, humans have been relatively disconnected on the shop floor. They’re using paper-based checklists and SOPs and work procedures, the same sort of technology they were using 20, 30 years ago. So, they’re relatively disconnected, and we know little about how they’re operating and how they’re performing and where they need help and where they need assistance.

If we can connect those workers – and I am talking connecting with phones, tablets, wearable devices – if we can connect those workers we have a digital portal into how they’re performing, and through AI we can analyze how they’re performing and then offer them real-time guidance almost like an AI assistant that’s sitting there helping them out if they are struggling, helping them out if they need help, guidance, or support, or if there is a potential safety or security issue that they might be running into.

The same way that AI has historically been used to act on machine data to improve machine efficiency and performance, we can use the same approach for the humans in the factory.

Joshua Poole: Mm-hmm, and can you provide any examples of the ways in which your platform, Augmentir, has benefited companies looking to embrace AI to improve their operations?

Chris Kuntz: Yes, there are a few different ways. More recently we just launched our Generative AI assistant called Augieℱ. And what that does is that allows workers or operations managers, using natural language, to solve problems faster, assist in troubleshooting, and provide guidance when needed.

One of the first use cases is troubleshooting. This happens every day in a plant, in a paper mill, it happens every day – there’s a problem with a machine, we need to get it back up and running. Otherwise, there’s a downtime issue, which leads to production/revenue loss. And it’s not a standard procedure to fix the machine. And so there’s troubleshooting that needs to happen. This process is very collaborative. But also from a worker standpoint, they typically have to go to 5, 6, 10 different systems to try to find information or talk to different people.

And what a Generative AI assistant can do is be that digital front end to all that wealth of information and return information on, “Hey here’s the solution to this problem. It’s been solved before, it’s in this published guide, here you go.” Or, “You may want refer at this work procedure. This is something, a troubleshooting guide that could help you solve the problem.” Or, “Here’s a subject matter expert that exists” and you can remotely connect to this person who has expertise in this particular type of equipment.

And so being able to give real-time access to that individual at the time of need is critical. And I think the other big area, at least early on here, is around training.

So, if you think about the skilled labor, workforce shortage, the tenure rates in manufacturing, people are quitting faster. They’re not sticking around for 15 years, they’re sticking around for three years, maybe, possibly, at max. And so, training and learning and development, HR leaders have to think about how to change onboarding practices because it’s not practical anymore to onboard someone for six months if they’re only gonna be around for nine months.

And so the goal, with many of the organizations that we speak with, the goal is to reimagine and rethink training and move it away from the before they’re productive in the classroom to move it onto the floor. Move it into the flow of work, they call it. And so what we can do with AI there is, we don’t understand that worker or their skill level or their competency levels. And if that’s digitally tracked, we can use AI to augment those work instructions and work procedures to say, “Hey, you’re a novice. This is your first month on the job. You’re required to watch this safety video before you do this routine.” And if you’re an expert worker, maybe you wouldn’t be required to do that. Or if you were trained, but your performance is lagging vs. the benchmark, we can come – the instructions can come and be dynamically adjusted to say, “Hey, here’s some additional guidance to help you through this procedure and through this routine.”

So, it gives visibility and insight into areas. I mean, if you had three people on the shop floor, you’d probably know exactly what they were doing. But once you get some larger organizations and they have dozens of people or hundreds of people, it becomes much much harder to understand where the opportunities for improvement are. And AI has the ability to do that, certainly in the training area.

Joshua Poole: Hmm, that’s very interesting. And of course, AI is largely unregulated worldwide, which can create problems like AI washing and irresponsible use. But what do you see as the biggest concern with the proliferation of AI systems within the packaging industry?

Chris Kuntz: So, certainly there’s a lot of concerns with respect to that, and for Augmentir, our approach is we leverage a – certainly from a Generative AI standpoint, we leverage a proprietary, fit-for-purpose, pre-trained large language model that sits behind our Generative AI solution. And when you combine that with robust security and permissions that can help factory managers, operators, and ever engineers or frontline workers only have access to the information that they need, and still provide the benefits of problem-solving faster and improved collaboration.

One of the other things though that I think is really important is this concept of “verified content” – so we’ve all used ChatGPT, right? And early on, I think they had this disclaimer, ChatGPT is 90% correct, so it could return false data. That’s not just not acceptable in an industrial settting. You can’t say, “Here’s a routine to do a centerlining on a piece of equipment” and have someone stick their hand in a place and get it chopped off. You can’t be 90%, you have to be 100%.

So, we have a concept of our Generative AI system, the ability to return verified and unverified data, and then the organization can decide what they want to do with that. So, if it’s a frontline worker, maybe, if it is unverified data, it’s labeled, and you need a supervisor that has to come over if you are going to perform that routine. And then the ability to sort of take the information that comes back and categorize it in terms of verified data, unverified data, and then be able to control how you’re using that. So, it’s not the wild wild west, it’s a very controlled environment. The scope of, if you think about our, in our world, if we’re serving a manufacturing company – and Augmentir is being used for digital manufacturing in paper and packaging companies like Graphic Packaging and WestRock, and so the information that, in our scope of their world is corporate documentation, engineering documentation, operational data, work order data, people data – could be their skills matrix and training history and things like that, but it’s all contained within their enterprise. We’re not looking outside of that, it’s really a constrained data set. And that’s what feeds our large language model.

That significantly helps the application of this, there are people that are exploring using more open AI and GPT models to do this. But then you run into the problems that you said, where there’s a lot of information that both you are feeding into the AI, which could be a security risk, and then the information that you are getting back that could be a security risk.

Joshua Poole: Okay, and as a final question. What advice would you give to politicians working to establish these regulatory frameworks for AI systems?

Chris Kuntz: Great question.

You know, our point of view is we think, you know President Biden had the AI regulation executive order here in the United States back in October, we think it’s much needed on several fronts. Certainly, every company now is saying that they’re an AI company and trying to sprinkle in AI to everything they do. And some of that can be a little problematic.

But at least in the U.S. here in Biden’s AI regulation executive order, there was a lot of talk about job disruptions and putting focus on the labor and union concerns related to AI policies. I think that reinforces our use of AI as a way to augment workers. We’re not looking to replace workers and it’s addressing a huge problem. I think the Department of Labor, they’re issuing guidance to employers around AI that you can’t use it to track workers and you can’t use it to, you know there’s labor rights that exist in the world. And I think that gets back to having these AI co-pilots or Generative AI assistants that can help workers perform their jobs safely and correctly, maximizing the potential. It’s really where on-the-job learning comes into play. It’s things that were happening off the factory floor before. Now it’s squarely suited to help address some of the big manufacturing labor workforce problems that exist today. So, there’s a lot of language in that executive order around making sure that AI is used, not just responsibly, but used for purposes that are going to further the industry. And again, that’s squarely where we sit in terms of workforce development and using it to address the labor shortages from a training and support standpoint.

But, overall, I think, absolutely we embrace the regulatory – Generative AI regulation – and control aspects of this because it could become problematic if you are not doing that, for sure.

Joshua Poole: Mm-Hmm that’s very interesting. Chris, thanks for your time today.

Chris Kuntz: Yes, thank you very much. Thanks for having me.

 

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The Evolution of AI in Manufacturing

Learn about the best practices for optimal asset maintenance performance and how to track your assets to ensure that everything is in working condition.

The evolution of artificial intelligence and machine learning technologies in manufacturing has seen tremendous growth over the past few decades, with astounding leaps in technology and industry-wide transformations.

evolution of ai in manufacturing

Dating back to the 1960’s, manufacturers started using AI in robotics and basic automation. This early usage focused on automating manual, highly repetitive human tasks such as assembly, parts handling, and sorting, allowing for higher levels of production and efficiency.

Over time, this evolved with AI-enabled machine vision systems, which were used to automate visual inspections, allowing for better quality control and precision during production cycles. More recently, AI has been at the center of warehouse automation, as well as the Industrial Internet of Things (IIoT), where physical machines and equipment are embedded with sensors and other technology for the purpose of connecting and exchanging data, which is used in predictive analytics for machine health monitoring. Manufacturers can now glean valuable insights from data collected over time about optimizing their operations for maximum efficiency without sacrificing quality.

Despite the breath of applications that AI has in the industrial setting, there is a common thread across all of the above examples – AI has largely been used to automate highly repetitive or manual tasks, or perform functions designed to replace the human worker.

However, these examples laid the groundwork for the adoption of AI in manufacturing and for the use of AI technologies that augment and directly support frontline workers today.

Read below for more information on how the use of AI and GenAI is evolving in manufacturing, and being used to augment the human worker, transforming productivity and efficiency at a time when workforce optimization is needed most.

Using AI to Augment, not Replace the Workers in our Factories

Today, AI technologies in manufacturing have evolved to encompass a diverse range of applications. According to Deloitte, 86% of surveyed manufacturing executives believe that AI-based factory solutions will be the primary drivers of competitiveness in the next five years. Robotics and automation have become more adaptive and collaborative, working alongside and augmenting human workers to streamline production processes and increase efficiency – rather than simply trying to replace them.

As computing power and algorithmic capabilities improved, AI in manufacturing has become more advanced and widespread. The emergence of Industry 4.0, characterized by the convergence of digital technologies, further accelerated AI’s role in manufacturing. By leveraging tools like connected worker solutions to gather frontline data, manufacturing organizations can now capitalize on AI’s extraordinary computing power to analyze that data and derive actionable insights, improved processes, and more.

Much like the industry has learned to optimize equipment from the 1.7 Petabytes of connected machine data that is being collected yearly, we are now able to optimize frontline work processes and people from highly granular connected worker data, with one major caveat: In order to leverage this incredibly noisy data, a system has to be designed with an AI-first strategy, where the streaming and processing of this data is intrinsic to the platform – not added as an afterthought.

The potential for AI to help augment the human worker is there, but why now?

Because for today’s manufacturers, time is not on your side.

The workforce crisis in manufacturing is accelerating, and at the forefront of the minds of Operations and HR leaders. Job quitting is up, tenure rates are down, and manufacturers struggle daily to find the skilled staff necessary to meet production and quality goals. The threat is huge – with significant impacts to safety, quality, and productivity.

AI-based connected worker solutions allow industrial companies to digitize and optimize processes that support frontline workers from “hire to retire”. These solutions leverage data from your connected workforce to optimize training investments and proactively support workers on the job, across a range of manufacturing use cases.

 

paperless factory

Furthermore, solutions that leverage Generative AI and proprietary fit-for-purpose, pre-trained Large Language Models (LLMs) can enhance operational efficiency, problem-solving, and decision-making for today’s less experienced frontline industrial workers. Generative AI assistants can leverage enterprise-wide data, provides instant access to relevant information, closes skills gaps with personalized support, offers insights into standard work and skills inventory, and identifies opportunities for continuous improvement.

Augmentir’s AI-First Journey

At Augmentir, since the beginning, we pioneered an AI-first approach toward manufacturing and connected frontline worker support. 

augmentir's ai-first journey

Many manufacturing solutions incorporated AI technology as an add-on or afterthought as the technology gained more advanced capabilities and popularity. We, however, have been championing and building a suite of solutions using AI as a foundation. Our platform was designed from the bottom up with AI capabilities in mind, placing us as a leader in the connected frontline worker field. 

  • 2019 – Augmentir launched the world’s first AI-first connected platform for manufacturing work empowering frontline workers to perform their jobs with higher quality and increased productivity while driving continuous improvement across the organization. This marked the start of our AI-first journey, giving industrial organizations the ability to digitize human-centric work processes into fully augmented procedures, providing interactive guidance, on-demand training, and remote expert support to improve productivity and quality.
  • 2020 – Augmentir unveiled True Opportunityℱ, the first AI-based workforce metric designed to help improve operational outcomes and frontline worker productivity through our proprietary machine learning algorithms. These algorithms take in frontline worker data, then combine it with other Augmentir and enterprise data to uncover and rank the largest capturable opportunities and then predict the effort required to capture them.
  • 2021 – Building on user feedback and field data, Augmentir reveals True Opportunity 2.0ℱ, with improved and enhanced capabilities surrounding workforce development, quantification of work processes, benchmarking, and proficiency. By Leveraging anonymized data from millions of job executions to significantly improve and expand the platform’s ability and automatically deliver in-app AI insights we were able to increase benefits and returns for Augmentir customers.
  • 2022 – Augmentir announces the release of True Productivityℱ and True Performanceℱ. True Productivity allows industrial organizations to stack rank their largest productivity opportunities across all work processes to focus continuous improvement teams at the highest ROI and True Performance determines the proficiency of every worker at every task or skill enabling truly personalized workforce development investments.
  • 2023 – Augmentir launches Augieℱ – the GenAI-powered assistant for industrial work. By incorporating the foundational technology underpinning generative AI tools like ChatGPT, we enhanced our already robust offering of AI insights and analytics. Augie adds to this, improving operational efficiency and supporting today’s less experienced frontline workforce through faster problem-solving, proactive insights, and enhanced decision-making.
  • 2024 – As this year progresses, we have already continued to refine our AI-first solutions and apply user feedback and additional features to best support frontline industrial activities and workers everywhere.
  • 2025 and beyond – True Engagementℱ, looking forward we predict the evolution of AI in manufacturing activities will continue, progressing until we can accurately measure signals to detect the actual engagement of industrial workers and derive useful information and insights to further enhance both HR and manufacturing processes.

We are deeply involved in applying AI and emerging technologies to manufacturing activities to augment frontline workers, not replace them. Providing enhanced support, access to key knowledge (when and where it does the most good), and improving overall operational efficiency and productivity.

The Future of AI in Manufacturing – The Journey Forward

As we press onward into the future, we at Augmentir are determined to champion the application of AI and smart manufacturing to augment and enhance frontline workers and industrial processes. We will continue to evolve our application of AI and its use cases in manufacturing to help frontline teams and workforces, reinforcing our AI-first pedigree.

The addition of Augie to our existing AI-powered connected worker solution is an important step forward. Augie is a Generative AI assistant that uses enterprise-wide data, provides instant access to relevant information, closes skills gaps with personalized support, offers insights into standard work and skills inventory, and identifies opportunities for continuous improvement. Augie is a result of our dedication to empowering frontline workers, leveraging AI to support manufacturing operations, and giving manufacturing workers better tools to do their jobs safely and more efficiently.

With patented AI-driven insights that digitize and optimize manufacturing workflows, training and development, workforce allocation, and operational excellence, Augmentir is trusted by manufacturing leaders as a digital transformation partner delivering measurable results across operations. Schedule a live demo today to learn more.

 

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AI: Revolutionizing How Humans Work in Manufacturing

AI is playing a key role in changing the manufacturing landscape, augmenting workers and empowering them with improved, optimized processes, better data, and personalized instruction.

Deloitte recently published an article with the Wall Street Journal covering how AI is revolutionizing how humans work and its transformative impact. They emphasized that AI is not merely a resource or tool, but, that it serves almost as a co-worker, enhancing work processes and efficiency. This article discussed how the evolving form of intelligence augments human thinking and emphasized this as a catalyst for accelerated innovation.

Manufacturing is uniquely situated to benefit from AI to improve operations and empower their frontline workforces. The skilled labor gap has reached critical levels, and the market is under tremendous stress to keep up with growing consumer demand while staying compliant with quality and safety standards. Manufacturing workers are crucial to the success of operations – maintenance, quality control and assurance, and more – manufacturers rely upon their workforce to ensure production proceeds smoothly and successfully.

AI is playing a key role in changing the manufacturing landscape, augmenting workers and empowering them with improved, optimized processes, better data for informed decision-making, troubleshooting, personalized instructions and training, and improved quality assurance and control. According to the World Economic Forum, an estimated 87% of manufacturing companies have accelerated their digitalization over the past year, the IDC states 40% of digital transformations will be supported by AI, and a recent study from LNS Research found that 52% of industrial transformation (IX) leaders are deploying connected worker applications to help their frontline workforces. Not only that, AI technology is expected to create nearly 12 million more jobs in the manufacturing industry.

Integrating AI into manufacturing not only enhances productivity, but also opens the door to new possibilities for worker safety, training, and innovative new manufacturing practices. Here are some ways AI is transforming manufacturing operations:

  • AI-based Workforce Analytics: Collecting, analyzing, and using frontline worker data to assess individual and team performance, optimize upskilling and reskilling opportunities, increase engagement, reduce burnout, and boost productivity.
  • Personalized Training in the Flow of Work: With AI and connected worker solutions, manufacturers can identify and supply training at the time of need that is personalized to each individual and the task at hand.
  • Personalized Work Instructions: AI enables manufacturers to offer customized digital work instructions mapped to their skill levels and intelligently assign work based on each individual’s capabilities.
  • Digital Performance Support and Troubleshooting Guide: Generative AI assistants and bot-based AI virtual assistants offer support and guidance to manufacturing operators, enabling access to collaborative technologies and knowledge bases to ensure the correct actions and processes are taken.
  • Optimize Maintenance Programs: AI algorithms analyze data from sensors on machinery and other connected solutions to predict when equipment is likely to fail. This enables proactive maintenance, minimizing downtime and reducing maintenance costs. Additionally, with AI technologies, manufacturers can implement autonomous maintenance processes through a combination of digital work instructions and real-time collaboration tools. This allows operators to independently complete maintenance tasks at peak performance.
  • Improve Quality Control: AI-powered solutions can improve inspection accuracy and optimize quality control and assurance processes to identify defects faster. With connected worker solutions, manufacturers can effectively turn their frontline workforce into human sensors supplying quality data and enhancing assurance processes.
  • Ensure Worker Safety: AI-driven safety systems coupled with connected worker technologies monitor the work environment, supplying real-time data and identifying potential hazards to ensure a safer workplace for employees.

connected enterprise

As AI continues to advance, the manufacturing industry is poised for even greater transformation, improving both the quality of products and the working conditions for employees. AI is revolutionizing the way humans work and how the manufacturing industry approaches nearly every process across operations, augmenting work interactions, productivity, efficiency, and boosting innovation.

How well can we really know our workers?

AI-powered technology may be the missing puzzle piece for today’s workforce crisis.

It wouldn’t be fair to attribute all of manufacturing’s current labor shortage woes to the pandemic–there are a lot of factors contributing to this frustrating situation, and many of them were looming long before we ever heard of COVID-19. Did it make things worse? Probably. And the forecast doesn’t look very sunny if you believe what analysts have to say about it. However, despite the current crisis, there is hope yet for manufacturing, specifically in the form of AI and Connected Worker Technology.

Sure, the face of the workforce has changed dramatically. The pool of potential laborers has shrunk. Businesses are being forced to hire people traditionally considered under-qualified. And that leads to a whole host of other complications, including a drop in operational efficiency, a rise in safety issues, and more. The pessimists out there would only see the threat to the global market these challenges pose–the manufacturing industry makes between 11 and 12 percent of the US economy after all.

Good thing we’re optimists at heart! Behind every challenge is an opportunity, as far as we’re concerned. And when it comes to this challenging labor market in particular, we see a huge opportunity for businesses to work with what they’ve got, and still reach operational goals. We have the potential to assess how every worker performs on the job, regardless of the experience and skill set they bring on day one, and use that information to improve individual and enterprise-wide performance. Puts a new light on the labor shortage, doesn’t it?

You can’t fix what you can’t see.

We know using data is important to directing and improving operations–that’s business best practices 101. But insights drawn are only as good as the data itself. And even though there can’t be many businesses out there who haven’t yet jumped on the digital transformation bandwagon, we suspect a lot still rely on outdated data collecting and reporting mechanisms. Those digital spreadsheets had their moment, but we’ve got better options now. Maybe you opted for a Bluetooth software program or distributing a digital survey for your workers. But even with those innovations, what do these data indicators really tell you? Is this reliable and usable information? We didn’t think so either.

Imagine what you could do with real-time data, rather than a summary of operational KPIs at the end of set periods? Even better–imagine capturing the performance metrics of each individual worker rather than their self-generated assessments and observations and having the potential to use that knowledge to improve their skill set and operational proficiency. That’s when data becomes intelligence. And that intelligence has the potential to become so valuable to your enterprise that you’ll wonder how you ever operated without it.

Not convinced you could benefit from data at that level of individual performance? Let us draw an analogy we think you’ll appreciate.

Think of each worker as a newly licensed driver; what happens after passing the road test?

Remember the day you got your driver’s license? We spent hours, if not days and weeks practicing behind the wheel, eagerly waiting to be evaluated by a driving instructor. And let’s be honest, plenty of us winged it, too. Either way, once you show them you can do a three-point-turn and know to stop at the flashing pedestrian crossing sign, everyone walks away with the proof of their proficiency–a driver’s license. 

Then what happened? Nothing. Maybe a celebratory high-five and then eventually years of driving. In one, five, or ten years, what do we know about each person’s capabilities? Unless they’ve wracked up a stack of tickets for traffic violations, we don’t know anything. For all we know, they haven’t sat behind the steering wheel since passing. There is no mechanism to re-assess whether drivers are highly skilled or at-risk of creating an accident in operations.

Now what if we looked at our frontline workers through that lens? You know when they were hired that they could perform X, Y and Z. Some could do even more. But what about after that? What if you could assign an AI-based driver instructor to follow each new driver around for ongoing assessment and intervention in the moment of need?

Put smart connected worker technology in the passenger seat

Adopting connected worker technology powered by artificial intelligence (AI) increases the reliability and credibility of data by analyzing employee performance in ‘real-time.’ That individualized data can be used to connect workers with a company’s digital library of training tools and resources, having an immediate impact on operational proficiency and cultivating a healthy learning environment for workers.

Connected worker technology that leverages AI offers self-guided learning processes when opportunities are identified, reduces human error and improves safety, provides updates on pressing issues and equipment failures and access to a variety of applications. Who wouldn’t want to work for an organization like this? One that offers a high probability of job satisfaction and encourages personal skill development? A culture like that can help the operation on many levels, from reducing operational costs to attracting new employees. 

What now? There is only one connected worker solution that can provide this level of intelligence on your workforce–contact us to learn more about how Augmentir can benefit your business and ask for a demo!

This Is Not Your Grandfather’s Workforce

Today’s dynamic and changing manufacturing workforce needs continuous learning and performance support to effectively sustain and deliver effective on-the-job performance.

Every day we hear about the growing manufacturing “Skills Gap” associated with the industrial frontline workforce. The story is that 30% of workers are retiring in the near future and they are taking their 30+ years of tribal knowledge with them, creating the need to quickly upskill their more junior replacements. In an attempt to solve the knowledge gap issues, an entire generation of companies set out to build “Connected Worker” software applications, however, they all relied on the existing training, guidance, and support processes – the only true difference with this approach has been the creation of technology that takes your paper procedures and puts them on glass.

Along with tribal knowledge leaving, today’s workforce is also more dynamic and diverse than previous generations. The 30-year dedicated employees are no longer the norm. The average manufacturing worker tenure is down 17% in the last 5 years and the transient nature of the industrial worker is quickly accelerating. An outgrowth of the COVID pandemic brings forth the Great Resignation, where workers are quitting in record numbers, and worker engagement is down almost 20% in the last 2 years. 

This new manufacturing workforce is changing in real-time – who shows up, what their skills are, and what jobs they need to do is a constantly moving target. The traditional “one size fits all” approach to training, guidance, and performance support is fundamentally incapable of enabling today’s workers to function at their individual peak of safety, quality and productivity. 

Digitizing work instructions is a great start to helping close the manufacturing skills gap, but alone, it won’t help completely solve the problem. We must go a step further to overcome the lack of a skilled and qualified manufacturing workforce. 

Enter the 2nd generation of Connected Worker software, one based on a data-driven, AI-supported approach that helps train, guide, and support today’s dynamic workforces by combining digital work instructions, remote collaboration, and advanced on-the-job training capabilities. 

These 2nd generation connected worker solutions are designed to capture highly granular data streaming from connected frontline workers. These platforms are built from the ground up on an artificial intelligence (AI) foundation. AI algorithms are ideal for analyzing large amounts of data collected from a connected workforce. AI can detect patterns, find outliers, cleanse data and find correlations and patterns that can be used to identify opportunities for improvement and creates a data-driven environment that supports continuous learning and performance support.

This approach aligns perfectly with the dynamic, changing nature of today’s workforce, and is ideally suited to support their 5 Moments of Need, a framework for gaining and sustaining effective on-the-job performance.

For example, Augmentir’s AI-powered connected worker platform leverages anonymized data from millions of job executions to significantly improve and expand its ability to automatically deliver in-app AI insights in the areas of productivity, safety, and workforce development. These insights are central to Augmentir’s True Proficiencyℱ scoring, which helps to objectively baseline each of your team members for their level of proficiency at every task so organizations can optimize productivity and throughput, intelligently schedule based on proficiency and skill-levels, and personalize the level of guidance and support to meet the needs of each member of the workforce.

This provides significant benefits to Augmentir customers, who leverage Augmentir’s AI in conjunction with the platform’s digital workflow and remote collaboration capabilities, allowing them to deliver continuous improvement initiatives centered on workforce development. These customers are able to utilize the insights generated from Augmentir’s AI to deliver objective performance reviews, automatically identify where productivity is lagging (or has the potential to lag), increase worker engagement, and deliver highly personalized job instructions based on worker proficiency.

Traditionally, there was a clear separation between training and work execution, requiring onboarding training to encompass everything a worker could possibly do, extending training time and leading to inefficiencies. Today, with the ability to deliver training at the moment of need, onboarding can focus on everything a worker will probably do, identifying and closing skills gap in real-time and significantly reducing manufacturing onboarding times. In one particular case, Bio-Chem Fluidics was able to reduce onboarding time for new employees by up to 80%, while simultaneously achieving a 21% improvement in job productivity across their manufacturing operation.

As workers become more connected, companies have access to a new rich source of activity, execution, and tribal data, and with proper AI tools can gain insights into areas where the largest improvement opportunities exist. Artificial Intelligence lays a data-driven foundation for continuous improvement in the areas of performance support, training, and workforce development, setting the stage to address the needs of today’s constantly changing workforce.

Trends from October’s Top Virtual Manufacturing Events

These virtual events were a great way to connect with manufacturing professionals and discuss some of the industry’s top challenges and topics – workforce transformation, learning and development, lean manufacturing, and autonomous maintenance.

October was an exciting month in the virtual manufacturing world! Augmentir had the pleasure of participating in several virtual events including the American Manufacturing Summit, Gartner Supply Chain Symposium/Xpo, and the Enterprise Wearable Technology Summit (EWTS). Each of these virtual events were a wonderful way to connect with manufacturing professionals and discuss some of the industry’s top challenges and topics – workforce transformation, learning and development, lean manufacturing, and autonomous maintenance. 

EWTS

The Enterprise Wearable Technology Summit (EWTS) is the longest-running and most comprehensive event dedicated to the business and industrial applications of wearables, including AR/VR/MR glasses and headsets, body-worn sensors, and exoskeletons. This year’s event took place in four bite-sized conference days (every Wednesday from October 6-27, 2021), with community, networking and additional content drops throughout the rest of the month. This unique format allowed for great networking as well as some very valuable sessions. In one of the polls, 32% said that Remote onboarding and training was the top use case for immersive/wearable technology at their company.  

American Manufacturing Summit

The American Manufacturing Summit is a leadership focused meeting designed to bring global manufacturing, operations, engineering, quality and supply chain leaders together to discuss current trends, strategic insights, and best practices in an ever-evolving manufacturing environment. Dave Landreth, Augmentir’s Head of Customer Strategy, had the opportunity to lead a fire-side chat in discussing how Artificial Intelligence and Connected Worker technologies are key pillars of the Industrial Workforce Transformation. We also enjoyed the 1:1 meetings that took place as part of the American Manufacturing Summit. 

Gartner Supply Chain Symposium/Xpo

The Gartner supply chain conference offers attendees a one-stop-shop to access research-backed sessions, get expert advice and problem-solve with colleagues. The main focus of this event is to address the strategic needs of CSCOs and supply chain executives and showcase new technologies that adapt to the ever-changing environment in which they’re operating.

Sessions from the event dealt with purpose-driven supply chains and learnings from the pandemic for the healthcare supply chain, risk assessment and global trade, top trends for smart manufacturing, the future of quality management and supply chain planning, and resolving the dichotomy of logistics outsourcing. 

Key announcements from the virtual manufacturing events:

Continuous improvement, connected worker technology, AI, and data-driven technology were among the top trends from these events. Manufacturing organizations are looking for Connected worker software, like Augmentir, to integrate frontline workers and improve productivity, training, and quality. In addition, as we continue to work remotely and see more supply chain disruptions, AI-based, data-driven technology will be essential to building flexible factories that address these challenges and allow for continuous improvement.

Empowering Your Frontline Workers to go the Extra Mile with Augmentir

Augmentir, a data-driven, AI-powered Connected Worker solution ensures that your frontline workers can go the extra mile every day by providing training, guidance, and support through the combination of digital work instructions, remote collaboration, and advanced on-the-job training.

In 2009, the Extra Mile America Foundation, a foundation promoting action and empowering positive change, first celebrated the “Extra Mile Day”.  Fast forward to 2014 and 527 US cities declared November 1st as the official “Extra Mile Day”. This day is designated to living life to your full potential, having a positive influence, and always striving to make the world a better place. At Augmentir, we believe in positive change, living to your full potential, and going the extra mile every day. In 2017, our founders decided to continue their journey of creating some of the most important software technology revolutions in manufacturing (Wonderware (1987), Lighthammer (1997), and ThingWorx (2008) by focusing on the most important asset, the frontline workers.

We recognize that today’s industrial workforce is different, and the old way of supporting workers just doesn’t work anymore. The static “one size fits all” approach used in the past, no longer applies to this generation of workers.

Augmentir, is a data-driven, AI-powered Connected Worker solution that ensures that frontline workers can go the extra mile every day by providing training, guidance, and support through the combination of digital work instructions, remote collaboration, and advanced on-the-job training.  

As your workers become more connected, companies have access to a new rich source of activity, execution, and tribal data, and with proper AI tools they can gain real-time insights into areas where the largest improvement opportunities exist. Artificial Intelligence lays a data-driven foundation for continuous improvement in the areas of performance support, training, and workforce development, setting the stage to address the needs of today’s constantly changing workforce.

When it comes to our frontline workers, let’s enable them to go the extra mile, not just on November 1st, but every day, by empowering them with the only AI-powered Connected Worker solution.

Augmentir: New Look, Same Powerful AI and Connected Worker Solution

You may have noticed that our website and brand look a little different. Augmentir has a new look, but under the hood, its the same powerful AI that is helping to transform the industrial workforce of the future.

You may have noticed that our website and brand look a little different. Well, that’s because behind the scenes for the past few months, we’ve been under construction (no pun intended).

Augmentir was founded in 2017 with the vision to use AI to empower the industrial frontline workforce to perform at their best. This was a continuation of our rich history – our founding team has been at the forefront of three of the most important of these software technology revolutions in manufacturing over the past three decades – Wonderware Software in 1987, Lighthammer in 1997, and ThingWorx in 2008.

A lot has changed since then. The world we live in today is not the same as it was 4 years ago. And in the last two years, the COVID-19 pandemic has altered the stability of the workforce and drastically magnified some of the industry’s top workforce challenges, which stem from the unprecedented levels of dynamism in the areas of skills diversity, reduced tenure, and increased churn from the “Great Resignation”. Unlike the stable and predictable workforce of the recent past, today companies have to live in the new normal where workers are hard to find, hard to engage, and hard to keep.

These top challenges of today have only reinforced the need for an AI-powered, data-driven approach to empowering frontline workers.

This data-driven era we’re entering into is one of continuous learning and development with tools like remote collaboration and digitized work processes truly integrating frontline workers into the fabric of the business from a collaboration standpoint whereas they may have been overlooked before.

Augmentir’s AI-powered connected worker platform provides the tools to not only survive in this new normal but to thrive.

You can’t build a truly modern, connected workforce without AI

The term “connected worker” has become a recent buzzword in the manufacturing world, and is now considered a tool that the new generation of workers expect to work with. But true connected work means using AI to allow frontline workers to have access to internal and external resources that are appropriate for when and how they need them.

Augmentir isn’t your typical connected worker platform. Our platform was built from the ground up on an AI foundation. AI algorithms are ideal for analyzing large amounts of data collected from a connected workforce. AI can detect patterns, find outliers, cleanse data and find correlations and patterns that can be used to identify opportunities for improvement and create a data-driven environment that supports continuous learning and performance support. Our connected worker platform utilizes AI to help train, guide, and support today’s frontline workers in a dynamic workforce by combining digital work instructions, remote collaboration, continuous development and advanced on-the-job training capabilities.

This approach aligns perfectly with the dynamic, changing nature of today’s workforce, and is ideally suited to achieve and sustain effective on-the-job performance.

As the world’s only AI-powered connected worker platform, we decided it was time to refresh our brand identity to accentuate our strongest feature and the thing that makes Augmentir unique – AI. We’re still the same AI-first connected worker platform that you know – just with a new look.