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AI Copilots and AI Agents in Manufacturing – Chris Kuntz at MD&M East 2025

Recap of Chris Kuntz’s session at MD&M East 2025 on how AI copilots and AI agents are transforming manufacturing—from enhancing workforce capabilities to enabling autonomous operations.

June 11, 2025

At this year’s MD&M East, formerly IME East 2025, Augmentir took center stage as Chris Kuntz, VP of Strategic Operations, delivered a powerful presentation on the transformative role of AI Copilots and AI Agents in manufacturing.

Read below for a brief recap of the presentation, as well as a video recording of the presentation.

Addressing the Workforce Crisis with AI

Chris opened with a sobering reality: even if every skilled worker in the U.S. were employed, 35% more manufacturing jobs would remain unfilled. Citing a $1 trillion annual opportunity cost by 2030 (Deloitte), Chris emphasized that traditional workforce strategies aren’t enough—and the time for intelligent automation and workforce augmentation is now.

Key Highlights from the Presentation

The Rise of AI Copilots and AI Agents

Chris introduced AI Copilots as conversational tools powered by LLMs, providing contextual, real-time support to workers. AI Agents, on the other hand, are autonomous systems that execute complex tasks independently—reducing friction, downtime, and manual inefficiencies.

The Augmented Connected Worker

At the heart of the talk was Augmentir’s Connected Worker technology—a framework that brings together:

AI-powered guidance and support to help frontline workers perform tasks more efficiently, safely, and accurately.
Real-time data capture and insights that drive continuous improvement across operations, training, and workforce performance.
A unified digital platform that connects people, processes, and systems to enable scalable workforce transformation in manufacturing.

6 Game-Changing Use Cases

Chris walked attendees through six real-world use cases—showing how Augmentir and Augie are delivering measurable outcomes for manufacturers:

Content Assistant – 76% faster digitization of SOPs and training docs
Work & Training Assistant – 82% reduced onboarding time
Image Comparison – Improved inspection accuracy, reduced rework
Skills & Training AI Agent – On-demand learning and certification
Operations Agent – Real-time troubleshooting support
Corporate Knowledge Graphs – Smarter access to institutional knowledge

Case studies from leading packaging and beverage companies added real-world credibility, demonstrating how organizations are scaling faster while minimizing downtime and safety incidents.

Video Recording

Full Transcript

My name is Chris Kuntz. I’m with an AI company called Augmentir, and we provide connected worker software for frontline workers in manufacturing. Today what I’ll be talking about is artificial intelligence, which in many ways has taken over the media, and become a major part of our lives, but I want to talk about it in the context of manufacturing and specifically talk about generative AI assistants and AI agents that can be used in manufacturing to help guide and support today’s frontline workers.

So just a quick 30 seconds on Augmentir and who we are as a company. We’re a relatively young company, founded in 2018, but we have a pretty deep history in innovative software and manufacturing, dating back to the late 1980s. The founders of Augmentir were the same industry innovators that founded Wonderware in 1987, which revolutionized HMI software in factories. Wonderware went public and is now part of AVEVA/Schneider Electric. We were the founders of Lighthammer, which is now part of SAP’s MII offering. And we were the founders of ThingWorx, which is now part of PTC and revolutionized the Industrial Internet of Things space. And when we left PTC, the team got back together again and we wanted to focus on tackling what we considered to be the next big problem in manufacturing at the time, which was the human worker.

If you think about AI and how it’s been, automation and how AI has optimized production lines, really the last mile for driving efficiency in manufacturing is the human worker. And even more apparent over the past five years since the pandemic, the labor shortage, the skilled labor shortage has created dramatic impacts on product quality, product efficiency, and overall throughput in manufacturing. And so our goal at Augmentir was to tackle that.

So let’s start this conversation by talking about AI and the history of AI in manufacturing. And it dates back to the 1960s. AI has been used in automation in manufacturing for decades now. It’s been used to drive incredible levels of efficiency. It’s been used in machine vision systems for quality improvements. And you see that you, when you walk around, manufacturing trade shows like this, it’s been used in warehouse in warehousing automation, and more recently, it’s been used in the industrial internet of things, digitally connecting equipment and using AI to analyze the data that is coming off of that equipment to drive greater efficiencies in production, production efficiency in manufacturing. But a common theme across all of this is up to this point, AI has been used to replace the human worker or to optimize manual labor or manual efforts that humans were doing in factories, previously. AI has a unique opportunity, specifically around generative AI co-pilots, if you think ChatGPT or AI agents, is to augment the human worker, not replace them.

And so the question we asked ourselves at Augmentir, when we started, was, can AI do the same for humans? Can AI drive efficiency for the humans that are still on the shop floor in manufacturing, quality, engineering, and maintenance roles and in equipment operation. More importantly, in maintenance, can AI be used to optimize the work that they’re doing? And why now?Here are some statistics from a report that LNS Research, an analyst firm based out of Boston ran last year on the future of industrial work. Pretty fascinating statistics. When they look at the average tenure rate in manufacturing, 2019 compared to the end of last year. So from 20 years to three years, the average time and position went down from seven years to nine months, and the average three-month retention rate, the rate at which people stay in after the first three months, from 90% down to 50%. So the problem you have in manufacturing today is yes, there’s a labor shortage, yes, it is difficult to find skilled labor, but because humans are required in manufacturing, what organizations are doing is hiring less skilled workers. And now you have a problem that’s really twofold on the shop floor. You have less experienced workers that also have less experience or less skilled workers, also have less experience. And that results in safety issues, quality issues, product recalls, downtime, everything possible that you can imagine that relates to human error in or on a factory floor.

In this survey, from LNS, the respondents, 92% of them said they were looking at technology as a way to offset that skilled labor gap. Now, it’s not the only solution, certainly there are better hiring strategies, better training strategies, but certainly looking at technology as a big piece of offsetting that labor crisis. Just another statistic here from a study in Deloitte, even if every skilled worker, and this is just in America, even if every skilled worker was employed, there would still be a 35% gap in unfilled job openings in manufacturing. That’s how bad it is. And so Deloitte predicts by 2030, that it’s a $1 trillion problem in the US alone. And I think they forecasted $3 trillion globally, a problem that exists for production output and manufacturing.

So that brings us to what we’re talking about here today, AI agents and co-pilots. Everyone here has used ChatGPT or Gemini or Perplexity or whatever, chatbot you want to use today, fantastic results and fantastic opportunities when you think about consumer AI, but what I want to do is talk about the context of AI assistance, as well as agents, which there’s some blurring of the line there, but we’ll talk about that, and their applicability in industrial operations and why it’s quite a bit different from consumer AI.

So what is an AI co-pilot? Best example is ChatGPT, right? We’ve all used it. Natural language interface, the ability to use what they call a large language model, LLM, for those of you that might not be as technical, which has the ability for that agent or that assistant to understand vast amounts of data and it provides context assistance to users. On the flip side, what is an agent? An agent is an AI bot that acts more autonomously. They can operate based on a prompt like you would have with ChatGPT, but they don’t have to. So they can actually take autonomous action based on instructions you give it. Now, when you think about it, I’m going to use ChatGPT as an example today because I think we’ve all probably used it or used something similar. They’re starting to blur the lines a little bit with their, I think they’re calling it the ChatGPT operator, so that’s starting to blur the lines between autonomous and strictly prompt-based AI. But the idea is the same in the context of today, what we’re talking about in terms of an AI co-pilot or an assistant that is a prompt-based bot that that a user might be using. And from an agent standpoint, it is something that can act more autonomously. And a prerequisite to all this, when you think about manufacturing and you think about frontline workers, whether they are working in safety, quality, equipment and machine maintenance and repair or equipment operation, a prerequisite to all this is the ability to have a connected worker.

And by connected worker, what we like to talk about at Augmentir is a worker that is not only connected with a digital or a mobile tool, like a phone, a tablet, a wearable technology, a wearable augmented reality-based headset, for example, but also digitally connected into the business. So using that interface to not just connect them physically with a device, but connect them into HR systems, learning management systems, ERP systems, quality systems, and safety systems, systems that they use every day. But now that they’re connected, they can become human sensors on the shop floor. And there’s a vast amount of data that we can then capitalize on here, and AI can then act on.

So what I want to do now is talk about consumer AI, again, the example of ChatGPT compared to industrial AI. And in the case of today, I’m going to give some examples of manufacturing companies that are actually using this technology today. But when you think about industrial operations, you have to think quite a bit differently than how we might use Gemini or ChatGPT today. So I’m just going to walk through an example here. You have a frontline worker, an operator on a manufacturing floor, and their job every day is to operate the mixer. Okay? Part of their job is also to periodically do a clean, inspect, and lubricate on that piece of equipment, so it doesn’t go down or so that they can prevent failures from happening. So that’s a CIL. So now go back to the context that I started this conversation with. Let’s say you have a less experienced worker, maybe they are a novice worker.

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Augmentir Wins Gold at 2024 Brandon Hall Group Excellence in Technology Awards

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Augmentir recognized by the Brandon Hall Group for the “Best Advance in Generative AI for Business Impact”, wins gold in the 2024 Technology Excellence Awards.

December 10, 2024

We did it again!

We are excited to announce today that Augmentir won Gold in the 2024 Brandon Hall Group Excellence in Technology Awards for “Best Advance in Generative AI for Business Impact“.

The 2024 Brandon Hall Group Excellence in Awards™ are given for work in Learning and Development, Talent Management, Talent Acquisition, Human Resources, Sales Enablement, Future of Work, and Education Technology. Augmentir received its gold award in the Future of Work category based on our breakthrough, innovative use of Generative AI to address skilled labor shortages and workforce challenges that are crippling the manufacturing industry today.

Entries were evaluated by a panel of veteran, independent senior industry experts, Brandon Hall Group analysts, and executives based upon these criteria: fit the need, program design, functionality, innovation, and overall measurable benefits.

“In our 31st year, the Excellence in Technology Awards continue to showcase the best innovations in learning, talent management, talent acquisition, HR, workforce management, and sales enablement technologies. We are proud to receive applications from a diverse range of organizations globally, reflecting the ever-evolving landscape of technology solutions,” said Brandon Hall Group Chief Operating Officer Rachel Cooke, leader of the Excellence Awards program.

Augmentir’s generative AI solution – Augie™ – is a central component to the Augmentir Connected Worker platform. Augie is a generative AI assistant that improves operational efficiency and supports today’s less experienced frontline workforce through faster problem-solving, proactive insights, data analysis, rapid content creation, and enhanced decision-making.

Augmentir recently unveiled powerful new updates to Augie, and launched the industry’s first Industrial Generative AI Suite, targeted towards improving safety, quality, and productivity for the industrial frontline workforce. Augie’s suite of gen AI services expand on the platform’s existing capabilities, which have been in use by leading manufacturers for over a year, transforming operations and addressing the skilled labor shortage through advanced troubleshooting and real-time digital assistance to frontline workers. The Augie Industrial Gen AI Suite includes:

Augie Industrial Work Assistant
Provide real-time support and guidance to workers on the floor or in the field. Augie helps workers with standard work, troubleshooting, and information access.
Augie Content Assistant
Automatically convert existing digital content (Word Excel, PDF, etc) into native Augmentir Work instructions, SOPs, OPLs, CILs, Checklists, etc., accelerating deployment. Generate training, checklists, and quizzes from a wide range of source types including images, manuals, free-form tests, etc., to streamline worker training and onboarding.
Augie Data Assistant
Augie provides insights from any source of operational data, including standard datasets such as Skills, Standard Work, Safety, and Work Execution, as well as customer-specific datasets generated through Augmentir’s report configurator. Augie eliminates the need for “report writing” and through its conversational interface answers questions, performs math, and generates graphical reports, increasing responsiveness.
Augie Extensibility Assistant
Augie increases the productivity of developers building new functions and supporting existing user-defined functions within Augmentir’s extensibility framework. Augmentir’s unique Platform-as-a-Service offering empowers customers and partners to create unique solutions that solve critical business challenges—a capability that no other platform on the market offers.
Augie Industrial GenAI-as-a-Service
As an industry first, Augie exposes its GenAI capabilities as APIs within Augmentir’s extensibility framework. This allows companies and partners to create innovative, customized GenAI solutions tailored to business, or industry-specific needs and use cases. Commonly used APIs include: translateText enabling on-the-fly translation of dynamic content, and imageQA, enabling direct comparison or summarization of images, supporting critical applications in Quality, Safety, and Operations.

“We’re thrilled to be recognized by the Brandon Hall Group for bringing the transformative power of generative AI to industrial frontline operational processes,” said Russ Fadel, CEO of Augmentir. “Just as we have seen GenAI deliver transformational value to the consumer and enterprise, the Augie Suite provides the tools to enable companies to empower their frontline workers, regardless of experience, to perform with higher levels of safety and productivity. Additionally, this provides the tools for our partners to build innovative use cases to solve previously unsolvable problems.”

Augmentir introduced Augie in early 2023, becoming the first software provider in the manufacturing sector to offer a generative AI solution focused on the industrial frontline workforce. Since its launch, Augie has been adopted by industry leaders across all manufacturing and production verticals, helping prevent safety and quality issues at the point of work, driving operational efficiency, and giving frontline workers the tools, guidance, and support they need to do their best work.

Augie’s generative AI capabilities are built into the core of the Augmentir platform, so customers can quickly and securely leverage the latest AI advances within the framework of digital collaboration, skills management, and work execution. This allows customers to leverage existing data, documents, applications, and their existing tribal knowledge, increasing their ROI.

Interested in learning more?

If you’d like to learn more about Augmentir and see how our AI-powered connected worker platform enables Augmented Connected Worker initiatives to improve safety, quality, and productivity across your workforce, schedule a demo with one of our product experts.

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How Augmentir’s AI Delivers Transformational Value Across the Connected Worker Journey

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Discover how Augmentir’s AI transforms the connected worker journey—boosting training, productivity, and continuous improvement across operations.

July 24, 2025

In an era defined by digital transformation and workforce disruption, industrial companies are reimagining how they support their frontline teams. At the heart of this shift is the Connected Worker—a worker empowered by technology to perform safely, efficiently, and with confidence.

But enabling a connected workforce requires more than just digitizing procedures or implementing standalone tools. It demands intelligence embedded throughout the entire worker lifecycle.

That’s where Augmentir’s AI-powered Connected Worker platform shines. By infusing intelligence into every phase of the connected worker journey—from content creation to onboarding, daily operations, and continuous improvement—Augmentir delivers measurable gains in productivity, safety, and workforce engagement.

A Framework for Transformation: The Connected Worker Journey

The Connected Worker Journey consists of four critical, interrelated stages:

Content Conversion & Creation
Worker Onboarding & Training
Operational Excellence
Continuous Improvement

At each phase, Augmentir’s embedded AI transforms outdated processes into intelligent workflows—helping organizations streamline deployment, scale training, and drive continuous value.

Let’s explore how.

1. Content Conversion & Creation

Laying the Foundation for Frontline Intelligence

Deploying modern Connected Worker software delivers ROI through a combination of features that support data collection, compliance, auditability, and data visibility for enabling continuous improvement.

For all companies, the Connected Worker journey begins with transforming legacy documents—SOPs, work instructions, training manuals, checklists, videos, and more—into structured, digital-ready formats that support the requirements above.

This means converting thousands (to tens of thousands) of legacy documents trapped in PDFs, Word docs, Excel files, Powerpoints, videos, and proprietary systems, all while trying to preserve their structure, intent, and compliance relevance. In the past, this process was typically manual, becoming one of the costliest and longest phases in the Connected Worker journey.

Augmentir, even prior to Generative AI (GenAI) era, has been a pioneer in providing tools to significantly reduce this effort even while meeting the three fundamental requirements of conversion:

Maintain the integrity of the form design: the format of legacy documents did not arrive by chance, rather they were the result of serious considerations for human factors. Arbitrarily changing these designs results in significant retraining of the frontline workforce, loss of usability, and potentially, significant operational qualification costs in regulated use cases.
Simplify the addition of data collection: make incorporating data collection throughout the form simple, efficient, and smart. Data collection offers the opportunity for smart validation, data driven conditional workflows, escalation, and continuous improvement.
Embed granular event tracking: Connected Worker ROI depends on the ability to provide compliance and auditability, which is enabled through granular event tracking. Additionally, continuous improvement relies on smart, digital “time and motion” data, enhanced through AI.

Today, general purpose GenAI tools offer the tantalizing vision that they can transform this process by accelerating the conversion of legacy digital content into, structured content suitable for Connected Worker use cases. There are examples where GenAI has been demonstrated as a general purpose solution to performing these conversions. Unfortunately, in each of the examples that Augmentir has reviewed, these tools met none of the fundamental conversion requirements.

Included in Augmentir’s suite of Industrial GenAI tools, is Augie™ Content Assistant, which is purpose-built for Connected Worker use cases. Augie has been built with domain specific tooling which, when combined with advanced large language models (LLMs), delivers intelligent content that meets the foundational requirements of the Connected Worker space.

With the Augie Content Assistant, Augmentir turns the challenge of content digitization into a fast, AI-driven advantage:

91% faster content conversion: What used to take hours now takes just minutes to convert a single document. When scaled across an organization’s entire repository of SOPs, work instructions, training materials, and more, this reduces the overall content conversion effort from months down to days.
Multi-format compatibility: Word, Excel, PowerPoint, PDFs, images, and narrated videos are seamlessly converted into native Augmentir content, complete with data collection and high resolution embedded event tracking.
Fidelity preserved: Structure, logic, and intent are maintained through AI-optimized prompts. This ensures that converted content mirrors the original format workers are familiar with—helping reduce change fatigue and making it easier for frontline teams to adopt digital workflows with minimal disruption.
Instant translation: Localize content quickly without compromising compliance or clarity.

This is more than digitization—it’s intelligent transformation that enables data collection, auditability, and operational scalability from day one.

2. Worker Onboarding & Training

Adaptive Support That Accelerates Learning

Once your content foundation is in place, the next challenge is onboarding and upskilling your workforce. Traditional methods are static, generic, and fail to reflect real-time needs.

With Augmentir’s Augie Training Assistant and AI Agents, onboarding becomes a personalized, dynamic experience:

Tailored workflows: New hires are guided step-by-step based on their role, skill level, and assigned tasks.
Training in the flow of work: Instruction happens in real time, with guidance delivered during actual task execution.
Digital assistants ensure worker safety and compliance: Workers have natural language access to digital assistants to guide and support them while they work—enforcing safety, quality, and best practices.
Continuous adaptation: Augmentir’s True Opportunity™ AI continuously monitors skilling and reskilling performance and adjusts training content based on worker feedback and progression.

This ensures faster ramp-up times, better comprehension, and more confident employees from day one.

3. Operational Excellence

Smarter, Safer, More Productive Frontline Work

With your workforce engaged and trained, the next step is supporting them during everyday operations. Here, Augmentir’s AI becomes a digital copilot—delivering real-time, personalized support in the flow of work.

With your workforce engaged and trained, the next step is supporting them during everyday operations. Here, Augie Work Assistant, which is fully customizable and extensible by customers, becomes a role-specific digital copilot—delivering real-time, personalized support in the flow of work.

Key capabilities include:

Task-specific guidance: Augie delivers insight tailored to the person, the task, and the real-time conditions.
Role-Specific Digital SME: The Augie Work Assistant can be configured at the role level, provides always-on expertise that is tuned to needs to each person in each role—answering questions, flagging issues, and improving accuracy.
AI-powered integration: Embedded AI APIs automate documentation, resolve issues in real time, and enable intelligent quality checks.

The result? Fewer errors, faster resolutions, and safer, more confident workers.

4. Continuous Improvement

Closing the Loop with Actionable Insights

Continuous improvement (CI) doesn’t just happen—it requires the right data, insights, and tools to identify what matters most. Augmentir empowers teams to move from guesswork to precision by surfacing opportunities directly from real-world operations.

AI-powered tools include:

True Proficiency: Aligns training and performance data to uncover skill gaps and enhance learning programs.
True Opportunity: Pinpoints the highest-impact areas for improving quality, productivity, and safety.
Augie Data Assistant: Enables natural-language queries to quickly reveal trends, inefficiencies, or outliers.
Augie Content Assistant: Capture & Convert tribal knowledge embedded in informal collaboration into formal, reusable SOPs.
Augie Autonomous Agents: Automate repetitive tasks, monitor KPIs, and trigger proactive CI actions.

Together, these tools ensure that continuous improvement isn’t a periodic initiative—it’s built into the fabric of daily operations.

AI That Delivers Real-World Value

Augmentir’s AI doesn’t just digitize work—it reimagines it. By embedding intelligence throughout the entire Connected Worker Journey, the platform empowers frontline teams to:

Onboard faster
Work safer and smarter
Learn continuously
Drive real business outcomes

With Augmentir, companies move beyond digital transformation toward AI-driven workforce transformation—turning every worker into a connected, empowered, and continuously improving contributor to operational excellence.

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The Great Resignation in Manufacturing

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Recently, Augmentir completed a rigorous qualification audit as part of a Tier 1 Pharmaceutical Manufacturing company’s Good Manufacturing Practice (GMP), and we are pleased to announce that our product successfully passed the audit.

February 17, 2022

A recent article published by The Washington Post shows some shocking numbers on the amount of Americans leaving their jobs over the past year. It’s no surprise that hotel and restaurant workers are resigning in high numbers due to the pandemic, but what is surprising is the fact that the manufacturing industry has been hit the hardest with “a nearly 60 percent jump” compared to pre-pandemic numbers. This “Great Resignation in Manufacturing” is the most of any industry, including hospitality, retail, and restaurants, which have seen about a 30% jump in resignations.

However, if you dig deeper, this trend isn’t new. This recent increase in job quitting in manufacturing has simply magnified a problem that had already been brewing for years, even prior to the start of the pandemic. In fact, in the four years prior to the pandemic (2015-2019), the average tenure rate in manufacture had decreased by 20% (US Bureau of Labor Statistics).

This accelerating workforce crisis is placing increased pressure on manufacturers and creating significant operational problems. The sector that was already stressed with a tight labor market, rapidly retiring baby-boomer generation, and the growing skills gap is now facing an increasingly unpredictable and diverse workforce. The variability in the workforce is making it difficult, if not impossible to meet safety and quality standards, or productivity goals.

Manufacturing leaders’ new normal consists of shorter tenures, an unpredictable workforce, and the struggle to fill an unprecedented number of jobs. These leaders in the manufacturing sector are facing this reality and looking for ways to adjust to their new normal of building a flexible, safe and appealing workforce. As a result, managers are being forced to rethink traditional onboarding and training processes. In fact, the entire “Hire to Retire” process needs to be re-imagined. It’s not the same workforce that our grandfather’s experienced, and it’s time for a change.

The Augmented, Flexible Workforce of the Future

The reality is that this problem is not going away. The Great Resignation in manufacturing has created a permanent shift, and manufacturers must begin to think about adapting their hiring, onboarding, and training processes to support the future workforce in manufacturing – an Augmented, Flexible Workforce.

What does this mean?

It means adopting new software tools to support a more efficient “hire to retire” process to enable companies to operate in a more flexible and resilient manner.
It means starting to understand your workforce at an individual level and using data to intelligently closes skills gaps at the moment of need and enables autonomous work.
And it means taking advantage of data. More specifically, real-time workforce intelligence that can provide insights into training, guidance, and support needs.

Investing in AI-powered connected worker technology is one way to boost this operational resiliency. Many manufacturing companies are using digital Connected Worker technology and AI to transform how they hire, onboard, train, and deliver on-the-job guidance and support. AI-based connected worker software provides a data-driven 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.

As workers become more connected, manufacturers 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. Today’s workers embrace change and expect technology, support and modern tools to help them do their jobs.

To learn more about how AI is being used to digitize and modernize manufacturing operations, contact us for a personalized demo.

AI Manufacturing 2021 – Event Recap

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After more than a year of virtual conferences, we were finally able to participate in person at the AI Manufacturing conference in Dallas early this November and discuss how AI is shaping the future of the manufacturing workforce.

November 17, 2021

After more than a year of virtual conferences, we were finally able to participate in person at the AI Manufacturing conference in Dallas early this November. This year’s event was hybrid, face-to-face on November 3 and 4th, and virtually on November 5th. While it was refreshing to be able to network face to face with leaders in the Manufacturing industry, it was great to have the opportunity to also network virtually on November 5th. If you aren’t familiar with the AI Manufacturing conference, this conference is the leading Artificial Intelligence event for Manufacturing Industries. This year’s event focused on:

The use of AI to Improve Quality, Reduce Defects, and Increase Profits
Developing a Digital Twin to Optimize Plant Operations
Using Building Blocks to Modernize Manufacturing Activities and Facilitate Growth
Designing Products Enabled by Additive and Hybrid Manufacturing Techniques
Exploring the Use of AI in Industrial Attacks and Defense

Using AI to Unlock the True Potential of Today’s Modern, Connected Workforce

Dave Landreth, Augmentir’s Head of Customer Strategy had the opportunity to present on “Using AI to Unlock to the True Potential of Today’s Connected Workforce”. In this session, he discussed the variability of the workforce with generations, how they need to be trained differently, and how AI can assist in worker proficiency. Dave also discussed Bob Mosher’s 5 moments of need and how AI can be applied at the time of learning.

The Misunderstood Fear of AI

Our founders saw that the humanistic approach was missing with traditional connected worker platforms and realized that AI was the key to saving the manufacturing world and unlocking worker potential. However, companies are reluctant to adopt AI in fear that automation will take over and eventually replace human workers in manufacturing. Others fear that AI would be used negatively to track workers, in a “big brother” type of way.

As we’ve seen with our customers, this couldn’t be farther from the truth. When AI is leveraged ethically with the workforce in mind, it can be used to help improve and ultimately grow the talent of your workers. Assessing workers on their performance has been done for years through subjective performance reviews. Using AI allows the assessments to be based on data and can provide a path forward for worker improvement and continued growth.

Understanding Today’s Struggles Within Manufacturing

The struggles that manufacturers face today aren’t the same struggles that were present 40 years ago. One of the number one issues in manufacturing is hiring. Today, most manufacturers believe that hiring is a risk, with a limited pool of candidates. They are struggling with employees who don’t have the needed skill set and are questioning how they can train them and evaluate their performance.

Manufacturing companies also struggle with retaining employees. We are all aware of the workforce retention issues right now. Employees are feeling like they aren’t heard and that they can’t contribute to the company, which causes them to look for a new career. There is also the struggle of thoughtful upskilling, meaning that formal training programs only recognize one type of worker. The average manufacturing plant sees 4 generations of workers, ranging from those fresh out of high school to the ones that have worked on a plant floor for 40+ years. Different generations learn differently and require different levels of support. There isn’t a one size fits all approach for teaching different generations.

Another challenge with the workforce that isn’t as obvious, is with mergers and acquisitions. An acquisition means that companies now consist of two workforces doing things differently and needing to understand what part of procedures from the newly acquired company is worth incorporating into the existing procedures.

Leveraging AI to Help Build and Grow a Top Performing Workforce

AI is uniquely suited to solve these challenges, and we recognized that early on at Augmentir. We started looking at how AI could help build and grow a top performing workforce. One way AI can help is the ability to hire for potential by increasing the hiring of candidates to those not as skilled. AI allows companies to understand a worker’s skillset and provides the ability for personalized workflows to guide them in the context of work while they are doing their job, whether it’s a new worker or one with dozens of years of experience. AI can also help with the “Right person – Right Job – Right Time” approach – always ensuring that the correct person is performing the task at the most efficient time.

The use of AI allows all workers to contribute by allowing inline feedback to optimize work procedures. In addition, AI can be used to ensure personalized career job competency allows workers to be hired even if they do not have the optimal set of skills and experience. Measuring a worker’s proficiency when they are completing the work allows the worker to focus on each specific step and guides them at the time of need, instead of during classroom training. AI provides workers with predictive and stable data to help them grow in their roles. Having a data-driven way to measure success and provide advancement opportunities helps establish career paths as well as opportunities to grow.

With an AI-based onboarding approach, organizations are able to hire a wider range of individuals with varying skill sets. If we can teach someone in the context of doing their work, onboarding time is reduced due to being able to train them in the field. We also see an increase in productivity and are constantly evolving their learnings. When workers feel included and confident about their careers, they are also more likely to want to stay and grow with the company. The ability to train workers in the field while doing their jobs with AI personalization allows you to clearly and quickly assess how a worker is doing, where you focus the help to them, and driving those 1:1 work procedures is a game-changer.

AI in Manufacturing will solve many of the challenges that we are seeing.

Learning & Development and the 5 Moments of Need

The Five Moments of Need methodology was created by Bob Mosher, a thought leader in learning and development with over 30 years of experience. He realized that after 20 years, classroom teaching was the wrong approach since it rarely teaches you things that you do in your job on the shop floor. Classroom learning allows an individual to gain a certain level of confidence, but quickly falls off when it’s time to apply it within context to a given workflow.

According to Bob’s methodology, the 5 moments when our workforce needs knowledge and information consists of:

When people are learning how to do something for the first time (New).
When people are expanding the breadth and depth of what they have learned (More).
When they need to act upon what they have learned, which includes planning what they will do, remembering what they may have forgotten, or adapting their performance to a unique situation (Apply).
When problems arise, or things break or don’t work the way they were intended (Solve).
When people need to learn a new way of doing something, which requires them to change skills that are deeply ingrained in their performance practices (Change).

The approach that Bob and his team adopted in the last 10 years is to think more about performance support. The variability of the workforce, both skilled and young, proves that there’s not a one size fits all approach. This is where AI comes in: being able to deliver personalized work procedures for every worker, allowing for continuous learning and growth. Based on proficiency, there may be a more guided set of work instructions, a session with a remote expert, or a supervisor sign-off required in order to complete the job on quality and on schedule. AI can also be used to continuously measure and assess how the workers are doing. This is where organizations can start seeing growth within their workforce.

Looking Ahead

We had a blast at this year’s AI Manufacturing conference and are already looking forward to another successful event next year! If you’re interested in learning more about why AI is an essential tool in digital transformation, from reducing costs and downtime to improving over quality and productivity, we’d highly suggest considering attending next year. In the meantime, if you’re looking for information surrounding AI, digital transformation, and building a connected workforce, check out our eBook: “Building a Modern, Connected Workforce with AI”.

Factory Agents: The Next Evolution in Manufacturing, Powered by Augmentir

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Discover how AI Factory Agents from Augmentir are transforming manufacturing with real-time insights, automation, and workforce augmentation.

May 31, 2025

In the evolving landscape of Industry 4.0, popularized by Klaus Schwab, and now Industry 5.0, manufacturers are under increasing pressure to become more agile, resilient, and efficient. Amid labor shortages, shifting customer expectations, and digital disruption, one of the most transformative tools emerging is Factory Agents: smart, context-aware AI agents capable of autonomously performing tasks, surfacing insights, and augmenting human decision-making.

What are Factory Agents?

Factory agents are not physical robots, nor are they just software scripts. They’re intelligent, digital entities — powered by AI — that act on behalf of manufacturing teams to interpret data, automate actions, and optimize workflows. They serve as proactive copilots on the shop floor, embedded into the frontline work environment, continuously learning from human activity and contextual factory data to provide real-time support and operational insights.

These agents can assist with:

Recommending optimized workflows
Identifying skill gaps or training needs for frontline workers
Monitoring process performance and flagging anomalies
Automatically capturing tribal knowledge
Personalizing work instructions based on the worker’s experience and certification level

In short, factory agents bridge the gap between human intelligence and machine efficiency on the shop floor — and Augmentir is leading the charge.

Augmentir’s AI Agent Studio: Manufacturing Intelligence Made Easy

While agentic AI the concept of AI agents has existed in other sectors, Augmentir is the first to bring a no-code Industrial AI Agent Studio purpose-built for manufacturing. This unique platform allows operations leaders, supervisors, and even non-technical users to create and deploy custom AI agents tailored to specific needs across:

Workforce onboarding and training
Maintenance and repair operations (MRO)
Quality assurance
Safety procedures
Performance monitoring

These agents are powered by proprietary algorithms and generative AI that continuously learn from your workforce and operations data. That means over time, the agents get smarter — making increasingly precise recommendations, automating more tasks, and reducing variability across the shop floor.

The result? An adaptive, intelligent frontline that can respond dynamically to production demands, labor variability, and skill shortages.

Meet Augie: The Face of Next-Gen Industrial AI

At the core of Augmentir’s AI capabilities is Augie — an Industrial Generative AI assistant built specifically for frontline manufacturing environments. Augie acts like a real-time guide and operational partner for shop floor workers, supervisors, and even plant managers.

Here’s what makes Augie different:

Context-aware assistance: Augie understands the unique context of your operation — such as a specific piece of equipment, a shift schedule, or a worker’s skill level — to tailor guidance appropriately.
Conversational interface: Workers can interact with Augie naturally through chat, enabling real-time Q&A, issue resolution, or step-by-step guidance.
Continuous learning: As workers interact with Augie, it learns and improves, capturing undocumented knowledge and institutionalizing best practices across the organization.

Rather than replacing workers, Augie amplifies their capabilities, making everyone on the shop floor more confident, capable, and productive.

Real-World Impact: Augmentir in Action

Companies using Augmentir have reported measurable improvements across multiple KPIs:

20–40% reduction in training time by personalizing learning to individual skill levels
30% improvement in first-time quality through smarter digital work instructions
25% gain in workforce productivity due to real-time guidance and fewer delays
Stronger worker retention through empowered learning and growth pathways

In a time when manufacturers are grappling with a skills gap, labor shortages, and increased demand for agility, these outcomes are game-changers.

Why Factory Agents Are Defining the Future of Industrial Work

The traditional shop floor has been defined by rigid systems and static processes. But today’s manufacturers need more flexibility — they need systems that adapt to shifting demand, dynamic labor pools, and constant process change.

AI factory agents offer this adaptability and with Augmentir’s Industrial AI platform, manufacturers can unlock this potential without a massive overhaul or technical burden.

Factory agents represent a new class of industrial tools — intelligent, autonomous, and human-centric. As the first platform to bring this vision to life, Augmentir is not just building tools, but reshaping how manufacturing work gets done.

With Augie and the AI Agent Studio, Augmentir is helping manufacturers step into a new era of operational excellence — where the frontline is not just automated, but truly augmented.

Learn more about how Augmentir’s AI shop floor agents can modernize your operations – contact us today for a live demo.

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Generative AI in Manufacturing: Benefits, Risks, and Top Use Cases

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Explore top use cases for generative AI in manufacturing, how GenAI copilots and digital assistants work, and benefits for frontline workers.

June 17, 2024

Generative AI in manufacturing refers to the application of generative models and artificial intelligence techniques to optimize and enhance various aspects of the manufacturing process.

While traditional AI focuses on data analysis, pattern recognition, and decision-making, generative AI creates new content and synthetic data, enabling innovative solutions. This involves using AI algorithms to generate new product designs, optimize production workflows, predict maintenance needs, and improve production efficiency within frontline operations.

According to McKinsey, nearly 75% of generative AI’s major value lies in use cases across four areas: manufacturing, customer operations, marketing and sales, and supply chain management. Manufacturers are uniquely situated to benefit from generative AI and it is already a transformative force for some. Generative AI is driving innovation and efficiency across the manufacturing sector, enabling advanced digital solutions and competitive advantages. A recent Deloitte study found that 79% of organizations expect generative AI to transform their operations within three years, and 56% of them are already using generative AI solutions to improve efficiency and productivity.

Manufacturing is rapidly evolving and by integrating cutting-edge technologies like Generative AI, manufacturers can better support, augment, and enhance their frontline workforces with improved decision-making, collaboration, and data insights. Gen AI is being adopted as a modern alternative to traditional methods, surpassing manual inspections and basic automation to deliver greater operational improvements.

Join us below as we dive into generative AI in manufacturing exploring how it works, the benefits and risks, and some of the top use cases that generative AI, specifically generative ai digital assistants, can provide for manufacturing operations:

What is Generative AI in Manufacturing
Benefits of Leveraging Generative AI in the Manufacturing Industry
Risks of Generative AI in Manufacturing
Top Use Cases of Generative AI Manufacturing Assistants
Future-proofing Manufacturing Operations with Augie™

What is Generative AI in Manufacturing

Generative AI refers to artificial intelligence systems designed to create new content, such as text, images, or music, by learning patterns from existing data. In manufacturing, this includes the ability to generate new product designs and create synthetic data, such as realistic images, videos, or text, to support manufacturing innovation and AI training. The use of Large Language Models (LLMs) and Natural Language Processing (NLP) enables these systems to analyze vast amounts of data, leveraging advanced algorithms and machine learning algorithms to improve prediction accuracy and operational efficiency, simulate different scenarios, and generate innovative solutions that can impact a wide range of manufacturing processes.

Large Language Models

Large Language Models (LLMs) are a type of generative artificial intelligence model that have been trained on a large volume – sometimes referred to as a corpus – of text data. They are capable of understanding and generating human-like text and have been used in a wide range of applications, including natural language processing, machine translation, and text generation.

In manufacturing, generative AI solutions should leverage proprietary fit-for-purpose, pre-trained LLMs, coupled with robust security and permissions. Industrial LLMs use operational data, training and workforce management data, connected worker and engineering data, as well as information from enterprise systems. LLMs can also enhance document search by efficiently finding, extracting, and summarizing information from technical manuals, reports, and operational records.

Natural Language Processing

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves the development of algorithms and models that enable computers to understand, interpret, and respond to human language in a way that is both meaningful and useful.

For generative AI, NLP is a key technology that enables the assistants to understand and generate human-like text, providing seamless conversational user experiences and valuable assistance to frontline workers, engineers, and managers in manufacturing and industrial settings.

NLPs allow the AI to process and interpret natural language inputs, enabling it to engage in human-like interactions, understand user queries, and provide relevant and accurate responses. This is essential for common manufacturing tasks such as real-time assistance, documentation review, predictive maintenance, and quality control.

By combining large language models and natural language processing, generative AI can produce coherent and contextually relevant text for tasks like writing, summarization, translation, and conversation, mimicking human language proficiency. NLP also enables interactive learning experiences, allowing employees to engage with training content, receive immediate feedback, and clarify doubts in real time.

Benefits of Leveraging Generative AI in the Manufacturing Industry

Generative AI and solutions that leverage them offer several benefits for manufacturing operations, including:

Operational/Production Optimization and Forecasting: GenAI technology offers a significant boost to manufacturing processes by monitoring and analyzing in real-time, spotting problems quickly, and providing predictive insights and personalized assistance to boost efficiency for manufacturing workers. Through process optimization and enhancing efficiency with real-time data analysis and automation, manufacturers can streamline operations, reduce downtime, and improve productivity. Additionally, AI assistants empower manufacturers to explore multiple control strategies within their process, identifying potential bottlenecks and failure points.
Proactive Problem-Solving: Generative AI-powered tools provide real-time monitoring and risk analysis of manufacturing operations, enabling the quick identification and resolution of issues to optimize production and efficiency. They can spot events as they happen, providing valuable insights and recommendations to help operators and engineers rapidly identify and resolve problems before they escalate. Predictive analytics and improved quality control help reduce waste and support continuous improvement in manufacturing processes.
Reduce Unplanned Downtime: Generative AI solutions can analyze vast datasets to predict equipment maintenance needs before issues arise, allowing manufacturers to schedule maintenance proactively, minimizing unplanned disruptions. Generative AI can also optimize maintenance schedules and delivery schedules to further reduce downtime and improve supply chain reliability. This not only improves downtime but also contributes to the overall operational resilience of mission-critical equipment.
Personalized Support and On-the-job Guidance: Generative AI tools can be tailored to diverse roles within the manufacturing plant, offering personalized assistance to operators, engineers, and managers. It can provide role-based, personalized assistance, and proactive insights to understand past events, current statuses, and potential future happenings, enabling workers to perform their tasks more effectively and make better, more informed decisions. GenAI solutions and applications involved implementing generative AI provide optimized parameters for operators and help manage inventory more effectively.

These benefits demonstrate the significant impact of generative AI on frontline manufacturing activities, improving overall operational efficiency, adjusting processes where needed, and driving operational excellence.

Pro Tip

Generative AI assistants can take these benefits one step further by incorporating skills and training data to measure training effectiveness, identify skills gaps, and suggest solutions to prevent any skilled labor issues. This guarantees that frontline workers have the essential skills to perform tasks safely and efficiently, while also establishing personalized career development paths for manufacturing employees that continuously enhance their knowledge and abilities.

Risks of Generative AI in Manufacturing

Generative AI in manufacturing presents several risks, including data security, intellectual property concerns, and potential bias in AI models. The reliance on vast amounts of data raises the risk of data breaches and cyberattacks, potentially exposing sensitive information. Intellectual property issues may arise if AI-generated designs or processes inadvertently infringe on existing patents or proprietary technologies. Additionally, biases in training data can lead to suboptimal or unfair outcomes, affecting the quality and equity of AI-driven decisions. There is also the risk of over-reliance on AI, which may reduce human oversight and lead to errors if the AI models make incorrect predictions or generate flawed designs. Ensuring proper validation, transparency, and human intervention is crucial to mitigating these risks.

The use of any genAI tool in manufacturing requires careful consideration of ethical, data privacy, and security risks, as well as potential impacts on employment.

Top Use Cases for Generative AI Manufacturing Assistants

Generative AI assistants and frontline copilots are AI-powered tools designed to provide valuable assistance and insights in industrial settings, particularly in manufacturing. These assistants are a type of generative AI that are used in manufacturing operations to enhance human-machine collaboration, streamline workflows, and offer proactive insights to optimize performance and productivity for frontline workers. The manufacturing sector is being transformed by these advanced AI applications, which are driving efficiency, innovation, and better decision-making across the industry.

What makes frontline AI assistants unique among other generative AI copilots is the enhanced human-like interaction beyond standard data analytics and analysis to understand the context around a process or issue; including what happened and why, as well as anticipate future events.

Generative AI assistants work via specialized large language models (LLMs) and generative AI, providing contextual intelligence for superior operations, productivity, and uptime in industrial settings. Additionally, they typically involve natural language processing for understanding human language, pattern recognition to identify trends or behaviors, and decision-making algorithms to offer real-time assistance. This, combined with machine learning techniques, allows them to understand user inputs, provide informed suggestions, and automate tasks. AI and machine learning are used together in manufacturing to automate defect detection and optimize supply chains, further enhancing operational efficiency.

Here are 6 of the top use cases for generative AI in manufacturing:

1. Troubleshooting

Troubleshooting is such a critical use case in manufacturing. With today’s skilled labor shortage, frontline workers are often times in situations where they don’t have the decades of tribal knowledge required to quickly troubleshoot and resolve issues on the shop floor. AI assistants can help these workers make decisions faster and reduce production downtime by providing instant access to summarized facts relevant to a job or tasks, this could come from procedures, troubleshooting guides, captured tribal knowledge, or OEM manuals.

2. Personalized Training & Support

With GenAI assistants, manufacturers can instantly close skills and experience gaps with information personalized, context-aware to the individual worker. This could include: on the job training materials, one point lessons (OPLs), or peer/user generated content such as comments and conversations.

3. Leader Standard Work

With Generative AI assistants, operations leaders can assess and understand the effectiveness of standard work within their manufacturing environment, and identify where there are areas of risk or opportunities for improvement.

4. Converting Tribal Knowledge

One of the more pressing priorities that many manufacturers face is the task of capturing and converting tribal knowledge into digital corporate assets that can be shared across the organization. With connected worker technology that utilizes Generative AI, manufacturing companies can now summarize the exchange of tribal knowledge via collaboration and convert these to scalable, curated digital assets that can be shared instantly across your organization.

5. Continuous Improvement

AI and GenAI assistants can help us identify areas for content improvement, and make those improvements, measure training effectiveness, and measure and improve workforce effectiveness.

6. Operational Analysis

Generative AI assistants can also provide value when it comes to operational improvements. GenAI assistants can use employee attendance data to help shift managers or line leaders determine where the risks are, and potentially offset any resource issues before they become truly problematic. An organization’s skills matrix, presence data, and production schedules all can feed into a fit-for-purpose, pre-trained LLM – giving you information that manufacturing leaders need to keep their operations running.

Generative AI and other AI-powered solutions are leveling up manufacturing operations, analyzing data to predict equipment maintenance needs before issues arise, allowing for proactive maintenance scheduling, and minimizing unplanned disruptions. With these tools manufacturers can empower frontline workers with improved collaboration and provide real-time assistance with contextual information, ensuring relevant and timely support during critical decision-making processes.

Overall, generative AI is transforming a wide array of manufacturing and industrial activities, connecting workers in ways that were previously thought impossible, and making frontline tasks and processes safer and more efficient for workers everywhere.

Future-proofing Manufacturing Operations with Augie™

Augie™, Augmentir’s generative AI assistant for frontline work, represents the next generation of generative AI solutions, purpose-built to help manufacturing companies future-proof their operations. By harnessing the power of artificial intelligence and machine learning, Augie enables manufacturers to optimize production processes, improve quality control, and reduce maintenance costs—all while adapting to rapidly changing market demands.

With Augie, manufacturers can analyze vast amounts of data from diverse sources, including machine data, sensor data, and historical data, to identify patterns and make predictive, data-driven decisions. This advanced platform delivers real-time insights into production processes, allowing manufacturers to quickly respond to shifts in demand, supply chain disruptions, or operational anomalies. Augie also features sophisticated algorithms for demand forecasting, inventory management, and supply chain optimization, helping companies minimize environmental impact and maximize operational efficiency.

Augie pulls in skill capabilities, workforce development information, and training data in addition to MES and ERP data. It offers contextual, proactive insights and automated workflows to optimize production and prevent bottlenecks, contributing to manufacturing efficiency, uptime, quality, and decision-making.

Additionally, Augie ties together operational data, training and workforce management data, engineering data, and knowledge/information from various disparate enterprise systems to empower frontline workers, streamline workflows, and increase manufacturing performance.

By integrating Augie into their operations, manufacturers can boost productivity, reduce unplanned downtime, and achieve significant cost savings. The platform’s AI-driven quality control ensures improved product quality, while its customer service automation capabilities enhance responsiveness and satisfaction. Ultimately, Augie empowers manufacturing companies to stay ahead of the competition, adapt to evolving industry trends, and secure a sustainable, competitive advantage in the global marketplace.

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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Paperless Manufacturing: Your Guide to Transitioning to a Paperless Factory

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Learn how to digitize your operations and build a paperless factory in this paperless manufacturing guide from Augmentir.

September 20, 2023

Manually managing and tracking production in manufacturing has become a thing of the past. That’s because manufacturers are adopting a new digital approach: paperless manufacturing.

Paperless manufacturing uses software to manage shop floor execution, digitize work instructions, execute workflows, automate record-keeping and scheduling, and communicate with shop floor employees. More recently, this approach also digitizes skills tracking and performance assessments for shop floor workers to help optimize workforce onboarding, training, and ongoing management. This technology is made up of cloud-based software, mobile and wearable technology, artificial intelligence, machine learning algorithms, and advanced analytics.

More recently, your journey to paperless manufacturing is being accelerated through the availability of generative AI assistants and supporting import tools that can streamline the conversion of existing content into interactive, mobile-ready content for your frontline teams.

Paperless manufacturing software uses interactive screens, dashboards, data collection, sensors, and reporting filters to show real-time insights into your factory operations. If you want to learn more about paperless manufacturing processes, explore this guide to learn about the following:

What is a paperless factory?
What are the benefits of going paperless in manufacturing?
How do you go paperless in manufacturing?

What is a paperless factory?

A paperless factory uses AI-powered software to manage production, keep track of records, and optimize jobs being executed on the shop floor. Paperless manufacturing is intended to replace written record-keeping as well as paper-based work instructions, checklists, and SOPs, and keep track of records digitally.

For example, in most manufacturing operations, everything from quality inspections to operator rounds and planned and autonomous maintenance is done on a regular basis to make sure factory equipment is operating properly and quality and safety standards are met. In most manufacturing plants, these activities are done manually with paper-based instructions, checklists, or forms.

Operators and shop floor workers in paperless factories use software to execute work procedures and see production tasks in ordered sequences, which enables them to implement tasks accordingly. Workers are able to view operating procedures, or digital work instructions, using mobile devices (wearables, tablets, etc.) in real-time.

Furthermore, paperless manufacturing incorporates the digitization of shop floor training, skills tracking, certifications, and assessments. This digital approach uses skills management software helps optimize HR-based processes that were previously managed via paper or spreadsheets, and includes the ability to:

Create, track, and manage employee skills
Instantly visualize the skills gaps in your team
Schedule or assign jobs based on worker skill level and proficiency
Close skill gaps with continuous learning
Make data-driven drive operational decisions

What are the benefits of going paperless in manufacturing?

There are a number of reasons for factories to go paperless, from cost-effectiveness to increased productivity and sustainability. A paperless system can revolutionize production processes, workforce management, and business operations.

Here are the top benefits of going paperless:

Accelerate employee onboarding: By digitizing onboarding and moving training into the flow of work, manufacturers can reduce new hire onboarding time by 82%.
Increase productivity: Digitizing manufacturing operations means no more manual, paper-based data collection or record-keeping. Workers have more time to run their equipment, execute shop floor tasks, and find solutions to problems.
Boost data accuracy: People are prone to making mistakes, but shop floor data capture and validation can help offset human error and improve accuracy.
Improved workforce management: Digital skills tracking and AI-based workforce analytics can help optimize production operations and maximize worker output.
Manage real-time operations: Human-machine interface systems eliminate the need for paper, files, and job tickets. This means that workers can analyze inventory and other data in real-time.
Save money: Although going paperless means that the cost of paper is eliminated, the savings extend beyond that. With greater productivity, operations in real-time, and improved production optimization, costs can be reduced in many areas.

How do you go paperless in manufacturing?

Going paperless starts with digitizing activities across the factory floor to increase productivity, and extending that value through a digital connection between the shop floor and enterprise manufacturing systems. We lay out below the four basic steps for how to go paperless in manufacturing:

Step 1: Digitize your existing content with Gen AI and Connected Worker technology.

Paperless manufacturing starts with the use of modern, digital tools that can quickly and easily digitize and convert your existing paper-based content. Tools like Augmentir’s Augie™, a generative AI suite of technologies, helps you import and convert existing content regardless of format. Once converted, Connected Worker solutions that incorporate enhanced mobile capabilities and combine training and skills tracking with connected worker technology and on-the-job digital guidance can deliver significant additional value. A key requirement to start is to identify high-value use cases that can benefit from digitization, such as quality control or inspection procedures, lockout tagout procedures, safety reporting, layered process audits, or autonomous maintenance procedures.

Pro Tip

You can now import existing PDF, Word, or Excel documents (just like the PDF above) directly into Augmentir to create digital, interactive work procedures and checklists using Augie™, a Generative AI content creation tool from Augmentir. Learn more about Augie – your industrial Generative AI Assistant.

Step 2: Augment your workers with AI and Connected Worker technology.

AI-based connected worker solutions can help both digitize work instructions and deliver that guidance in a way that is personalized to the individual worker and their performance. AI Bots that leverage generative AI and GPT-like AI models can assist workers with language translation, feedback, on-demand answers, access to knowledge through natural language, and provide a comprehensive digital performance support tool.

As workers become more connected, companies have access to a rich source of job activity, execution, and tribal data, and with proper AI tools can gain insights into areas where the largest improvement opportunities exist.

Step 3: Set up IoT sensors for machine health monitoring.

The industrial Internet of Things (IoT) uses sensors to boost manufacturing processes. IoT sensors are connected through the web using wireless or 4G/5G networks to transmit data right from the shop floor. The use of machine health monitoring tools along with connected worker technology can provide a comprehensive shop floor solution.

Step 4: Connect your frontline to your enterprise.

Digitally connected frontline operations solutions not only enable industrial companies to digitize work instructions, checklists, and SOPs, but also allow them to create digital workflows and integrations that fully incorporate the frontline workers into the digital thread of their business.

The digital thread represents a connected data flow across a manufacturing enterprise – including people, systems, and machines. By incorporating the activities and data from these previously disconnected workers, business processes are accelerated, and this new source of data provides newfound opportunities for innovation and improvement.

Augmentir provides a unique Connected Worker solution that uses AI to help manufacturing companies intelligently onboard, train, guide, and support frontline workers so each worker can contribute at their individual best, helping achieve production goals in today’s era of workforce disruption.

Our solution is a SaaS-based suite of software tools that helps customers digitize and optimize all frontline processes including Autonomous and Preventive Maintenance, Quality, Safety, and Assembly.

Transform how your company runs its frontline operations. Request a live demo today!

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