Discover how AI Factory Agents from Augmentir are transforming manufacturing with real-time insights, automation, and workforce augmentation.
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.
https://www.augmentir.com/wp-content/uploads/2025/05/factory-agents-ai-in-manufacturing.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2025-05-31 11:40:432025-06-10 16:00:11Factory Agents: The Next Evolution in Manufacturing, Powered by Augmentir
Explore top use cases for generative AI in manufacturing, how GenAI copilots and digital assistants work, and benefits for frontline workers.
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:
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.
https://www.augmentir.com/wp-content/uploads/2024/06/generative-ai-in-manufacturing.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-06-17 18:43:542025-06-09 15:26:36Generative AI in Manufacturing: Benefits, Risks, and Top Use Cases
Learn how to digitize your operations and build a paperless factory in this paperless manufacturing guide from Augmentir.
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:
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!
https://www.augmentir.com/wp-content/uploads/2023/09/paperless-manufacturing-augmentir.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2023-09-20 14:12:392025-06-04 13:02:43Paperless Manufacturing: Your Guide to Transitioning to a Paperless Factory
AI and connected worker technology is helping frontline managers combat employee burnout and improve engagement and retention.
Industrial work comes with an immense amount of stress. Without providing the worker with the right level of support, this stress can lead to increased errors, poor work performance, and eventually, employee burnout. Recently, Gallup reported that 76% of employees experience some form of workplace burnout. This not only affects performance and productivity but much more, including engagement and employee retention.
To offset employee burnout, managers should aim to:
Reduce employee stress
Remove roadblocks ensuring their workers have the proper tools to complete their tasks
Ensure workers are a good match skill-wise for the work they are doing
Give workers a say in how the work is completed
Empower workers to believe that the work they are doing is valued and important
In a 2022 Gallup poll, 79% of employees responded as not being engaged at work, this same poll found that most employees don’t find their work meaningful and do not feel hopeful about their careers.
When supporting workers and battling workplace burnout, there is no “one size fits all” answer, and many organizations are realizing that taking the same approach for “desk workers” does not account for the many and uniquely different needs demanded by frontline or “deskless” workers. Managers must keep in mind these needs when combating and detecting burnout and boosting employee engagement in manufacturing.
Artificial Intelligence (AI) and machine learning-based technology combined with a worker-centric approach can help tremendously in this respect, accounting for the human element in industrial operations while still taking advantage of innovations.
Using AI to Enhance Worker Experience and Reduce Burnout
By utilizing the capabilities of connected worker platforms and AI, companies can take a proactive approach to reducing stress and preventing employee burnout.
The meteoric rise of AI tools like ChatGPT and natural language processing has created a surge in interest in all things AI and while it’s not a cure-all, AI has the potential to be extremely effective in helping workers get access to the information and support they need while on the job, as well as predicting, detecting, and reducing workplace burnout. By taking highly granular connected worker data and using AI to filter out the unnecessary portions, industrial operations are able to not only improve tasks and productivity but better support and empower frontline workers. Organizations can use AI to engage employees by:
Creating communication touchpoints and streamlining communication
Pairing workers and tasks based on skill level
Suggesting training and certification opportunities for upskilling workers
Create feedback paths so employees have a say in how tasks are completed
To complement AI and software platforms, organizations can implement other tools such as wearable devices, mental health applications, and more to aid in engagement efforts. Finding the right balance and combination is key for knowledge exchange and conversation – making employees more engaged within the team.
The Human Element
It is important to take advantage of new technologies and implement them where needed, but technology by itself is not the answer. Finding a balance between technology integration and a worker-driven approach is key and it is paramount that the true needs of the workforce are not forgotten. Although AI and machine learning-based technology can help tremendously with detecting and reducing employee burnout, it has its limits and can only do so much. Technology cannot replace how workers feel and how they interact with management on a day-to-day basis. And at the end of the day, AI can only augment employees and should be used to empower them, never to replace them.
https://www.augmentir.com/wp-content/uploads/2023/03/human-side-of-ai.jpg6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2023-03-14 03:05:062025-05-27 16:10:51The Human Side of AI: Supporting Workers to Reduce Employee Burnout
Learn how manufacturers combat the manufacturing skilled labor shortage and close skills gaps with an Augmented Connected Workforce (ACWF).
An Augmented Connected Workforce (ACWF) offers manufacturing and other industrial organizations a powerful solution to combat the ever-worsening skilled labor shortage and skills gap. According to a report by Deloitte and the Manufacturing Institute, an estimated 2.1 million manufacturing jobs could go unfilled by 2030 and the cost of those missing jobs could potentially total $1 trillion in 2030 alone.
By integrating advanced technologies like artificial intelligence (AI), connected worker platforms, and other emerging solutions manufacturers can enhance the capabilities of their existing workforce and bridge skill gaps. Connected worker tools offer real-time monitoring of your frontline workforce, ensuring seamless operations. Moreover, connectivity enables remote collaboration, allowing experts to assist frontline workers from anywhere in the world. This interconnected ecosystem empowers workers with the tools they need to succeed and attracts new talent by showcasing a commitment to innovation and technology-driven growth.
Through an ACWF, manufacturers can effectively combat the manufacturing skilled labor shortage and close the skills gap while driving productivity, innovation, and remaining competitive. Read more about ACWF in manufacturing below:
A critical element of transitioning from a traditional workforce to an Augmented Connected Workforce (ACWF) is implementing and adopting new technologies and processes. Here are a few steps that can help with the adoption of ACWF technologies and smooth transitions in industrial settings:
Step 1: Assess Current Processes – Organizations must understand existing workflows and identify areas where AI, connected worker platforms, and other ACWF technology can replace paper-based and manual processes to enhance efficiency and productivity.
Step 2: Invest in Technology – Procure AI-driven analytics platforms, mobile technology, and wearable technology to enable real-time data collection and remote collaboration.
Step 3: Training and Onboarding – Provide comprehensive training programs to familiarize workers with new technologies and workflows. Emphasize the importance of safety protocols and data privacy.
Step 4: Pilot Programs – Start with small-scale pilot programs to test the effectiveness of the implemented technologies in real-world manufacturing environments. Target high-value use cases that can benefit from a transition from paper to digital.
Step 5: Continuous Improvement – Gather feedback from workers and supervisors during pilot programs and adapt implementation initiatives based on their input. Continuously optimize processes and technologies for maximum effectiveness.
By following these steps, manufacturers can smooth the transition from a traditional manufacturing workforce to an ACWF, empowering their frontline workers with improved capabilities, skills, and overall operational excellence.
Supporting Learning in the Flow of Work
Augmented Connected Workforce (ACWF) technologies allow for increased frontline support and for new processes around learning and training to strategically upskill and reskill, reduce time to competency for new workers, and to combat the skilled labor shortage in manufacturing and more. Connected worker tools, such as wearable devices and IoT sensors, enable real-time monitoring of worker performance and environmental conditions, ensuring safety and efficiency on the factory floor.
An ACWF also allows for improved workflow learning capabilities giving frontline workers access to expert guidance, remote assistance and collaboration, microlearning, and other learning in the flow of work options regardless of the worker’s location.
ACWF tools further enhance frontline activities through:
Digital work instructions and guidance: Smart, connected worker platforms provide digital work instructions, procedures, and visual guidance easily accessible to workers on mobile devices.
Digital mentors and training: Some ACWFs incorporate “digital mentors” – GenAI-powered industrial assistants that can provide step-by-step guidance to workers, especially new hires.
Knowledge capture and sharing: Connected frontline worker applications serve as knowledge sharing platforms, capturing data and insights from frontline workers, which can then be analyzed by AI software and used to improve processes, update work instructions, and share knowledge across the organization
Performance monitoring and feedback: ACWF solutions provide visibility into worker performance, allowing managers to identify areas where additional training or support is needed.
In summary, ACWF initiatives empower frontline workers with the digital tools, knowledge, and support they need to learn and improve their skills directly within their daily workflows, rather than relying solely on formal training programs. This helps close skills gaps and drive continuous improvement.
Future-proofing Manufacturing Operations with an ACWF
Adopting an Augmented Connected Workforce (ACWF) approach centered around augmenting frontline workers with mobile technology, immersive training, collaborative decision-making, and continuous improvement, allows manufacturers to future-proof their operations and gain a sustainable competitive advantage. This concept empowers employees with powerful tools that augment and enhance their capabilities, productivity, and overall business processes by accessing critical information and fostering collaboration
AI-powered software can analyze vast amounts of data to optimize production processes and predict workforce development needs. At the same time, connected frontline worker solutions enable the integration of mobile and wearable technologies and provide real-time data insights, aiding in optimizing factory operations and adapting to evolving industry trends.
For an Augmented Connected Workforce, integrating AI and connected worker technologies serves as a vital strategy for manufacturers navigating the skilled labor crisis. Augmentir encourages organizations to embrace ACWF transformations and expedites adoption through a comprehensive connected worker platform leveraging the combined benefits of connected worker and AI technologies.
With Augmentir, frontline workers can access critical information, real-time data and insights, and expert advice and guidance all in the flow of work preventing lost time and improving both efficiency and productivity. Schedule a live demo to learn more about how an Augmented Connected Workforce future-proofs manufacturing operations and enhances frontline activities.
https://www.augmentir.com/wp-content/uploads/2024/06/augmented-connected-workforce-acwf-manufacturing.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-04-03 16:14:362025-05-22 11:56:03How to Combat the Manufacturing Skills Gap with an Augmented Connected Workforce (ACWF)
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 and tacit 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 Proficiency™ 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.
https://www.augmentir.com/wp-content/uploads/2021/10/this-is-not-your-grandfathers-workforce.jpg6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2021-10-14 16:29:462025-02-07 02:47:12This Is Not Your Grandfather’s Workforce
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.
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 Augie™. 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.
https://www.augmentir.com/wp-content/uploads/2024/04/packaging-industry-connected-workforce.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-04-07 18:34:312025-01-29 12:32:24How AI and the Augmented Connected Workforce is Revolutionizing the Packaging Industry
The evolution of AI in manufacturing has seen tremendous growth over the past few decades, now becoming more adaptive and collaborative, and being used to augment and directly support frontline workers.
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.
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.
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.
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 Opportunity™, 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 2.0™, 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 Productivity™ and True Performance™. 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 Augie™ – 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 Engagement™, 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 industrial transformation partner delivering measurable results across operations. Schedule a live demo today to learn more.
https://www.augmentir.com/wp-content/uploads/2024/03/evolution-of-ai-in-manufacturing.webp6301200Chris Kuntzhttps://www.augmentir.com/wp-content/uploads/2021/09/Augmentir_Logo_Sm-300x169.pngChris Kuntz2024-03-23 02:00:412025-01-13 12:01:40The Evolution of AI in Manufacturing
