The growing skills gap in the manufacturing industry, combined with a tight labor market, is creating increased challenges for manufacturing companies of all sizes. In fact, in 2019 more than 25% manufacturers had to turn down new business opportunities due to a lack of workers, according to a report from the National Association of Manufacturers (NAM).

What’s behind this growing skills gap problem?

One key factor is the extraordinary number of retiring workers who are walking out the door with vast amounts of experience and skills. Unfortunately, for most manufacturers the knowledge possessed by this senior workforce has yet to be captured in any digital or electronic format. At the same time, a younger, more tech-savvy generation of unskilled workers is coming into the market. They may have the attitude and aptitude, but lack the skills required to effectively participate in day-to-day operations.

This lack of a skilled frontline workforce is creating an increased focus for manufacturing companies on training and up-skilling their workers. A recent article highlighted some staggering statistics on what lies ahead. According to the Manufacturing Institute: “Manufacturers are set to spend $26.2 billion on internal and external training initiatives for new and existing employees in 2020 to combat the shortage of available workers. Nearly 70% of manufacturers said they are creating or expanding training programs for their workforce. Three-quarters of respondents said upskilling workers helped to improve productivity, promotion opportunities and morale.”

Using Artificial Intelligence to Close the Skills Gap

Fortunately, manufacturers are turning to emerging digital technologies to equip and train their workforces with the tools and knowledge needed to be productive. Technologies such as mobile and wearable devices, augmented and mixed reality (AR/MR), and artificial intelligence (AI) are helping to connect a new generation of workers, and are allowing organizations to proactively deliver the right level of support and guidance.

One of the most notable examples of technology adoption is using artificial intelligence to augment human activity in manufacturing.

Artificial Intelligence has been branded as a threat to replace the human workforce, but some leading manufacturing companies are turning to AI as a way to help onboard and train new workers.

Artificial Intelligence is increasingly being used to augment, not replace, the human workforce. AI is uniquely able to address the fundamental macrotrends of growing skills gaps and the loss of tribal knowledge.

Supported by an increased level of connected-ness of today’s frontline workers, AI systems are capable of taking in large amounts of data and finding correlations and patterns that can be used to help improve productivity, enhance skills, and provide more cost-effective, targeted training. With an ecosystem of content authors, frontline workers, subject matter experts, operations managers, continuous improvement engineers, and quality specialists, there are dozens of opportunities to address the skills gap, improve quality, and improve performance.

Using AI to Reduce Training Time

In one example, Bio-Chem Fluidics, a manufacturer of high performance pumps and valves for clinical diagnostics and analytical chemistry applications, is using AI combined with digital work instructions to improve the onboarding and training process for their new technicians and operators. One of the most significant impacts were achieved with training and onboarding new operators.

According to Bio-Chem, the company’s training time for temp workers has been reduced by over 80%.

“Augmentir has made our complex procedures very repeatable for operators of all skill levels. As a result, our training time for new operators has been reduced by over 80%. The flexibility and ease-of-use of the Augmentir platform have made it painless to implement across our company.”

Linsey Holden-Downes, Vice President of Operations at Bio-Chem

The company uses Augmentir’s AI-powered connected worker platform to digitize and standardize their work instruction library, and leverages Augmentir’s AI to deliver insights that are helping them optimize their training efforts.

After adopting Augmentir, it now takes their team lead roughly two weeks to fully train a new hire whereas prior to Augmentir, it would have taken three months of supervision. Additionally, the time spent monitoring new hires is dramatically reduced from an estimated 50% of a team lead’s time to just 10%.

Using AI to Augment Digital Work Instructions

Like Bio-Chem, many manufacturing companies rely on knowledge that is either recorded on paper or trapped in the heads of their senior workforce. With the increase in skilled technicians that are retiring, this is creating an urgency for companies to act.

STRONGARM, a Pennsylvania-based manufacturer, recently dealt with these issues. STRONGARM designs and builds ergonomic and environmentally protected workstations for companies in a wide range of markets, including food, pharmaceutical, CPG, packaging, and transportation. In recent years, the company faced growing challenges within its operation – an aging and retiring workforce, talent shortage, and increased competition – which increased pressures to produce high quality products at lower costs.

STRONGARM’s initial focus was on the assembly and final quality control processes for the company’s most complex workstation and industrial display unit. Augmentir’s rapid authoring environment allowed STRONGARM to quickly migrate their existing paper-based instructions to digital, augmented instructions that incorporated rich media, checklists, verifications, and several other features that were central to their assembly and QC processes.

According to Steve Thorne, General Manager at STRONGARM, “Augmentir’s AI-based ‘True Opportunity’ system enables us to gain insight into how our technicians are performing, and autonomously identifies our largest capturable opportunities across our entire operation.”

“When one of our senior and most experienced technicians retired recently, we were able to onboard a new technician and trust Augmentir’s AI engine to guide him during the learning curve to get product out the door at 100% quality so that we didn’t miss shipments. Once Augmentir’s AI engine determined that the worker had become proficient, it recommended that the instructions should be adjusted to enable him to complete the job faster while still meeting quality and safety goals.  This has resulted in a 20% reduction in average build time in our most complex workstations.”

Steve Thorne, General Manager, STRONGARM

The level of personalization that an AI-based system can deliver to work procedures and instructions allows companies to not only address initial skill gaps but also deliver continual improvements over time.

Intelligently supporting workers in real-time with remote experts and AI-bots

The benefits that AI can bring to industrial companies are not limited simply to standard operating procedures, work instructions, or training. Companies are also turning to AI as a way to intelligently guide and support frontline workers with real-time decision support. Connected workers are increasingly relying on “Remote Expert” capabilities to leverage the expertise of senior colleagues for on-the-job troubleshooting and problem solving. As we look ahead, AI-bots will be able to capture tribal knowledge of these subject matter experts during remote expert sessions, and intelligently guide workers that are experiencing the same situations.

Addressing the Skills Gap with Augmentir

At Augmentir, our AI identifies patterns and generates insights based on analyzing data from connected workers. These insights improve worker performance as well as provide positive impact on training, operational workflows, and quality.

Digital Work Instructions help guide connected workers with visual aids and augmented with AI-driven insights and contextual information enable workers to perform at their best.

Integrated Remote Expert assistance helps workers resolve issues faster using insights from Augmentir’s AI and information from the guided procedure.

Augmentir’s AI uses granular data  to identify the largest opportunities in improving the skills of the frontline workforce, and helps to drive continuous improvement throughout the organization.

If you’d like to see how our AI is able to help your organization address the skills gap by and uncover and rank “True Opportunities” across your workforce, sign up for a free 30-day trial of Augmentir.

Gartner recently published their annual Hype Cycle for Manufacturing Operations Strategy and Innovation in Manufacturing Industries. These two Hype Cycles focus on the leading-edge technologies and methodologies that will significantly change how manufacturers innovate, deliver and support products and services.

Augmentir was recognized as an important player in both Hype Cycle reports.

Emerging Technologies in Manufacturing

According to the report, “manufacturers that adopt the breakthrough opportunities presented as innovation profiles on this Hype Cycle will accelerate digitalization that increases the agility and ability to innovate products and business operations”.

These Hype Cycles, which reflect the convergence of the physical and virtual worlds, present several key mature and emerging technologies, including the following:

  • Connected Worker: Connected workers use digital technologies to improve and integrate their interactions with both physical and virtual surroundings. Through the use of digital work instructions, as well as augmented/mixed reality delivered through mobile/wearable devices, they are able to make faster and better decisions that optimize and improve a process or workflow that they participate in.
  • Immersive Experiences – Manufacturing Operations: According to Gartner, immersive experiences refer to enabling the perception of being physically present in a nonphysical world or enriching people’s presence in the physical world with content from the virtual world. The report covers three kinds of immersive experiences: Augmented Reality (AR), Mixed Reality (MR), and Virtual Reality (VR).
  • Machine Learning: Machine learning is a technical discipline that identifies patterns and generates predictions based on analyzing large sets of data. According to Gartner’s research: “In manufacturing operations, machine learning (ML) can take advantage of available data and rely on the algorithms to identify patterns and correlations. It can also use them to predict outcomes, to find the best course(s) of action and to control processes. Typical use cases include eliminating unplanned downtime and stoppages, increasing yield optimization, reducing energy usage, improving product quality or stabilizing production processes.”

Augmentir is mentioned in the context of Connected Workers and Immersive Experiences in Manufacturing Operations as software and solution provider.

Augmentir’s Approach to Connected Workers and Immersive Experiences in Manufacturing

Augmentir sits and the intersection of these innovation profiles, using artificial intelligence and machine learning (AI/ML) to amplify the value that digital technologies bring to the connected worker.

Digital Work Instructions help guide connected workers with visual aids and augmented with AI-driven insights and contextual information enable workers to perform at their best.

Integrated Remote Expert assistance helps workers resolve issues faster using insights from Augmentir’s AI and information from the guided procedure.

Augmentir’s AI uses granular data  to identify the largest opportunities in improving the skills of the frontline workforce, and helps to drive continuous improvement throughout the organization.

At Augmentir, our AI identifies patterns and generates insights based on analyzing data from connected workers. These insights improve worker performance as well as provide positive impact on training, operational workflows, and quality. According to Gartner, “The possibility of predicting performance is extremely attractive for manufacturers. This is driving the strong interest in ML. ML is an essential enabler of artificial intelligence (AI), smart factories and intelligent automation”.

Our view at Augmentir is that the purpose of a connected worker platform isn’t simply to deliver instructions and remote support to a frontline worker, but rather to optimize the performance of the connected worker ecosystem. Artificial intelligence is uniquely able to address the fundamental macrotrends of growing skills gaps and the loss of tribal knowledge. With an ecosystem of content authors, frontline workers, subject matter experts, operations managers, continuous improvement engineers, and quality specialists, there are were dozens of opportunities to improve performance.

If you’d like to see how our AI is able to uncover and rank “True Opportunities” across your workforce and improve overall worker performance or drive continuous improvement, sign up for a free 30-day trial of Augmentir.

Artificial Intelligene

This post by Augmentir CEO Russ Fadel was originally published on Medium.

I have been a fan of Marc Andreessen since the Netscape days — he has consistently predicted the macro changes in numerous marketscapes before virtually anyone else. Recently, I was watching Marc on Youtube “Why You Should Be Optimistic About the Future” and found his discussion on AI particularly enlightening, and in complete alignment with Augmentir’s journey. The entire video is worth watching, but the discussion on AI runs from between the 7:00 to 9:00 minute mark.

Some of the most insightful (paraphrased) quotes include:

  • “There is a more fundamental question — is AI a feature or an architecture?”
  • “A16z sees this with most start-up pitches now — ‘here are the 5 things my product does…and oh yeah, AI is always bullet number 6.’ Number 6 because it was the bullet they added after they created the deck”
  • “If AI is a feature, then this is correct, where every product will have AI sprinkled on it.”
  • “We (a16z) believe AI is an Architecture, and if it is, everything above this will need to be rewritten.”
  • “Ultimately, the goal of AI is to answer questions, even before the have been posed.”

At Augmentir we had to make a strategic decision at the time of company founding (late 2017), as to whether AI was going to be a feature of our connected worker platform or, whether it was going to be the architecture that our connected worker functionality ran on. We didn’t frame the decision as elegantly as Marc did, but we nevertheless asked, “will AI be a feature of our product or will it be pervasive?”

Even though no one in our space had chosen this path, we decided AI would be pervasive. We postulated that the purpose of a connected worker platform wasn’t to deliver instructions and remote support to a frontline worker, but rather to optimize the performance of the connected worker ecosystem. We knew that AI was uniquely able to address the fundamental macrotrends of growing skills gaps and the loss of tribal knowledge. With an ecosystem of content authors, frontline workers, subject matter experts, operations managers, continuous improvement engineers, and quality specialists, we predicted that there were dozens of opportunities to improve performance.

By building our connected worker platform on an AI architecture, all data is automatically pipelined, labelled, and cleansed, and is immediately available to start generating insights and recommendations. On this journey, the scope of what we can use AI for has even surprised us. Our initial thoughts were on personalizing instructions and content to make each frontline worker perform this current task safely and as quickly as they can, given their current proficiency. This immediately expanded to a generalized True Opportunity™ system that uses AI to stack rank where an organization has the largest capturable opportunities across all stakeholders. The range of this is astounding: which jobs have the largest monthly opportunity, which workers can benefit from targeted training, what is the optimum time to perform any given task, what inline training material can benefit from an update, what content/procedures would benefit the most from an update, etc.

The future looks even more fantastic — AI bots offer a realistic opportunity to capture tribal knowledge and convert it to a scalable corporate asset, and AI Diagnostic bots to make everyone an immediate expert.

This is only possible when you view AI as an architecture, not as a feature.

If you’d like to see how the AI architecture of our connected worker platform is able to uncover and rank “True Opportunities” across your workforce and improve overall worker performance or drive continuous improvement, sign up for a free 30-day trial of Augmentir.


enterprise ar

Recently, I co-founded Augmentir with three key executives from my prior start-up, ThingWorx. ThingWorx was the company that created the Industrial IoT application platform category and was arguably the most successful start-up in the IIoT space, getting acquired by PTC in early 2014. The acquisition of ThingWorx was the first, non-distressed acquisition in the space and directly led to the wave of acquisitions that have reshaped the IIoT landscape.

Prior to founding Augmentir, I did a fairly deep dive into the marketecture of Enterprise AR and found that it reminded me of the early IIoT space — fragmented with lots of companies occupying niches:

  • Custom solution builders around Smart Glasses
  • Vertical Solution Builders
  • Smart Glass vendors
  • Technology providers
  • And more

Gartner’s recent release of the IIoT “Magic Quadrant” was both reaffirming and disappointing. It was personally reaffirming to see that ThingWorx, under the leadership of Jim Heppelmann, CEO of PTC, occupies the “Most Magic” position in the IIoT space. However, it was disappointing to see that after ten years, the IIoT space has not yet “crossed the chasm” as evidenced by the fact that no vendor is in THE Magic Quadrant.

Why would IIoT, a space that has so many compelling ROI stories, still be stuck between early adopter and mainstream? I have a point of view on this — while IIoT solutions can be extremely valuable and transformative, they have to be easy to own in order to achieve mainstream adoption. IIoT is anything but “easy to own”: this starts with the high friction, traditional enterprise sales process, long pilots, expensive, “Value-based” pricing, long, risky implementation cycles, and vendor lock-in/high switching costs. This makes it hard for even large enterprise businesses to fully adopt, and given these dynamics, it’s easy to understand how Small and Mid-sized businesses have been essentially locked out of the IIoT opportunity.

Unfortunately, in Enterprise AR I see the same dynamics unfolding. High friction sales and POCs, combined with long implementation cycles and high prices, will keep Enterprise AR mired in the early adopter phase. Already, sales of Smart Glasses for the enterprise are hugely disappointing, signaling that the market is unfolding much more slowly than analysts have projected. This will disappoint many investors and dash the hopes of many startups who believe in market projections, not realizing that they are a key part of the problem.

Certainly, we need a new direction if Enterprise AR has any hope of ever crossing the chasm and achieving mainstream adoption.

If you’re ready to test out Augmentir’s Connected Worker platform and start leveraings AR and AI to continually improve the productivity of your frontline workforce, sign up for a free 30-day trial.

field service

“There is an increasing pressure on the sector to make the most out of every technician.”

“The impact of lost knowledge and customer relationships built over the years and decades by retiring technicians is keeping service leaders up at night.”

“Many companies have not been able to capture their ‘tribal knowledge’ in a systematic way, risking the loss of valuable insight into service operations.”

These quotes come from “The Future of Field Service”, a February 2018 article in Field Technologies Online. Of course, they also could have been quotes from a 2008 or even earlier version of the article. Why is it that these problems that have been considered significant issues by Field Service executives are not solved and still considered problems year after year? Are they simply intractable problems that have no solution? Perhaps the answer to these questions is hidden in another quote from the article:

“The core of field service, the technician’s visit, is the aspect least addressed by field service management solutions.”

To date, everything before and after a site visit is digitized and chronicled to great extent, but much of what goes on during the visit is still very much a “black box”. Sure, there are now Remote Expert video based collaboration tools that may allow recording of a session, but what if the person on site IS the expert and doesn’t need to make that call? In addition, these solutions don’t capture what went on before or after the the call. What did the tech do that lead up to the call? Without that information we (a) put the expert at a disadvantage because they have no context to help solve the problem and (b) fail to capture the tribal knowledge of what NOT to do, or understand the common mistakes that might lead to difficulties in the field.

From my early days working on Internet based Remote Service, first with Questra and then with ThingWorx, I have seen many companies that have tried to address the Tribal Knowledge issue in many ways. Knowledge Management systems, social networking tools, video chat sessions, etc. have all been moderately successful at best, and usually at very high cost. The reason for this is that they largely relied on “after the fact” documentation. Asking the tech to remember everything that happened while on-site (while they are rushing off to the next job) is often a lesson in futility.

So, what is the answer? How do we break open that black-box? To quote from the article once again:

“It seems so paradoxical that so few field service management solutions focus on these aspects of field service”

Some folks have seen IIoT as a solution, letting the equipment itself collect and send data. While this is certainly helpful, it doesn’t reveal the true story of what the tech is experiencing onsite. Others have said that the aforementioned video collaboration tools are the answer, but again, there is the critical before and after the call information that is missing. And mixing Social Networking and people heading towards retirement is almost never a good idea(!).

So what is the answer? Are we destined to forever be wandering around the darkened room of the customer site visit with a blindfold on? At Augmentir we think perhaps not. But much more on that later…

If you’re ready to start addressing the skills gap problem within your organization and want empower your workforce with the tools they need to continually improve the productivity and quality of work, sign up for a free 30-day trial of Augmentir. Test out the easiest connected worker platform on the market at no risk and start creating sustainable value throughout your manufacturing organization.

Observations from EWTS 2018
Last week Augmentir exhibited at the Enterprise Wearable Technology Summit (EWTS) in Austin, Texas. The EWTS conference is one of the longest-running events dedicated to Wearables and Augmented/Virtual/Mixed Reality, and this year’s attendance was very rich. Hundreds of attendees came to the show, with many decision-makers and senior managers in the mix.

As one of about 40 exhibitors, Augmentir spoke to dozens of customer prospects that stopped by the booth. Interestingly, all of the companies we spoke with had very unique use cases and requirements, and were early in their exploratory phase in terms of adopting AR and Wearable technology within their enterprise – further reinforcement that, although the momentum is building, the market is early and much friction still exists within the adoption lifecycle.

Another clear indicator of momentum was the number of formal announcements made at the show this year. From new partnerships to global deployments, there was lots of great news for the enterprise wearable technology space.

The keynote speech by Jay Kothari of Google Glass was yet another barometer of the change that’s happening in the wearable industry, as the Glass team shared insights on recent product introductions and scale-ups.

A primary takeaway from this speech – and the overall show – is that while broad industry trends are driving this technology transition, every customer is unique, and each organization will have its own, individual challenges as it converts. Suppliers need to be prepared to support those challenges as true technology adoption occurs. It will take strong partnerships between technology providers and company stakeholders to successfully deploy and scale-up new projects.

Many of these themes and key takeaways from the event further reinforced our belief that adoption of Enterprise AR solutions will only be able to reach scale once much of the existing friction is removed, and solutions are delivered that focus on an amazing end-user experience. This is what we are focusing on here at Augmentir, and we’ve been very encouraged by the customer validation thus far. Throughout this time of technology transition, as the Glass team advocated, it’s more important than ever to focus on the user. Once you understand their needs, their challenges and their opportunities, everything else will surely follow.

To learn how Augmentir’s platform leverages AR and AI to continually improve the productivity of your frontline workforce download our free white paper, “Rise of the Augmented Worker.”


Ready to start creating sustainable value within your organization? Sign up for a free 30-day trial of Augmentir and experience how we are helping industrial and manufacturing companies to empower their workforce with the tools they need to continually improve the productivity and quality of work.

digital transformation


Much has been said and written about Digital Transformation. Much less has actually been done about it.

Gartner defines Digital Transformation as “the process of exploiting digital technologies and supporting capabilities to create a robust new digital business model”. What isn’t said, and often not well understood, is that there are two fundamental aspects to Digital Transformation:

  1. The first is that all processes must be connected from end-to-end, with no digital air gaps. In this “Digital Thread” all links in the operations flow are seamlessly handed off from one system to another. In manufacturing this means from order receipt through shipment there is no paper in the process. Similarly in service operations, from ticket inception to closeout, there is no need for paper.
  2. The second is that all of the processes need to be both instrumented and agile. Instrumented so that you can get the data needed to apply Artificial Intelligence and Machine Learning (AI/ML) analysis to them, and agile so that you can improve them continuously over time.

Limitations of Automation in Manufacturing and Service

In high volume manufacturing scenarios, one of the unheralded benefits of automation has been to close the air gap between systems. Automation creates a machine-to-machine interaction without human intervention all the while generating the data required to feed to AI/ML systems. However, many business processes include activities that still heavily rely on humans to be accomplished and will for quite some time. Whether it is because the activity requires human dexterity, such as some assembly and QA procedures on a factory floor, or complex decision making, such as a field service engineer diagnosing a mechanical problem at a customer site. The costs, complexities, and capabilities of automation often make it infeasible for a large number of manufacturers and OEMs.

This creates a large barrier to Digital Transformation because a key component of digital transformation, the data regarding these operations, is only sparsely available. While high-level data, such as cycle time and yield on the factory floor or after the fact site visit write-ups by a service engineer, often exist, the detailed activities of the human worker are still a veritable “black box”. This is especially challenging for small to mid-sized businesses that overwhelmingly rely on human activities.

So where does that leave these companies — do they have no hope of leveraging AI/ML to continuously improve their business?

Closing the Air Gap of Human Operations

This is where Augmentir™ comes in. Our Connected Worker platform breaks down that barrier to true Digital Transformation. Augmentir not only gives companies a way to rapidly author augmented work instructions but, when those instructions are executed by humans, very fine-grained data about the process steps – the tools being used, content being accessed, and results being generated – are captured in a non-intrusive manner and then fed back into the appropriate enterprise systems. Simply put, Augmentir closes the air gap of human operations, enabling humans, and the work they do, to become a fully integrated part of the digital thread.

Augmentir also solves the second, even more challenging aspect of Digital Transformation. It has has been estimated that data scientists spent 80% of their time cleaning and labeling data. Collecting and making sense of the sparse and noisy data streaming back from human activities has been a huge barrier to taking advantage of the advances in AI/ML technology to optimize human-centric operations. The Augmentir platform not only collects the fine-grained data, but cleanses, labels it, and then presents it to our embedded AI engine to develop unique insights into your operations. These insights help organizations identify where opportunities for improvement exist. This is the virtuous cycle of continuous improvement that is the hallmark of a “digitally transformed” organization.

Are you interested in learning how your organization can leverage augmented work procedures to close the air gap of human operations and continuously improve processes? Schedule your personalized demo and see Augmentir in action. 

Ready to Dive In?

If you’re ready to test out all of the features of the easiest connected worker platform on the market and start creating sustainable value throughout your manufacturing organization, sign up for a free 30-day trial of Augmentir.

digital transformation strategy

There have been countless changes in technology over the past couple of decades: Machine Learning, Cloud Computing, Internet of Things, Artificial intelligence, and Augmented Reality (to name a few). But with all of these advances in technology, the 350 million workers in manufacturing are being asked to perform increasingly complex jobs using technology that has remained relatively unchanged for 20 years. Whether this is because enterprise software solutions are expensive, technically complex, difficult to implement, or lack continuous improvement opportunities, these users and processes have been underserved.

Although there has been a recent trend towards a digital transformation that looks at applying new technologies to improve operational processes, the workers who actually perform these processes are not being considered. Because of this, the frontline worker is largely disconnected from the digital thread of the business and improvement in productivity seems stagnant.

Key Challenges Manufacturers Face Today

As with any transformational change, adopting a digital transformation strategy is no easy undertaking. We currently see 4 key challenges industrial organizations face when adopting a digital transformation strategy:

1.) Tribal Knowledge and the “Skills Gap”
Senior production workers and subject matter experts have accumulated valuable experience and knowledge, which has been typically hard to capture and convert into an asset that is able to be easily shared and used by others. The younger workforce that is entering the manufacturing sector does not have the knowledge that their senior peers have, but are expected to perform the same jobs, at the same level of productivity and quality.

2.) Lack of Insight
Lack of insight into how workers are performing their jobs on a day-to-day basis is also an issue. There is no fine-grained detail regarding worker activity – how are workers performing vs. benchmarks, are they having trouble on certain steps, what are they doing well, do they have feedback on operational procedures that could help the rest of the workforce? This lack of data and insight has made it extremely difficult to improve the performance of frontline workers. As a result, there is little or no basis for making decisions for improvement across the organization.

3.) Lack of Guidance and Accurate Information
Organizations are struggling with the quality of human-centric processes, as they often suffer from inaccurate, outdated paper-based work instructions. In many cases productivity is also an issue because workers are not equipped with the right tools or instrumented with the appropriate guidance that would help them perform their jobs at peak productivity.

4.) Workers are Disconnected
And lastly, frontline workers are not integrated with their work environment. The human-centric and job-specific workflows are not digitally integrated into the overall business environment and enterprise systems (ERP, CRM) that are critical to the business. The reality of today’s frontline workforce in manufacturing is that workers are not connected to the digital fabric of the business.

Bridging the Digital-Reality Gap

The good news is that there are a number of new strategies and technologies that manufacturing organizations are implementing to solve these problems. In particular, the rise of Enterprise Augmented Reality has lead to a major shift in improving the productivity of the frontline workforce of manufacturing organizations.

Although this is a great first step, Enterprise Augmented Reality alone isn’t enough to deliver sustainable value in manufacturing. In order to see true transformational results, it is key to have a combination of the following:

  • Enterprise AR: Delivers initial improvements in productivity and quality for the frontline workforce.
  • Consumerization of Software: Enables ease-of-use and ubiquity across the manufacturing landscape.
  • Artificial Intelligence: Drives continuous improvement throughout the organization.

Only when these three elements are combined will you see continuous improvements in the productivity of your frontline workforce.

To learn how Enterprise Augmented Reality, Artificial Intelligence, and the Consumerization of Software are delivering transformational value in manufacturing download our white paper, “The Rise of the Augmented Worker.”


Ready to Dive In?

If you’re ready to start addressing the skills gap problem within your organization and want empower your workforce with the tools they need to continually improve productivity and quality, sign up for a free 30-day trial of Augmentir. Test out all of the features of the easiest connected worker platform on the market at no risk and start creating sustainable value throughout your manufacturing organization.