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The current COVID-19 crisis has impacted almost every industry worldwide, forcing businesses to implement work from home strategies, discontinue travel, and scale back on operations. Industrial and manufacturing businesses have been particularly affected by this due to reduced frontline worker availability, supply chain issues and the need to create social distancing workplaces for the health and safety of workers while also keeping them connected.

But in an industry that relies on the bulk of its operations to be carried out on-site, how can manufacturing businesses remain at capacity and keep frontline workers engaged during a time of remote working and travel restrictions?

According to Forbes, many leading manufacturing businesses are looking to Connected Worker technology to help manage these issues and keep up with operations. Through the use of augmented reality (AR) combined with artificial intelligence and machine learning (AI/ML), a connected worker platform allows manufacturers to take advantage of digital information to carry out tasks on site and complete jobs. Here are 3 major ways connected worker technology is helping keep manufacturing businesses stay afloat during COVID-19:

1. Flexible work arrangements that support social distancing
   As a result of COVID-19, manufacturers have initiated policies that encourage remote working, eliminate non-essential travel and instruct employees who are sick to stay home until they are better. This directly impacts the progression of operations that typically take place when multiple workers collaborate onsite. With on-demand remote expert functionality, onsite workers can quickly pull in an offsite colleague when their expertise is required. Procedures, videos, real-time data and more are readily available to intelligently guide and help workers on the job.
   
   
2. Reduced complexity and downtime
   Manufacturers can expect a learning curve as they assess strategies and implement new ways to work more at home and less on the shop floors. Using a connected worker platform allows companies to pivot quickly in order to retain the workforce and reduce downtime during learning periods. Frontline workers are able to receive fully augmented, guided instructions on any device to improve productivity, quality and customer satisfaction.
   
   
3. Gather insightful data to track progress during the crisis with AI
   It’s critical for manufacturers to monitor processes and track progress during this time so that they can make quick adjustments to ensure overall optimization. Incorporating AI into an AR strategy goes one step further by enabling true organizational optimization using the rich stream of activity data to recommend improvement actions to frontline workers, continuous improvement specialists, trainers, manufacturing engineers, and operation and service managers.

As this pandemic crisis continues to spread worldwide, manufacturers will likely continue to face challenges and look to connected worker technology to keep their workers safe and their businesses operating. It’s also important to recognize this as a major turning point in the way frontline workers interact and perform daily operations in the future. 

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 Artificial Intelligence (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 artificial intelligence 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 artificial intelligence 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.

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, and, according to Deloitte, manufacturing is already looking at a potential skilled labor shortage of 2.4 million workers in the next decade. 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 and require a well-planned digital transformation strategy to keep them competitive.

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.”