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Field Service News, a leading online journal dedicated to the Field Service industry, recently posted an article featuring Augmentir as one of their top three picks for best new solution providers in the Field Service Sector for Enterprise Augmented Reality (AR) powered by AI. Field Service News spoke with field service management professionals and field service solution providers across the globe over a 12 month period to cherry-pick the top three solutions that meet their needs.

What landed Augmentir on this notable list?

1.) Strong Leadership Team
The first reason is the strong leadership team with founding efforts at Wonderware, Lighthammer, and ThingWorx. The Augmentir team has a proven track record delivering industry-leading solutions in the industrial and manufacturing sectors.

2.) AI Powered Approach
In addition, Field Service Now calls out Augmentir for being different from the many Enterprise Augmented Reality providers that have suddenly noticed the potential in the field service industry and says, “the really interesting thing about Augmentir is that they’ve gone far beyond the initial approach that many of their peers are offering when it comes to Augmented Reality (AR) and dived straight into an Artificial Intelligence (AI) powered approach. In their own words, they position themselves as the first software platform built on Artificial Intelligence in the world of the augmented or connected worker.”

3.) Powerful Platform with an Easy-to-Use Interface
Finally, taking an AI approach is important when it comes to the use of AR in Field Service, because when leveraged alongside AI, AR becomes much more useful and powerful. Augmentir is a 100% AI-first company and understands that AR is the interface that makes the most sense for modern field service operations.

About Augmentir

Augmentir is the world’s only Smart Connected Worker Suite. Augmentir is being used by manufacturing and service companies to empower their frontline workers to perform at their best and deliver improvements in safety, quality, and productivity consistently, year-over-year.

Request a live demo today to learn more about why leading manufacturers are choosing our solutions to improve their manufacturing processes.

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.”

Recently, I co-founded Augmentir with three key executives from my prior start-up, ThingWorx, and have been exploring Enterprise AR. 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.