The augmented worker | Move

The augmented worker

by Renaud Pawlak - April 12, 2019

The nature of work is changing. Artificial intelligence (AI), robotics, virtual and augmented reality are becoming more sophisticated each day and revolutonizing almost every type of job, giving rise to what have been dubbed “augmented workers”. This trend looks set to accelerate in the future, meaning that organizations need to redesign jobs to take advantage of these new, transformative technologies and ensure that employees are not only more productive, but also happier in their work.

There are different types of “non-human” workforce that can be leveraged to accomplish an increased number of tasks, depending on an organization’s business strategy and needs. But a future augmented workforce is one that mobilizes all elements of the existing workforce, allowing both humans and machines to do what they do best. However, this is not simply a matter of reallocating tasks: it is a profound shift in the way we conceive work.

Outsourcing repetitive and strenuous tasks

Robotics and other cognitive technologies have proved themselves to be particularly useful in tasks that are physically demanding or repetitive for humans (for instance, assembly lines). Where this isn’t already the case, it is safe to assume that low added value tasks for humans will be entirely automated in the not too distant future.

It is not so much about working less as it is about working better: freed from these uninteresting, monotonous tasks, we will be able to focus on, and increasingly leverage, core human skills such as creativity, project management, ethical and strategic decision-making, communication, negotiation and problem-solving. This shift towards the more human aspect of work will also result in a real productivity boost as well as increased job satisfaction.

Managing the transition to automation

Many organizations are currently undergoing this transformation. During this important transition period, it will be crucial to maximize the value generated by automation without losing sight of both the short- and long-term effects that decisions made today will have on the company. There are a number of things that need to be taken into account in order for the transformation to be successful: first of all, employees need to be properly trained in these new technologies (while making these technologies more “employee-friendly” at the same time). That said, this exercise implies rethinking the role of employees altogether, redefining the workplace so that is more digital and collaborative, and in fact redesigning job functions themselves so that they are more appropriate for uniquely human skills.

The focus will also shift towards customer and employee experience and care, as well as company attractiveness for employees. Businesses that decide to automate, for example, while not providing their employees with the opportunities to retrain or move to a different position may see their image suffer – both internally and externally.

One sector where robot process automation (RPA) has been very successfully integrated into the workforce is, for instance, the banking and insurance industry. The processes followed are extremely standardized, and machine and human work is now effectively complementary here, with the role of the cashier becoming increasingly varied: cashiers are now able to devote more time to selling or advising customers on products rather than simply performing routine transactions such as transfers.

Artifical Intelligence and decision-making

However, RPA can be taken even further and used as an analysis tool as well. At the Mantu R&D Lab, we work across several technologies, including artificial intelligence and automation, and we are currently researching how RPA could be applied to web listening (real-time, constant web monitoring, which in turn can become an invaluable tool for online sentiment analysis and digital marketing ).

Such tools can also help with decision-making by speeding up information processing. Applied to financial analysis, Watson – IBM’s AI program – has shown that it can analyze and extract crucial information from a 150-page annual report in just minutes, whereas it would take an expert several hours to do so. The analyst is thus free to focus on making critical decisions based on the essential data in the report. In the age of Big Data, being able to process and analyze large amounts of complex information at speed is becoming a necessity for businesses, and in fact for all organizations, regardless of their sector. Algorithms can shed light on processes and correlation or causality patterns ‘hidden’ in a mountain of data, and effectively invisible to the human eye. In a way, they can help us make sense of the incredibly vast amont of data we generate on a regular basis, highlighting trends, spotting changes, uncovering recurring issues. This is an invaluable resource for decision-making.

Radio Frequency Identification technology and its applications

Yet another sector that has long benefited from automation to increase productivity is the logistics industry, with particular focus on stock management. Radiofrequency Identification (RFID) technology, for instance, is now routine for supply chain optimization.

A RFID chip allows an item to be detected, tracked and located in real time by connecting it to the organization’s cloud. Unlike bar codes, which are ‘read only’ (they have to be scanned to provide the product information), RFID chips can send information as radio waves to be then received and decoded by RFID readers. International sports retail giant Decathlon chose to RFID tag its entire stock, allowing it to instantly track 98% of its products and complete an inventory in just a few hours.

RFID technology could also be used to improve customer experience at a sales point– for instance by instantly tracking products coming off the shelves and into a customer’s shopping basket. Checkout-less supermarkets are currently being pioneered by Amazon, whose ‘Amazon Go’ stores were designed to allow customers to simply ‘check in’ on their Amazon Go app when thet enter the premises, take what they need without scanning any items, and simply walk out having their shop authomatically billed to the credit card associated with the user account on the app. It is not exactly clear whether Amazon is using RFID technology to do this, but it is a possibility.

The augmented worker: AR and VR

Although these immersive technologies are not yet widespread, their market is expected to grow substantially in the next 4-5 years. According to data published by magazineTechCrunch, the combined AR/VR market is set to reach up to $115bn by 2023.

Augmented reality (AR) and virtual reality (VR) have different uses: on a production line, for instance, AR can help guide technicians and optimize operators’ productivity by displaying instructions or construction plans to one side in their line of vision through a headset, goggles or a display. This strategy allowed Boeing to increase its technicians’ productivity by 40% while reducing production time by a quarter.

VR, on the other hand, can be used to provide an overview of a task, better visualize certain details of a product or even interpret an individual’s behaviour by tracking eye movement. At the Mantu Lab, we are currently undertaking such tracking tests to help our Recruitment department. We observe the specific points where the eye rests on a CV, for example, and then a software searches for an answer on the Internet related to the required information.

VR can also be used to help train employees in risky environments, such as building sites or nuclear plants. Colas, a subsidiary of French industrial group Bouygues, used this technology to help its teams familiarize themselves with the security measures in place on its construction sites. Another example is US retail giant Walmart, who trained its entire workforce using VR headsets.In this case, VR helped  democratize skills development, since the “Walmart Academies” training program was previously only available to managers.

Going mainstream

There is still much to do, however, before such procedures go beyond “proof of concept” and become mainstream. These technologies need to be perfected and rolled out at large scale, and it is for this reason that we have created a dedicated Innovation cluster at Mantu, including our Research and Development Lab, our start-up incubator Studio, and our Innovation Factories.

The future of work is not some distant reality, it is already with us, and organizations need to take advantage of the opportunities it offers. The changes underway today will continue, and at an ever-increasing rate: early adopters (those who will introduce and start optimising AI, robotics and other cognitive technologies) will quickly gain a competitive advantage, leaving slow movers behind.

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