Digital technology has played an important role in the workplace during COVID. Many businesses were forced to adopt a remote or hybrid work environment, while face-to-face businesses had to use technology to adapt their business model to a virtual environment. Post-COVID, this digital transformation will continue to unfold and drive the Fourth Industrial Revolution, a.k.a. Industry 4.0, with significant impact on work paradigms.
The future of work will be shaped by a digital workforce powered by advanced solutions, enabled through artificial intelligence (AI) and machine learning (ML). One of the biggest drivers of these changes will be the maturation and adoption of Digital Twinning technology, a method for creating an exact digital replica of the physical world. This technology allows an operator via the Digital Twin to interact with a physical object, space, or system in real-time.
An example of this would be jet engine repair. With digital twinning technology, the efficiency of repairing and performing preventative maintenance could be greatly improved if I had a service center where specialists could work remotely. These specialists would have instant access to all the real-time diagnostics information for the engine and could engage the engine with repair systems that are connected via digital twinning and advanced robotics solutions.
Remote processing will shift how work is organized and how employers recruit and incentivize the specialized employees needed to perform their work. Workers will no longer be limited by the physical world, hands-on work, and human-oriented process flows. Instead, their work will be gamified, allowing them more ability to expand the value of their craft by becoming more efficient in this environment. This new workforce will be paid based on the volume and quality of their work, influenced by machine learning and AI, rather than just hourly or salary compensation. Ambitious workers will be incentivized to find new and innovative ways to improve the quality and quantity of their work.
Key Enablers of Industry 4.0
Very rarely does one innovation alone enable a drastic value spike within an industry or field of work. Often, the convergence of several independent advancements brings about the major value shift that accelerates adoption and general use of a new capability.
Some of the digital innovations working in tandem to enable what work will look like in the future across many industries, such as manufacturing, health care, and logistics:
On-demand business model. This model relies on elastic service architectures that expand and contract as subscribers come and go limiting the cost exposure to the business operating the service.
Large scale services such as Netflix’s streaming service and Slack’s messaging and collaboration platform are entirely built on Amazon Web Services’ (AWS’s) pay-as-you-go services, taking advantage of the ability to incur costs only when directly tied to revenue and to scale up and down in real time as business expands and contracts.
Service providers operating within these on-demand business models have developed platforms using modern architecture that allows monthly pricing. Providers can substantially reduce costs to customers who commit to a long-term subscription once providers have achieved scale, while also giving customers the freedom to terminate all or a portion of their monthly services without a penalty.
This model has paved the way for many industries to innovate cost effectively and rapidly transform their capabilities, without incurring the high capital costs of large-scale infrastructure once needed.
Cloud services. As on-demand services become more prevalent, traditional data center models are being replaced by cloud service providers that offer on-demand infrastructure and application services. Cloud service providers offer a variety of infrastructure capabilities and highly advanced cybersecurity solutions that can be deployed globally with high levels of reliability and security built in.
Cloud services eliminate the need to plan, purchase, and deploy a massive network with infrastructure requiring constant maintenance and large amounts of staff. Businesses can now focus investment dollars on application services that create and sustain value for their customers.
Solutions such as Amazon’s AWS, Microsoft’s Azure, and Google Cloud lead the pack with a vast array of capabilities, enabling application developers to quickly develop and deploy advanced solutions on a set of iron clad Machine Learning solutions.
Big data solutions. Machine-learning systems have rapidly evolved due to cloud services enabling cost-effective processing and the storage of massive data sets. The development of open-source software that can be quickly trained from these massive data sets have sped up the investment, research, and commercialization of highly sophisticated solutions that can swiftly implement narrow artificial intelligence or machine learning.
Companies like DeepMind on Google Cloud, H20.ai, and Quantiphi running on AWS and Azure have quickly achieved results with machine learning from clients’ big data sets much more efficient and sustainable using a small, organized team of data scientists and data engineers. This has fostered widespread adoption and innovation across the enterprise.
Computer vision. This technology results from big data solutions’ ability to train machines to recognize the physical world with increasing precision with the use of digital image and video libraries. Computer vision is critical to the digital twinning process, which uses remote robotics to understand what the technology is interfacing with and allow the interaction between the worker’s virtual world and the object’s physical world. Computer vision has far surpassed simple identification and can accurately “see” and interact with the physical world in difficult environments, allowing workers to cross the virtual boundary into the physical world.
Samsara and SAS are two examples of companies who have developed sophisticated solutions with integrated services using computer vision.
Artificial Narrow Intelligence (ANI) – Machine Learning. “Narrow” or single-purpose machine learning has quickly become front and center for most major industries and the companies that serve them. Narrow AI, or ANI, permits machines to economically and accurately perform repetitive tasks, such as quality control scanning, at massive scale. For example, machine-learning based computer vision solutions can read thousands of license plates as cars pass through a toll gate with a high degree of accuracy. This eliminates the need for human operators in toll booths.
Companies like PREDII and Gestalt Robotics are advancing the integration of machine learning into manufactured systems and operational software, bringing us closer to the real adoption of digital twinning on physical systems, which will accelerate the reality of a digital workforce.
Cloud robotics. The last stage of the process where the digital twin interacts with the physical world will be enable by “cloud robotics.” These solutions allow the worker or specialist to connect to the physical systems with sophisticated capabilities. Combined with computer vision and cloud services and connected with 5G communications, the cloud robot will continuously mature in its ability to permit the remote operator to cross the barrier from the virtual world to the physical world.
The likes of companies such as CloudMinds for the service industry and Vecna VGo for the healthcare industry are deploying operator integrated cloud robotics solutions and making the specialist’s interaction with the physical world become more of a reality.
Artificial General Intelligence (AGI) – Machine Intelligence. As narrow intelligence systems become more prevalent, several organizations are working on the ability for machines to learn on their own, becoming more sentient, almost conscious.
This step is the turning point where digital twinning is integrated using machine intelligence, combined with big data, cloud computing, computer vision, and then connected with advanced cloud robotics. Together, these technologies will enable the specialist to do the digital work that connects the virtual world with the physical world.
The digital workforce will be able to connect to the Digital Twin grid in a frictionless manner. The integrated AI and instant access to information will allow the specialist to be almost superhuman in what they can accomplish.
It’s Not Work; It’s a Competition to the Top.
The future of work will require a specialized digital workforce to navigate the continuous advancements in digital twin solutions and the integration of sophisticated cloud-based capabilities. The level of innovation and quality of work may be unimaginable to today’s workforce.
From childhood to adulthood, digital employees of the future will have had years of training using this technology. They will effortlessly and naturally tap into information and peers to rapidly learn and practice new skills with ease. They will understand how to function in complicated environments with motion and augmented devices and will fearlessly compete to be better and do more, while watching their score rise in the specialist center among their teammates.
In this future paradigm, “Work” will be a challenging, real-time game attached to the Digital Twin grid enabling employees to prove they are the best at what they do and be paid appropriately to do it.