Yes, robots are coming for your job, but you might like the new one more (if you're still employed)

 For decades, a speculated wave of job-stealing technologies has stirred debate about the role of humans in the labor force alongside automation and AI adds a new wrinkle to the equation.

Image: GettyImages/gremlin

It's been a century since the word "robot" first entered the human lexicon. In the ensuing decades, automation has leaped from the pages of science fiction with very real-world implications on workforces around the globe. While the early use of these technologies may have focused on the hardware side, advances in AI are ushering in a new automated age, augmenting traditional human roles across industries; including skilled portions of the labor force.

For decades, a speculated wave of job-stealing technologies has stirred debate about the role of humans in the labor force alongside innovation and automation. Approaching an unknowable future and the not-yet-invented technologies, what has the history of automation shown us about the potential road ahead and what could we lose along the way?

Cost of automation: Economics and human labor

Earlier this month, Reign released a report, highlighting sentiments regarding AI and the impacts of these capabilities on the workforce. The findings detail robust growth in AI jobs as well as surging salaries for these workers. For example, Reign found that U.S. AI-related positions increased 21% in the last decade and the average salary surged 27% during this time.

A portion of the survey sought to gauge how concerned workers in various fields were about AI impacting their position. Overall, 54% of respondents were very or moderately concerned about AI "negatively disrupting" their job; across all industries, 36% of respondents believed AI will decrease the total number of positions. Interestingly, there may be educational factors at play within these automation fears. 

Juan-Manuel Fluxà, CTO at Reign, said those with at least a bachelor's degree were "more than twice as likely to believe that AI will create more jobs in the future," adding that this "divide in perception is very telling for how education level leads to knowledge and perception of AI."

Education may factor into the perceived threat of AI in terms of personal employment and workforce impact, but a human's skill set and ability to readily upskill amid an evolving labor market could also play a key role in their future livelihood.

Earlier this year, the World Economic Forum predicted that automation, "contrary to popular fears about job losses," would result in a net increase of 58 million jobs. Among the jobs automation will transform, the organization projected 2-in-3 will "become higher-skilled," and the remaining third being "lower-skilled."

Joël Blit, associate professor of Economics at the University of Waterloo, said "predictions are best guesses," but the "reality," is that no one knows how AI and robotics will impact the workforce. Speaking to his own opinion, Blit predicted that there would be job losses as a result of automation, although employment gains in other areas could offset these reductions.

"Many people are failing to understand that the biggest issue is probably going to be not so much job losses from these technologies, as much as the distributional impacts that they are going to have," Blit said.

While some workers with skills "complementary to these technologies" will greatly benefit from this tech, with wage increases to boot, people whose skills are substitutes for these technologies are going to be hurt, according to Blit.

Similar to information and communications technology, he said if these capabilities predominantly end up hurting people in the mid and low ends of skill distribution, then "we will see an increase in inequality."

Moving forward, Blit said there will likely be "a lot of churn" in the labor market, and governments will need to play a part in retraining, as "many jobs will disappear, but many others" that require new skills will be generated.

Evolution of automation and career quality

Tom Hathaway, a Udemy instructor who teaches courses on business analysis and navigating digital transformation, said he's read a lot about servers and transportation workers "being phased out," but other positions, not so much.

"I haven't heard a lot of discussion about business analysts losing their jobs but it would not surprise me if they did," he continued.

Automation negating the need for a human position is one thought to mull over, the quality of the new role augmented by said automation is another point altogether. If these automated processes are creating new positions for humans in these industries, has the general evolution of these sector-specific positions been a net positive or negative for people in these fields?

This is all a matter of perspective, according to Hathaway: For the person being replaced, the new positions often "require higher qualifications," leaving people who do not obtain these new skills "easily out of the picture." At the societal level, he said these new jobs come with more global GDP value-add, making it a perspective "net win."

"Of course, for those who can and do acquire the required skills for the new positions, it is progress at its finest," he continued.

Economics and a premium on human "ingenuity"

Whether automation entails software-heavy digital transformation investments or a so-called dark warehouse named due to its ability to operate with minimal optic-enabling luminescence overhead (unlike traditional human-filled factories), the decision to automate may come down to basic economics and the almighty dollar.

"In many cases tasks could be theoretically automated, but they aren't because it's simply cheaper to pay a human to do that task – and a human is more versatile for retraining when the task likely changes in the future," said Ryan Hill, director in the Gartner HR practice.

It's also important to bear in mind the pace of automation and industry-wide adoption to date. Across industries, a new automated process may take years or even decades to gain widespread usage. Simply put, going all-in on automation isn't so much akin to flipping a light switch as it is to situationally reinventing the lightbulb. The inherent speed of this automation and deployment could also help with the transition for humans in these industries; in essence, softening the blow of automation from a user-experience perspective.

"In a world where we immediately swap out all transactional, repetitive tasks and replace them with strategic difficult ones, the change would likely overwhelm workers," Hill said. But, in reality, he said, this transition is "often much slower," with tasks changing individually "over a sustained period."

"This enables workers to adapt over time and develop new proficiencies that make what was once difficult, simple and repetitive."

On this topic, Hill emphasized that automation does not get rid of workers' "easiest tasks," although it can make more "mentally-taxing" processes easier. As an example, he pointed to use-cases involving a junior lawyer conducting "hours of legal research" and a nurse tasked with prescribing the best triage approach for an incoming patient.

"AI and other emerging technologies can find patterns in data that can help make the hardest decisions easier and better-informed," he said.

Tasks on the creative and social side, according to Hill, "fundamentally require humans," because these undertakings involve "predicting future outcomes, creating new ideas and engaging in interactions with others," areas where AI has difficulty because it is "inherently based on backward-looking existing data."

"While it can find patterns indiscernible to humans, it doesn't yet have the ability fully to see events, create scenarios or anticipate social interactions that haven't yet happened," Hill said. "Instead, it provides data processing and pattern recognition as an input for human ingenuity."

Situationally, increased automation may set a premium on certain intrinsically human capabilities; as Hill explained, AI "implementation increases demand for social-creative skills — the ability to strategize, empathize and think critically."

SEE:  AI on the high seas: Digital transformation is revolutionizing global shipping (free PDF) (TechRepublic)

Automation and upskilling: New tech, new skills

As new technologies bubble up, these capabilities have the potential to reshape economies and the labor force. Similarly, as automation takes shape, there is the idea that as positions are replaced, the process creates new related positions for people in these fields. Although these new positions, as the World Economic Forum points out, may be disproportionately higher- or lower-skilled in time. But, how has this thinking played out in the tech sector over the last decade and what impact has this evolution of positions had on employees in this space?

"As automation starts working up the stack starting with the more routine tasks, tech workers have the opportunity to develop deeper skills and explore more innovative solutions," said Seth Robinson, senior director, technology analysis, at CompTIA. "This movement lines up with the shift within companies to make IT more strategic."

Situationally, tech workers have the opportunity to be more aligned with corporate goals as employees have "more bandwidth to explore emerging technology," according to Robinson, which, in turn, gives them a "more integrated position within the organization."

Corporate alignment aside, it's interesting to consider what types of roles could be churned out as new technologies evolve and automation potentially proliferates across industries. Decades ago, as the first welding robots hit assembly lines, even the most forward-thinking technologists may have been hard-pressed to dream up roles such as a Chief Artificial Intelligence Officer or an analytics translator.

That said, 60-80 years from now, what roles will humans still have and what will they bring to the table that software and hardware cannot automate?

"As much as automation is now capable of doing, there are still some incredibly hard problems to solve with algorithms," Robinson said, pointing to autonomous vehicles that have "been on the cusp of advanced capability" for quite some time, yet still have not "broken through to achieve the theoretical promises."

Untapped theoretical promises aside, AI, once unleashed is not without its situational algorithmic shortcomings. Over the last year, AI and algorithms, in general, have come under scrutiny related to potential biases and inequities these capabilities could introduce in industries ranging from healthcare to financial services.

Moving forward, Robinson said humans will be needed to monitor automated systems, seeing as "modern AI produces probabilistic results," people will indeed be able to spot these anomalies and improve algorithmic training as "there is always the chance that there will be something unexpected."

"For the foreseeable future, humans will likely remain involved in situations with even a moderate degree of complexity," he said.

Choosing a career path amid automation

Faced with increasingly agile AI capabilities and decades of automation ahead, the idea of selecting a sustainable career route for the next half-century could be a daunting task for students. Situationally, Hathaway suggested a high school-age student go "heavy on the humanities," as he believes this is the "last fortress that humanity might defend against the encroaching technology."

"Although AI can certainly master technically sound and correct writing, I would like to think that worst case, a future Shakespeare, Goethe or Hemingway could compete and possibly beat it."

As for the occupational long-game, Hathaway said he'd be "more than surprised if AI ever masters true emotional intelligence," although he's willing to wage the agents will be "great at faking it," although he rhetorically questioned the real value of this.

"I would really enjoy seeing them take a crack at what I call 'compassionate creativity,'" he said. "If they can do that, then AI could probably provide better answers to these questions than I can."

About the Author

R. Dallon Adams is a journalist originally from Louisville, Kentucky. His previous work includes a wide spectrum of beats and formats ranging from tech-savvy urban planning initiatives to hands-on gad...

Automation Booms After COVID - Do we need humans for that job?

 

Baylee Bowers pays for her lunch using her cell phone at Bartaco in Arlington, Va., Sept. 2, 2021.

AP Photo/Jacquelyn Martin

Ask for a roast beef sandwich at an Arby’s drive-thru east of Los Angeles and you may be talking to Tori — an artificially intelligent voice assistant that will take your order and send it to the line cooks.

“It doesn’t call sick,” says Amir Siddiqi, whose family installed the AI voice at its Arby’s franchise this year in Ontario, California. “It doesn’t get corona. And the reliability of it is great.”

The pandemic didn’t just threaten Americans’ health when it slammed the U.S. in 2020 -- it may also have posed a long-term threat to many of their jobs. Faced with worker shortages and higher labor costs, companies are starting to automate service sector jobs that economists once considered safe, assuming that machines couldn’t easily provide the human contact they believed customers would demand.

Past experience suggests that such automation waves eventually create more jobs than they destroy, but that they also disproportionately wipe out less skilled jobs that many low-income workers depend on. Resulting growing pains for the U.S. economy could be severe.

If not for the pandemic, Siddiqi probably wouldn’t have bothered investing in new technology that could alienate existing employees and some customers. But it’s gone smoothly, he says: “Basically, there’s less people needed but those folks are now working in the kitchen and other areas."

Ideally, automation can redeploy workers into better and more interesting work, so long as they can get the appropriate technical training, says Johannes Moenius, an economist at the University of Redlands. But although that's happening now, it’s not moving quickly enough, he says.

Worse, an entire class of service jobs created when manufacturing began to deploy more automation may now be at risk. “The robots escaped the manufacturing sector and went into the much larger service sector,” he says. “I regarded contact jobs as safe. I was completely taken by surprise.”

Improvements in robot technology allow machines to do many tasks that previously required people -- tossing pizza dough, transporting hospital linens, inspecting gauges, sorting goods. The pandemic accelerated their adoption. Robots, after all, can’t get sick or spread disease. Nor do they request time off to handle unexpected childcare emergencies.

Economists at the International Monetary Fund found that past pandemics had encouraged firms to invest in machines in ways that could boost productivity -- but also kill low-skill jobs. “Our results suggest that the concerns about the rise of the robots amid the COVID-19 pandemic seem justified,’’ they wrote in a January paper.

The consequences could fall most heavily on the less-educated women who disproportionately occupy the low- and mid-wage jobs most exposed to automation -- and to viral infections. Those jobs include salesclerks, administrative assistants, cashiers and aides in hospitals and those who take care of the sick and elderly.

Employers seem eager to bring on the machines. A survey last year by the nonprofit World Economic Forum found that 43% of companies planned to reduce their workforce as a result of new technology. Since the second quarter of 2020, business investment in equipment has grown 26%, more than twice as fast as the overall economy.

The fastest growth is expected in the roving machines that clean the floors of supermarkets, hospitals and warehouses, according to the International Federation of Robotics, a trade group. The same group also expects an uptick in sales of robots that provide shoppers with information or deliver room service orders in hotels.

Restaurants have been among the most visible robot adopters. In late August, for instance, the salad chain Sweetgreen announced it was buying kitchen robotics startup Spyce, which makes a machine that cooks up vegetables and grains and spouts them into bowls.

It’s not just robots, either -- software and AI-powered services are on the rise as well. Starbucks has been automating the behind-the-scenes work of keeping track of a store’s inventory. More stores have moved to self-checkout.

Scott Lawton, CEO of the Arlington, Virginia-based restaurant chain Bartaco, was having trouble last fall getting servers to return to his restaurants when they reopened during the pandemic.

So he decided to do without them. With the help of a software firm, his company developed an online ordering and payment system customers could use over their phones. Diners now simply scan a barcode at the center of each table to access a menu and order their food without waiting for a server. Workers bring food and drinks to their tables. And when they’re done eating, customers pay over their phones and leave.

The innovation has shaved the number of staff, but workers aren’t necessarily worse off. Each Bartaco location — there are 21 — now has up to eight assistant managers, roughly double the pre-pandemic total. Many are former servers, and they roam among the tables to make sure everyone has what they need. They are paid annual salaries starting at $55,000 rather than hourly wages.

Tips are now shared among all the other employees, including dishwashers, who now typically earn $20 an hour or more, far higher than their pre-pandemic pay. “We don’t have the labor shortages that you’re reading about on the news,” Lawton says.

The uptick in automation has not stalled a stunning rebound in the U.S. jobs market -- at least so far.

The U.S. economy lost a staggering 22.4 million jobs in March and April 2020, when the pandemic gale hit the U.S. Hiring has since bounced back briskly: Employers have brought back 17 million jobs since April 2020. In June, they posted a record 10.1 million job openings and are complaining that they can’t find enough workers.

Behind the hiring boom is a surge in spending by consumers, many of whom got through the crisis in unexpectedly good shape financially -- thanks to both federal relief checks and, in many cases, savings accumulated by working from home and skipping the daily commute.

Mark Zandi, chief economist at Moody’s Analytics, expects employers are likely to be scrambling for workers for a long time.

For one thing, many Americans are taking their time returning to work -- some because they’re still worried about COVID-19 health risks and childcare problems, others because of generous federal unemployment benefits, set to expire nationwide Sept. 6.

In addition, large numbers of Baby Boom workers are retiring. “The labor market is going to be very, very tight for the foreseeable future,” Zandi says.

For now, the short-term benefits of the economic snapback are overwhelming any job losses from automation, whose effects tend to show up gradually over a period of years. That may not last. Last year, researchers at the University of Zurich and University of British Columbia found that the so-called jobless recoveries of the past 35 years, in which economic output rebounded from recessions faster than employment, could be explained by the loss of jobs vulnerable to automation.

Despite strong hiring since the middle of last year, the U.S. economy is still 5.3 million jobs short of what it had in February 2020. And Lydia Boussour, lead U.S. economist at Oxford Economics, calculated last month that 40% of the missing jobs are vulnerable to automation, especially those in food preparation, retail sales and manufacturing.

Some economists worry that automation pushes workers into lower-paid positions. Daron Acemoglu, an economist at the Massachusetts Institute of Technology, and Pascual Restrepo of Boston University estimated in June that up to 70% of the stagnation in U.S. wages between 1980 and 2016 could be explained by machines replacing humans doing routine tasks.

“Many of the jobs that get automated were at the middle of the skill distribution,” Acemoglu says. “They don’t exist anymore, and the workers that used to perform them are now doing lower-skill jobs.”

About the Author

Industrial Distribution has delivered the information industrial distributors need to succeed since 1911. Concurrently, the publication provides a complete portfolio of integrated marketing solutions with print and online products, digital magazine delivery, single and multi-sponsored e-newsletters, a daily e-newsletter, resource guides and sponsored webcasts that enable you to reach and sell to these key buyers in the industrial distribution market.

 

The future of digital twins: what will they mean for mobile networks?

The idea of duplicating one’s mind into a machine, decoupled from the physical body has long fascinated humans. Now it’s reality – welcome to the age of digital twins. Maybe not for humans (yet), but for the systems around us. Let’s get to know digital twins and what they mean for optimizing and automating mobile networks.

In the 1930 Science Wonder Stories, featuring ‘The Infinite Brain’, inventor Anton Des Roubles uploads his entire brain into 200,000 memory cells and continues to live in machine-form after death, decoupled from his biological existence. That story quickly takes a turn for the worse, so we’ll leave Anton and his destiny there for now, with the observation that today we count memory in gigabytes and terabytes, but holding an entire human mind in a device is still very much science fiction.

There are many stories on the theme of artificial brains and cloning in popular culture and science fiction, but a more technological precursor of today’s digital twins can be found in the 1992 book ‘Mirror Worlds: or the Day Software Puts the Universe in a Shoebox...How It Will Happen and What It Will Mean’. Here, the computer science professor David Gelernter outlines a future where computer systems are interconnected globally and observe in real-time the physical world around us. The resulting images and representations can be presented to humans through a pane of glass, accurately mirroring the real world. In his vision, we can also interact with the presented images, controlling things in the real world through this mirror.

These were still only ideas, and the first real use of the digital twin concept was done in 2002, when Michael Grieves applied it to product lifecycle management in manufacturing. Since then, the concept has evolved and been applied to many areas. Here we’ll start with an overview of what digital twins are and their typical use cases, then move on to see how these ideas can be used in optimizing and automating our networks.

What is a digital twin?

In essence, digital twins are software representations of assets and processes, which are enhanced with capabilities not present in the real-world entity. There are many proposed definitions which emphasize different aspects of digital twins, and the concept is still evolving, but there are typically a few common characteristics listed here and illustrated in the picture below:

• First, you need data models and data structures to represent the observations, state and relations of the real-world objects of interest.
• Second, you need to populate these models with knowledge, for example, actual data from the specific real-world objects to create the actual digital twin instance. In many cases this data collection is done continuously to enable an accurate and up-to-date view in the twin.
• Third, you need tools that operate on the data to add value. These are the key to unlocking the capabilities and benefits of the digital twin. Arguably the most integral group of tools are the analytics models to extract insights from the digital twin, spanning from simple data retrieval to complex algorithms used to predict future behavior, simulate different scenarios and other analytics tasks. But there are also tools for actuation to add improved steering capabilities to the real-world objects.
• Finally, you need ways to interact with the twin through different APIs & visualization The insights you gain will be used to make better decisions in the real-world, either via a human or by direct actuation from the digital twin itself.

Conceptually this looks simple, but often advanced software is needed to realize these ideas, as we will see later. Data management will be a challenge as data volume grows and execution of simulations needed for analysis can be very time consuming.

Figure 1: The structure of a digital twin.

How are digital twins used?

Digital twins are often applied to big expensive machines, such as jet engines or power plants. In these cases, there are high costs associated with unexpected downtime and the consequence of a failure can be severe. With the enhanced knowledge you get from the digital twin, it is possible to optimize service windows and predict when different components need to be replaced. The advances in compute capacity and recent progress in AI-based analytics makes the concept attractive to a wider set of use cases and in the IoT area, ranging from simple devices like temperature sensors and light bulbs to entire cities with a complex web of traffic, utilities and buildings. There are many different initiatives pushing this ahead, both in standards and commercial offerings.

Turning our attention back to mobile networks, they certainly fit the criteria of big machines with high consequences for downtime and failures, as well as the added complexity of massive geographical scale. With this in mind, it does make sense to apply digital twin concepts here as well, to add value in both the operations phase and the development phase. In fact, in some ways existing solutions already use core ideas from digital twins. Network planning tools have long been used to understand the current network situation and plan for upgrades to satisfy future demands. Typically, these are not real time, but they use the same basic concept of data collection, analysis and prediction to support decision processes for network build-out to satisfy future demand.

There are also solutions to optimize site management and field operations, such as Ericsson’s Intelligent Site Engineering, which uses photos to create virtual 3D models of radio sites. Site deployment and maintenance can then be planned in detail from the office, reducing truck rolls, site visits and tower climbs. The Advanced Microwave Insights is a recent addition which analyzes data from microwave networks in near real time, to improve performance and save costs.

These examples are for quite specific use cases and we continue to identify additional use cases and to see how digital twins could be integrated in our 6G networks system to support development and operations. Some digital twin applications would be quite specific, focusing on one part of the network like a site or a radio cell at a high level of detail. On the other extreme, we have twins with an end-to-end view, leading to a higher-level analysis of services or network infrastructure. Other twins could focus on a specific domain like the RAN or edge clouds.

Figure 2: Areas of interest for digital twins in mobile networks.

What applications do we see for 6G networks?

Let’s take a closer look at some areas we have identified where network digital twins have interesting use cases.

New services - In 6G we expect a much wider variation of services to be deployed compared to today. There would be specialized services for small groups of users like emergency personnel or self-assembling robots. For the mass market, immersive sports events would be offered with enhanced interaction and augmented or virtual reality tailored to the specific occasion. Such services have high requirements on performance and their related service-level agreements (SLAs) have tough KPIs to fulfil. Using a network digital twin, we create a replica of the current network state (the “twin”), then deploy the new service in the twin, using analytics models to simulate and analyze the service performance and its impact on the other, already existing services. If everything looks good, we can go ahead and launch the services. But if there are potential issues, we can use this new insight to adjust the service parameters or prepare the network for the increased load. This could be both updated usage quotas or actual network upgrades to support the increased load.

Future scenarios and deployments – In making network upgrades and build-out, it is important to understand when and how to do this in the optimal way. The service requirements are projected by analyzing current trends and adding expected future developments. Once the needed network and compute capacity has been identified, a digital twin could be used to test different future scenarios. Options for coverage improvement and extension of transport and cloud infrastructure could be analyzed in greater detail and the resulting performance compared. Another separate but related aspect is the impact on services from different failure cases. In a digital twin, an operator would be able to identify weak spots and quantify consequences of failures. In doing so, it is possible to evaluate in greater detail both fulfilment of customer expectations and financial risks from SLA breaches. These insights can then be taken into account when planning for the future, to optimize the investments and funnel them to where they have the best economic value.

Training of AI – Most AI algorithms need large amounts of realistic data to be trained. A digital twin could be used to train algorithms when the real network does not provide enough data or, as in the case of reinforcement learning, it is risky to apply an AI algorithm under development to the real network. In a digital twin, one can also create different problem situations and failures which are rare in network operations, enabling the AI algorithm to train for such situations, to recognize and act correctly upon them in cases they do occur in live networks.

Configuration and function changes – When you apply a new configuration, a new AI model or a new software in a network it is important to know how it performs before introducing it to the whole network. In today’s continuous integration and deployment (CI/CD) pipelines, canary testing is done to validate that services perform and function as expected. A digital twin would be a further tool to test impact in a safe environment, for example, by evaluating the planned steps for deploying the new software in the cloud environment to make sure that the resources available are sufficient to do the test and, if successful, switch over to this new version.

In autonomous networks, a network management system takes actions on its own to make sure services have the expected performance and that the objectives defined for network operations are fulfilled. Understanding the impact of these actions is essential and a digital twin could be used to test different actions, evaluate their impact on relevant KPIs and decide on the best options before they are implemented for live services.

Figure 3: The process view of a digital twin.

Click to view an enlarged image

What are the challenges with realizing network digital twins?

So, how can we actually realize this? There are a few different parts which need to be in place. A key enabler here is data. Data-driven network architecture is crucial for developing the infrastructure required for digital twins – we need to know the state of the network in real time, or at least near to real time. Measurements need to be done at a greater level of detail than what is typically done today, gathered in databases, and made available for analysis. This is an area where there are currently significant efforts ongoing, both to capture data and to gather this data for later use. For some use cases, data availability is already sufficient to create digital twins, whereas other, more advanced use cases still need improved capabilities in this area. Typically, more detailed analyses would require more detailed data, and one would need to balance the cost of gathering and managing data with the gains achieved for different use cases.

In a digital twin, both historical and current data will be used to create models to understand behaviors, analyze the current situation and make predictions about the future. There is a trade-off here also, between complexity, accuracy and the computational resources needed to run the models. To some extent, we are helped by computer hardware improvements and availability of cloud environments. But even today, detailed models can in practice only be executed for quite small systems and few users. In many cases, there is also a time aspect – a need to evaluate models much more quickly in the twin than in the real network. Otherwise, we would get an answer, but it would be too late for it to be useful. To fulfil these conflicting requirements, there would be a mix of different models depending on the use case at hand, the network domain of interest, and the level of detail needed. Some would be based on different behavioral and physical models whereas others would use AI and be entirely learned from data.

To complete the digital twin, various tools and APIs for interaction need to be developed. Humans will create different scenarios and test configurations and control their execution in the twin. New visualization interfaces will also be required to present results and findings in a quick and accessible way, supporting timely decision processes. In a similar way, the digital twin must also be directly accessible from software used in development and operations, without direct involvement of humans. This access would be through management systems and AI training pipelines, but also other digital twins. In such a way, a network digital twin could be part of a larger digital twin ecosystem which could include, for example, city planning, transportation, smart buildings, and power grids.

The next steps – the evolution of digital twins

With the outlook described above and the opportunities identified, how can we realize the desired capabilities in this area? There are two tracks we need to evolve simultaneously to gradually introduce new solutions into network operations and development.

On one hand, specialized solutions will continue to develop for the most attractive use cases and new capabilities are already being added to existing solutions. As these evolve and become more advanced, similar to the evolution we see in the IoT area, common components and frameworks will likely emerge and gradually evolve into generally applicable solutions and a platform for new digital twins.

One the other hand, there is a need to understand the bigger picture on how digital twins will fit in an evolved network architecture. There are currently efforts ongoing in standardization (IETF & ITU-T) to define architectures for digital twins in a network context. Once these mature, they will form the basis for creating frameworks and solutions for different use cases, both those mentioned above and future use cases yet to be discovered.

To quote the prophetic words printed over 90 years ago in Science Wonder Stories: “And lest you jump to the conclusion that intelligent, or quasi-intelligent, machines are pure figments of the imagination, remember that already, today, we have machines that can “think" faster and better than any human being… There is no question that, in the future, even more wonderful machines will be evolved along these lines.”

About the Author

Ericsson is one of the leading providers of Information and Communication Technology (ICT) to service providers. Ericsson enables the full value of connectivity by creating game-changing technology and services that are easy to use, adopt, and scale, making our customers successful in a fully connected world.

Lars Magnus Ericsson founded Ericsson 145 years ago on the premise that access to communications is a basic human need. Since then Ericsson has continued to deliver ground-breaking solutions and innovate technology for good.

The next (digital) normal

 

The next (digital) normal

Consumer predictions about life after the pandemic focus on a more digitally enabled lifestyle and include changes in priorities. Routine activities are expected to happen digitally, making way for those things most longed for in life: having new experiences, creating memories with others and spending more time outdoors.

The shift to remote working

Remote work is expected to stay: three in 10 expect to continue working fully remotely after the pandemic. This increases even further to three in eight among the working population in Algeria and one in three among the working population in Ethiopia. Similarly, university students also expect that over 80 percent of their study hours will be spent online in the next normal.

Travel patterns will also change in the next normal but perhaps not in the way one might expect: one in three expect to switch their commuting methods for those with less environmental impact, such as using public transport, biking or walking. However, many consumers take the opposite view with their primary choice being car travel.

Consumers expect a shift in their chosen mode of transport going forward
Source: Ericsson ConsumerLab GTM 2021

The online shopping evolution

Shopping is an area of dual purpose: a pastime and social activity for some, while being purpose-driven and practical for others. Over the course of the pandemic, shopping in physical stores became challenging and online shopping a more attractive option.

Consumers predict online shopping will become a more common feature in the next normal for their shopping needs. While shopping in physical stores will still likely be a prevalent habit in the future, online purchasing is expected to account for 37 percent of all shopping activities. Aside from general shopping, groceries are another purchase category and even this highly routine activity is set to be handled online. Among the online population in the African markets, consumers estimate that one-fourth of all their grocery shopping will be ordered via online platforms.

The effect of the pandemic on online and in-store shopping purchases
Source: Ericsson ConsumerLab GTM 2021

A rise in e-health

Now, more than ever, consumers are realizing the importance of taking care of their health. Among those who have experienced illness in the past year, the frequency of using e-health services has risen to 3.6 times per month. The benefit of this experience during the pandemic means this group of consumers, in comparison to others, predict they will use e-health services even more in the future with an expected frequency of 4.6 times per month.

A duality in urban life in 2025

In exploring how consumers believe society will change by the year 2025, it becomes apparent two underlying sentiments inform their predictions: worry and ambition. Worry – for a challenge-filled future; and ambition – for the different ways they expect to circumvent these challenges.

Stress levels and work-life balance

Among the consumers in Africa, 67 percent of consumers believe that by 2025, the general stress level in their country will be much higher. More than seven in 10 also believe the majority will need to hold a second or third job, in addition to their primary job, to maintain a decent income. While this may be the case, three in four consumers also expect to be leading a healthier life and equally as many predict that they will practice more mindful living to a greater extent. Consumers are aiming to strike a better balance in daily life by means of a greater reliance on connectivity and online services in the future. Undoubtedly, the pandemic has inspired consumers to handle more of their needs through online services. This is something they expect to extend into the next normal and beyond.

Predicting the future

The two most shared predictions among consumers living in urban areas, are concerned with online security and a drive to lead healthier lives. The concern for online privacy comes against the backdrop of future lifestyles being further empowered through online services. The concern for health follows the pandemic that has highlighted the need to maintain a healthy lifestyle.

A digitally compromised future

Consumers are looking to prioritize new experiences and live more mindfully in the future. This does not place any boundaries on how entertainment is experienced. In fact, over three in five consumers predict that most entertainment, culture and social gatherings will take place over online platforms and be easily accessible for more to enjoy.

The privacy dilemma

Given the many and diverse ways in which consumers are expecting to use online services and platforms in their daily lives, the overall time they spend being connected will also increase. Today, one-third of consumers already feel concerned about online privacy.

Consumers who are most concerned about online security today are primarily in their mid-20s and above; exist most prominently in the working population; and are technology-interested individuals. However, 8 in 10 consumers across all age groups, professions and technology proficiencies, expect to pay more attention to online privacy and security by 2025.

Online concerns are set to grow
Source: Ericsson ConsumerLab GTM 2021

Consuming locally and traveling widely

The focus on having experiences and creating new memories is making consumers plan for a future that has both a local and global focus.

Driven partly by environmental concerns, consumers are not necessarily looking to consume less but to consume more locally. By 2025, as many as half predict they will only shop locally made products and locally grown produce. Furthermore, 59 percent of consumers predict that local consumption will become a new future norm.

Quarantine and social distancing, coupled with overall limits to physical mobility, during the pandemic, have underscored the value of breaking away from long-standing routines. However, with a future focused on new experiences and enjoying life, consumers are also looking to increase a behavior that seems at odds with their environmental worries: increased leisure travel.

Consumers globally will either refrain or partake more in air travel

Digital inclusion for all

Access to connectivity and digital inclusivity are paramount to rebuilding resilient, sustainable and equitable societies in the future.

When entering the next normal, consumers are predicted to have added an average of 3.4 more online services to their daily online activities, while also increasing the time they spend online by 10 hours per week in comparison to their pre-pandemic habits. While there is still a gap between the most advanced and the more moderate online users, the divergence is reducing as moderate users expanded the number of services taken up over the course of the pandemic.

It is increasingly important that access to connectivity is afforded to all, given the many and diverse needs consumers will need to seek to address through digital services in the future. This sets digital inclusivity high on the agenda for future urban planning.

In this pivotal moment where the world is slowly emerging from a significant crisis, there is an opportunity to shape a future urban reality that addresses the future challenges perceived by consumers, while also allowing for safe and inclusive digital spaces that enable them to focus their time and effort on those activities which matter most in their lives.

" About the Author "

Ericsson is one of the leading providers of Information and Communication Technology (ICT) to service providers. Ericsson enables the full value of connectivity by creating game-changing technology and services that are easy to use, adopt, and scale, making our customers successful in a fully connected world.

Lars Magnus Ericsson founded Ericsson 145 years ago on the premise that access to communications is a basic human need. Since then Ericsson has continued to deliver ground-breaking solutions and innovate technology for good.