2018 Mobile Industry Predictions

William Dudley

In my 11th edition of mobile industry predictions, 2018 is already starting off with technological bombshells, thanks to the U.S. FCC repealing network neutrality regulations in 2017. However, this debate is far from over.

Blockchain is gold: Any company mentioning blockchain suddenly rises to the top, and public companies discussing blockchain see stock valuations sometimes rise dramatically. Those results are primarily based on the hype that bitcoin and other cryptocurrencies experienced in December. Mobile networks continue to flourish, and 5G will likely become reality this year.

2017 predictions: How they fared

First, I’d like to review 2017 predictions to see how they fared against the reality of this dynamic, ever-changing industry. For each 2017 prediction, I will rate the correctness.

2017 prediction: Mobile messaging

Messaging through SMS will continue to grow and become the dominant worldwide channel for customer interactions. SMS will show resilience and staying power. RCS-type deployments will continue to disappoint. There will be no Android equivalent of Apple Messaging. Some IP messaging platforms will become legitimate alternative channels to A2P SMS.

2017 realityA2P messaging and messaging channels grew and our A2P messaging statistics reflected growth with double-digit percentage increases over 2016. Ovum indicates “A2P messaging is projected to grow at 8% CAGR from 2015 to 2018.”

I was wrong about RCS. A2P RCS is making a resurgence thanks to the catalyst of Google Jibe’s RCS Business Messaging. Other RCS hubs including Samsung, Mavenir, and ecrio, are providing solutions around the GSMA Universal Profile 1.0 and 2.0 standards, reducing RCS fragmentation. RCS is poised to become an important engagement channel.

2017 mobile messaging prediction score: 75% correct, because I thankfully I got the RCS part wrong.

2017 prediction: Chatbots 

Chatbots will be heavily hyped, but won’t gain significant prominence, barring a few customer-service solutions, which will ultimately lead to human interaction. Chatbots will replace the voice-call menu tree or request needed information for the human responder.

2017 Reality: Strongly hyped, chatbots are being used more, especially around customer service; however, their prominence is still questionable. Bots are mostly used in non-SMS social chat apps. For some platforms, the ability to discover new bots is a factor. A few SMS-based are being deployed and will play an increasing role in A2P RCS as a core element of conversational messaging on that channel. AI enhancement of chatbots has become commonplace, leveraging solutions like Google’s api.ai (now called Dialogflow) and Microsoft’s Bot Framework, among several.

2017 chatbot prediction score: 100% correct

2017 prediction: 5G

We will see a start of production 5G deployments by mobile carriers, initially targeting IoT applications; however, some will target consumer devices.

2017 reality: Almost a complete, but close, miss. GSA noted that 103 operators in 49 countries are “investing in 5G technology in the form of demos, lab trials, or field tests.” As of December 2017, 32 operators have made public commitments to deploy 5G in 23 countries, including Verizon Wireless, who has committed to roll-out 5G in 3-5 cities. Huawei indicated that Vodafone Italy had achieved the first 5G data connection in Milan, Italy, marking the start of their planned network rollout.

2017 5G prediction score: 20% correct

2017 prediction: LTE

By the end of 2017, there will be at least 650 LTE networks and 200 LTE-Advanced Networks launched worldwide.

2017 reality: At the end of 2017, there were 647 commercially-launched LTE networks with 680-700 anticipated networks. Per GSA, there are 216 LTE-Advanced networks in 105 countries.

2017 LTE prediction score: 100% correct

2017 prediction: Apple

Apple will launch a new iPhone 8 and iOS 11 featuring innovations, including an OLED screen, no hardware buttons, wireless charging, enhanced camera capabilities, and better support for LTE-Advanced. This will lead to record iPhone sales with Apple iOS gaining market share, but not dominating Android.

2017 reality: Apple launched the iPhone 8 and iOS 11; however, also announced and shipped the iPhone X, which included the OLED screen, no home button, and the other features. Wireless charging is available for the iPhone 8 and iPhone X. LTE-Advanced support is mostly unchanged in the new devices. Android-iOS market share varies worldwide, but Android remains in the 80-85% share with iOS at 15-20% share.

2017 Apple prediction score: 85% correct

2017 prediction: Two-factor authentication (2FA)

2FA will continue to be the dominant authentication and security mechanism, especially with increasing account breach reports. 2FA will be the dominant channel over SMS, although 2FA through TOTP solutions will gain prominence.

2017 reality: Breaches continued in 2017, resulting in hundreds of millions, if not over 1 billion subscribers’ data compromised. Deloitte was specifically determined to lack 2FA in place. The FIDO/Javelin State of Authentication 2017 report cited that SMS OTP for mobile was second only to password usage. It was 4th for online, with static and dynamic Knowledge Based Authentication (KBA or “secret” questions) coming in 2nd and 3rd. Software OTP (such as TOTP solutions) followed SMS OTP.

2017 two-factor authentication score: 100% correct

2017 prediction: Wearables

Wearables will grow, but lacking killer applications or functionality, will slowly track upward. Fitness/health continue to be the predominant applications. Apple and Fitbit continue to lead the pack. One or more existing platforms will shut down.

2017 reality: Jawbone, once worth over $3 billion, is said to be in liquidation, and TomTom cut jobs as they began restructuring toward their mapping and navigation. Apple re-took the lead in Q-3 with 23% share, with Xiaomi (21%) and Fitbit (20%) behind. In terms of predominant functionality, health and fitness lead the market; however, with LTE-enabled Apple’s Series 3, applications like messaging, alerts, and communications are gaining usage.

2017 wearables prediction score: 100% correct

2017 prediction: IoT

The most dominant IoT applications will be in transportation, especially vehicle automation, followed by logistics and smart home devices. Few vehicle manufacturers will provide capabilities of Tesla (e.g. downloadable software, self-driving capabilities, etc.), but more car manufacturers will provide mobile apps and remote vehicle management.

2017 reality: According to IoT Institute, asset tracking and monitoring was the most popular use case, followed by automation of manual processes and predictive maintenance. 2017 predictions were a little too specific; however, “automation of manual processes” can cover some consumer-focused IoT implementations. Huge changes in vehicle IoT were not realized, although there has been plenty of press around fundamental changes. The most innovative IoT solutions were in the industrial space.

2017 IoT prediction score: 30% correct

2017 prediction: Mobile operators

Expect some mobile operator consolidation in the U.S., with Sprint or T-Mobile USA being acquired. US Cellular could be acquired with some smaller tier 3 operators, leading to questions of competition and market dominance among remaining operators.

2017 reality: Sprint and T-Mobile again flirted with merging, and again called it off. US Cellular remains independent. Due to the FCC-imposed “quiet period,” there was little M&A among mobile operators in the United States.

2017 mobile operator score: 0% correct

2017 prediction: Mobile point of sale

POS will continue to grow in usage and acceptance by consumers. Apple Pay will top double-digit monthly usage. Consumers will begin to accept mobile payment solutions as more secure than credit cards. More sites will support Apple Pay and Android Pay.

2017 reality: Mobile contactless payment solutions increased. A November Bank Innovation report indicated that Apple Pay should reach 86 million users in 2017. Apple Pay is in 20 markets, representing 70% of the world’s card transaction volume, and in the US at more than 50% of all retail locations. Android Pay and Samsung Pay increased, but sort of split up the Android world. Apple Pay Cash launched in late Q4, enabling users to transfer money through iMessage and other channels, setting up iMessage to become a more comprehensive communications app.

2017 mobile point of sale score: 100% correct

2017 was a tough year for predictions. I was 61% correct, compared to  83% in 2016 and 82% in 2015.

2018 mobile industry predictions

2017 has set the table for new technologies to come to commercial fruition this year. In no particular order, here are my ten mobile industry predictions for 2018:

Mobile messaging continues dominance as the primary engagement tool for consumer interactions. SMS will continue to lead and surpass 2017 volumes. Messaging media usage will increase, including Facebook Messenger, WeChat, and others. For the first time, A2P RCS will launch commercial services with key brands and businesses interacting with consumers through RCS.

2018 will be the year that RCS returns, specifically optimized for A2P (or enterprise/business/brand engagement). While person-to-person or P2P RCS will grow, the biggest impact will be consumer interaction. Most will be through AI-assisted chatbots. By the end of 2018, there will be between 500 -700 million MAUs using RCS globally, starting to rival non-SMS messaging apps. This number will be higher if Apple iMessage supports RCS Universal Profile. 2018 may be the beginning of the end for many mobile apps as users discover that conversation interfaces work as well as, or better than, mobile apps with similar functionality.

Apple will grow its worldwide iOS market share, building on the success of the iPhone X. New iPhones in 2018 will leverage the new technology and capabilities introduced in iPhone X. Expect more enhancements to iMessage and improved LTE Advanced capabilities for more networks globally. 2017 revelations around battery slow-down issues ultimately won’t have much effect. Apple Watch will continue dominance in wearables, increasing its share to almost 30%.

Apple HomePod will launch with innovative capabilities, enabling close integration with Apple mobile devices that Amazon Echo and Google Home will not have. HomePod won’t overtake Amazon or Google, but will become the genesis of a new class of personal digital assistant that will grow in influence.

Authentication leveraging mobile solutions will gain more visibility and usage by global consumers. Two-factor authentication (2FA) over SMS will continue as the most-used solution by consumers, followed by 2FA via mobile apps. Mobile operators will close security vulnerabilities around SMS. Biometric authentication will grow in prominence.

Developer-centric API solutions for mobile channels will increase usage – especially in messaging engagement, fueling mobile messaging as a medium for customer engagement. Self-service by developers and non-developers in messaging – and even chatbot solutions – will bring mobile channels to more businesses, quicker and easier.

Expect over 750 commercially deployed LTE networks, over 300 LTE-Advanced commercially deployed networks, and over 50 5G commercially deployed networks. The GSA noted that 2018 should see over 3 billion LTE subscriptions. At the end of 2017, there were 116 mobile operators “investing in pre-standards 5G networks.” Many will provide fixed-wireless solutions and some specialty solutions. I doubt that we’ll see many mobile handsets supporting 5G; that will likely come to fruition in 2019 and beyond.

The U.S. network neutrality debate is not over. There will be legal and legislative challenges to the December 2017 repeal of various FCC regulations around network neutrality. This is a politically charged issue. Most Americans, as well as technology giants, supported the network neutrality provisions, but many mobile operators wanted them repealed. Expect confusion, but little negative consumer-facing activity by mobile operators and ISPs because of less regulation. Most people won’t notice accessibility changes.

Mobile-network connected IoT devices will continue to dominate the IoT space as industries rush to provide mobile-connected sensors. This will be especially important to asset-tracking across industries, especially those where movable assets must be tracked and maintained. Interestingly, these mobile-network connected sensors will primarily use existing networks. Companies providing IoT solutions will benefit by providing big-data mining, tracking, and maintenance capabilities to manage and process asset data from millions of connected sensors. IoT activities across industrial and consumer-focused solutions will increase substantially.

Blockchain (per Gartner, still in the Peak of Inflated Expectations) will be coupled with mobile platforms and applications to provide innovative solutions for finance, security, and mobile wallet/loyalty programs. Going beyond mobile-based cryptocurrency wallets and apps, mobile devices can be used as blockchain nodes that can store a variety of secure transactions. Innovations will demonstrate that mobile devices can be excellent for blockchain-based solutions, which can be as easy as downloading a specific app for consumers.

Last word

2018 mobile industry predictions cover a wide swath: Mobile messaging (SMS, RCS, messaging chat apps), authentication and blockchain as they relate to mobile, IoT, Apple, network neutrality and much more. This year, I’ve decided to stay away from mergers and acquisitions, though I think we’ll see some, but not as many, as previous years.

A variety of new businesses will emerge and become noteworthy in areas such as chatbots, IoT, 5G, and AI, but don’t rule out existing technology leaders. They, too, are working on innovative and amazing technology. Without doubt, 2018 will be another exciting year in the mobile industry.

For more insight on the future of mobile technology, see Digital Transformation Through Mobile Analytics.

This article originally appeared on The Future of Customer Engagement and Commerce.


About William Dudley

William Dudley is group director, mobile evangelist, and strategist of the Industry & LoB Products at SAP Digital Interconnect (formerly known as SAP Mobile Services). He has many years of experience building and managing telecommunications network infrastructures. He defines global strategy and solutions for SAP Digital Interconnect, a business unit of SAP, within the mobile ecosystem, focusing on solutions for messaging, mobile-enabled online security, next-generation networks (5G, LTE, IPX), and consumer engagement through mobile channels. As mobile evangelist, Mr. Dudley communicates through both internal and external publications, social media and is active in industry groups. You may follow him on Twitter at @wdudley2009. His primary blog site is https://blogs.sap.com/author/william.dudley/.

As Machine Learning Remakes Industries, Leaders Must Transform Enterprise IT

Jim McHugh

From cars that autonomously navigate dark and icy roads, to MRI scanners trained to spot brain abnormalities, to warehouses managed by sensors, drones, and robots, machine learning is already transforming industries in profound ways.

These applications are emerging amid a faltering Moore’s Law, which has run up against the laws of semiconductor physics. For four decades, we could count on the doubling of computational power every two years. Now, traditional semiconductors can only deliver about 10% performance gains in this timeframe. That means the performance gains that sustained advancements in the use of information technology through the PC, mobile, and cloud eras can no longer be relied upon to propel the promise of machine learning.

Instead, graphics processing units (GPUs) – chips evolved from those that power image-intensive video games and professional visualization applications – will provide the computational power needed to drive the machine learning revolution. A new computing model, called accelerated computing, takes advantage of the GPU’s faster processing speeds to train the complex algorithms used in machine learning software.

However, most companies’ data centers, where the algorithm training must take place, run on servers with traditional processors. This is hardly surprising, given that machine learning has only recently verged on mainstream business operations. An enterprise that intends to transform itself using machine learning will need to invest in the necessary combination of hardware and software to tap the vast promise of AI.

The power behind the algorithms

Machine learning is poised to change the way business is done across a range of industries. Consider the following examples.

Transportation. Automakers, at the forefront of AI’s transformation of the $10 trillion transportation industry, are racing to show how AI can differentiate their brands. Enhancing safety will be high on the list, as each year there are tens of millions of accidents worldwide and over a million fatalities. Companies worldwide are using a compact, GPU-powered supercomputer in the vehicle that is capable of guiding autonomous cars.

The same holds true for truck manufacturers and logistics businesses. GPU-powered servers in the data center are being used to train, virtually, autonomous trucks and other vehicles how to drive on millions of miles of high-definition mapped roads in a broad range of weather, road, and traffic conditions. Through such simulated driving efforts, the algorithms that run autonomous vehicles will be able to learn continuously from data collected from actual driving situations to make real-time decisions.

Healthcare. Medical imaging alone is estimated to become a $49 billion market worldwide by 2020, making it the biggest source of data in healthcare. Radiology, a prime area for machine learning advances, accounts for a large portion of medical images. According to Academic Radiology, the average radiologist must interpret a CT or MRI examination every three to four seconds to meet workload demands. In an eight-hour workday, that adds up to 8,000 images per radiologist a day.

AI algorithms can be trained to spot abnormalities using real and simulated medical images. This makes devices such as MRI scanners the first line of defense in spotting disease. These and similar devices can speed diagnosis, greatly improve accuracy, and allow doctors to concentrate their energies on the most difficult cases.

Manufacturing and agriculture. Advances in image recognition are creating a range of industrial Internet of Things opportunities. For example, IoT is becoming central to warehouses and fulfillment centers. Machine learning – fueled by image recognition, data, and sensors – steers robots among humans in warehouses.

Manufacturing companies are using connected machines such as drones and robots to inspect industrial equipment, which offers companies potential savings of tens of millions of dollars annually. Industrial farming won’t be left behind. Images taken from drones and satellites will be treated with machine learning to boost crop yields. Farming companies can use images and algorithms to process all the data captured by satellites to monitor the soil conditions and overall crop health. Analytics can track and predict weather changes that could impact crop yields.

An infrastructure for machine learning

All told, the nascent business opportunities enabled by massive data collection and the implementation of algorithms will require rethinking the data center. Without investments in enterprise IT infrastructure, machine learning can’t deliver what it promises.

A critical step toward business transformation is to make sure an organization’s data center can support compute-intensive workloads. GPU-accelerated computing redefines the economics of data center computing, replacing racks of CPU-based servers with far less hardware installation, power, and cost. For example, a company could potentially replace 300 CPU-based servers with one or two GPU-based servers, for a cost savings of more than 85%.

Those managing a company’s data center infrastructure need to ensure they have enough accelerated computing power and storage to handle all the data needed. This involves evaluating the whole picture to understand the incredible savings that can come from modernizing your architecture for the AI world.

Business leaders who perform due diligence to ensure their hardware is a match for their company’s machine learning ambitions will quickly understand the value of GPU computing.

To learn more about the technology requirements for deep learning, check out this webcast on May 24, 2018 and this white paper.


Jim McHugh

About Jim McHugh

Jim McHugh is vice president and general manager at NVIDIA with over 25 years of experience as a marketing and business executive with startup, mid-sized, and high-profile companies. He currently leads NVIDIA Deep Learning Systems – NVIDIA DGX Systems and GPU Cloud. Jim focuses on building a vision of organizational success and executing strategies to deliver computing solutions that benefit from GPUs in the data center. He has a deep knowledge and understanding of business drivers, market/customer dynamics, technology-centered products, and accelerated solutions.

How Machine Learning Is Disrupting The Professional Services Industry

Marcus Fischer

Over the last decade, knowledge has become the key driver for productivity and economic growth. Professional services providers like accountants and lawyers have benefited from this strong knowledge economy. These professionals have a combination of knowledge and expertise that makes them uniquely qualified for solving specific problems. Until recently, this industry has been relatively untouched by disruption. Machine learning is changing this equation. Recently, Eric van Rossum, global vice president of the Professional Services Industry Business Unit at SAP joined the S.M.A.C. Talk Technology Podcast to share how machine learning is reshaping the future of the professional service industry.

“Until recently, the professional services industry has been pretty immune to disruption,” says van Rossum on the podcast. “You had this asset or this knowledge or this expertise which kind of sits within the human mind. And you would hire these people at a certain cost. You would add 20% or 30% margin depending on what kind of industry you’re in, and you would position that service into the market space.”

Now, machine learning is disrupting this business model. Repetitive and codified professional services such as auditing, certain legal tasks, and call centers are becoming automated. At the same time, new value-added services are being designed. Machine learning is also helping to predict future workforce needs.

From rules-based automation to machine learning

Using technology to automate rule-based services, like basic auditing, is nothing new. Rule-based workflow automations are stagnant, however. Machine learning is different since the algorithm is able to “learn” as it processes data, accelerating performance and capabilities. For example, machine learning algorithms can cut accounting document review time in half. By the end of 2018, machines will author 20% of all business content, including legal documents and shareholder reports.

There are still a lot of back-office processes where people are involved to steer workflow, which is not predictable. Machine learning can help improve enterprise resource planning, ultimately streamlining this “back office workflow.” This is important for companies who currently struggle with workflow management and resource deployment. Only three out of 10 companies say they can identify and deploy the right resources for the right projects.

“One of the ideas that we’re positioning into the market is what we call intelligent ERP or autonomous ERP,” says van Rossum. “In a completely rule-based scenario, there’s a learning element to it. I think if you leverage intelligently a lot of the data which is in the system, you get much better at predicting the right resource.”

This shift in resource identification and deployment also means that more companies will move from full-time professional hires to contingent hires.

“The decision to staff a project really becomes the right time to staff, the right cost to staff, and then the right skill set to staff as well,” says van Rossum. “Machine learning will help a lot with smartly predicting the right resources.”

New outcome-based billing models

In the professional services industry, most companies charge a flat fee for a list of deliverables or an hourly rate for ongoing consulting. As the S.M.A.C. Talk Technology Podcast points out, these companies are rarely held accountable for the performance or quality of the work delivered.

Machine learning may change this by helping businesses move towards new outcome-based price models, rather than time and materials billing.

There are both positives and negatives to these new models, cautions van Rossum, especially for companies that struggle to correctly productize their services.

“As we go more to outcome-based, the potential to create more margin and more profit is there. But, the downside of that is if you get it wrong, you can take an incredible dive on your margin as well. A lot of these contracts are long term. They are going to be based on some sort of outcome-based model, or a usage-based model. And what these professional services firms will need to start thinking about more … you know a lot of these outcome-based engagements are then actually going to be based on (knowledge-based) products which they drive into the market.

Companies must consider how they productize services, including whether the cost for these services is based on a measurable outcome or result. This is a departure from the dominant pricing model where service is priced based on time or skill level, not a specific outcome.

One key component in outcome-based billing would be customer satisfaction. Factoring in business outcomes to service fees could improve customer satisfaction, strengthening customer relationships and improving retention.

Embracing digital transformation for future growth

Companies that embrace digital transformation will be able to retain and grow existing talent, attract new talent, and protect intellectual property. A smarter, more engaged workforce will give these companies a critical edge in today’s competitive market.

For more information on how digitization is transforming the professional services industry, listen to the S.M.A.C. Talk Technology Podcast with Eric van Rossum.


The Human Angle

By Jenny Dearborn, David Judge, Tom Raftery, and Neal Ungerleider

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.


This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.


The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

How Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.

Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!


About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Machine Learning In The Real World

Paul Taylor

Over the past few decades, machine learning has emerged as the real-world face of what is often mistakenly called “artificial intelligence.” It is establishing itself as a mainstream technology tool for companies, enabling them to improve productivity, planning, and ultimately, profits.

Michael Jordan, professor of Computer Science and Statistics at the University of California, Berkeley, noted in a recent Medium post: “Most of what is being called ‘AI’ today, particularly in the public sphere, is what has been called ‘machine learning’ for the past several decades.”

Jordan argues that unlike much that is mislabeled “artificial intelligence,” ML is the real thing. He maintains that it was already clear in the early 1990s that ML would grow to have massive industrial relevance. He notes that by the turn of the century, forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and logistics-chain prediction and building innovative consumer-facing services such as recommendation systems.

“Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success — these are the advances that have powered companies such as Google, Netflix, Facebook, and Amazon,” Jordan says.

Amazon, which has been investing deeply in artificial intelligence for over 20 years, acknowledges, “ML algorithms drive many of our internal systems. It’s also core to the capabilities our customers’ experience – from the path optimization in our fulfillment centers and Amazon’s recommendations engine o Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience, Amazon Go. “

The fact that tech industry leaders like Google, Netflix, Facebook, and Amazon have used ML to help fuel their growth is not news. For example, it has been widely reported that sites with recommendation engines, including Netflix, use ML algorithms to generate user-specific suggestions. Most dynamic map/routing apps, including Google Maps, also use ML to suggest route changes in real time based upon traffic speed and other data gleaned from multiple users’ smartphones.

In a recent article detailing real-world examples of ML in action, Kelly McNulty, a senior content writer at Salt Lake City-based Prowess Consulting, notes: “ML isn’t just something that will happen in the future. It’s happening now, and it will only get more advanced and pervasive in the future.”

However, the broader uptake of ML by enterprises – big and small – is less much less known. A recently published study prepared for SAP by the Economist Intelligence Unit and based on a survey of 360 organizations revealed that 68 percent of respondents are already using ML, at least to some extent, to enhance their business processes.

The report adds: “Some are aiming even higher: to use ML to change their business models and offer entirely new value propositions to customers…… ML is not just a technology.” The report’s authors continue, “It is core to the business strategies that have led to the surging value of organizations that incorporate it into their operating models – think Amazon, Uber, and Airbnb.”

McNulty notes that there are both internal and external uses for ML. Among the internal uses, she cites Thomson Reuters, the news and data services group, which, after its merger in 2008, used ML to prepare large quantities of data with Tamr, an enterprise data-unification company. She says the two partners used ML to unify more than three million data points with an accuracy of 95 percent, reducing the time needed to manually unify the data by several months and cutting the manual labor required by an estimated 40 percent.

In another example of enterprise use of ML, she notes that GlaxoSmithKline, the pharmaceuticals group, used the technology to develop information aimed at allaying concerns about vaccines. The ML algorithms were used to sift through parents’ comments about vaccines in forums and messaging boards, enabling GSK to develop content specifically designed to address these concerns.

In the financial sector, ML has been widely used for some time to help detect fraudulent transactions and assess risk. PayPal uses the technology to “distinguish the good customers from the bad customers,” according to Vadim Kutsyy, a data scientist at the online payments company.

PayPal’s deep learning system is also able to filter out deceptive merchants and crack down on sales of illegal products. Additionally, the models are optimizing operations. Kutsyy explained the machines can identify “why transactions fail, monitoring businesses more efficiently,” avoiding the need to buy more hardware for problem-solving.

ML algorithms also underpin many of the corporate chatbots and virtual assistants being deployed by enterprise customers and others. For Example, Allstate partnered with technology consultancy Earley Information Science to develop a virtual assistant called ABIe (the Allstate Business Insurance Expert). ABIe was designed to assist Allstate’s 12,000 agents to understand and sell the company’s commercial insurance products, reportedly handling 25,000 inquires a month.

Other big U.S. insurance companies, including Progressive, are applying ML algorithms to interpret driver data and identify new business opportunities.

Meanwhile, four years ago, Royal Dutch Shell became the first company in the lubricants sector to use ML to help develop the Shell Virtual Assistant. The virtual assistant enables customers and distributors to ask common lubricant-related questions.

As the company noted at the time, “customers and distributors type in their question via an online message window, and avatars Emma and Ethan reply back with an appropriate answer within seconds.” The tool was initially launched in the U.S. and UK but has since expanded to other countries and reportedly can now understand and respond to queries in multiple languages, including Chinese and Russian.

In the retail sector, Walmart, which already uses ML to optimize home delivery routes, also uses it to help reduce theft and improve customer service. The retail giant has reportedly developed facial recognition software that automatically detects frustration in the faces of shoppers at checkout, prompting customer service representatives to intervene.

Among SAP’s own customers, a growing number are implementing ML tools, including those built into SAP’s own platforms and applications. As SAP notes, “Many different industries and lines of business are ripe for machine learning—particularly the ones that amass large volumes of data.”

The manufacturing, finance, and healthcare sectors are leading the way. For example, a large European chemicals company has improved the efficiency and effectiveness of its customer service process by using ML algorithms to automatically categorize and send responses to customer inquiries.

In the mining sector, Vale, the Brazilian mining group, is using ML to optimize maintenance processes and reduce the number of purchase requisitions that were being rejected causing maintenance and operational delays in its mines. Before implementation, between 25 percent and 40 percent of purchase requisitions were being rejected by procurement because of errors. Since implementation, 86 percent of these rejections have been eliminated.

Elsewhere a large consumer goods company, the Austrian-based consumer good company, is using ML and computer vision to identify images of broken products submitted by customers from the over 40,000 products in the company’s catalog. The application enables the company to speed up repairs and replacements, thereby improving customer service and the customer experience.

Similarly, a global automotive manufacturer is using image recognition to help consumers learn more about vehicles and direct them to local dealer showrooms, and a major French telecommunications firm reduced the length of customer service conversations by 50 percent using chatbots that now manage 20 percent of all calls.

But not every enterprise ML deployment has worked out so well. In a highly publicized case, Target hired a ML expert to analyze shopper data and create a model that could predict which female customers were most likely to be pregnant and when they were expected to give birth. (If a woman started buying a lot of supplements, for example, she was probably in her first 20 weeks of pregnancy, whereas buying a lot of unscented lotion indicated the start of the second trimester.)

Target used this information to provide pregnancy- and parenting-related coupons to women who matched the profile. But Target was forced to modify its strategy after some customers said they felt uncomfortable with this level of personalization. A New York Times story reported that a Minneapolis parent learned of their 16-year-old daughter’s unplanned pregnancy when the Target coupons arrived in the mail.

Target’s experience notwithstanding, most enterprise ML projects generate significant benefits for customers, employees, and investors while putting the huge volumes of data generated in our digital era to real use.

For more insight on the implications of machine learning technology, download the study Making the Most of Machine Learning: 5 Lessons from Fast Learners.