2018 Predictions, Pt. 4: Emerging Digital Technologies Will Make An Impact On Business

Jacqueline Prause

Once again, we invited thought leaders with diverse areas of expertise to peer into the 2018 Game-Changers Radio Crystal Ball. What did they see?

Cloud-first business management accelerates in the small and medium enterprise market. Companies revamp talent frameworks for 21st century, real-time business. Digital transformation helps governments delight citizens with new digital services, like chatbots. The Internet of Things (IoT) shakes up business to the point of delivering Everything-as-a-Service. Cyberattacks continue. 5G networks come online, revving up Internet speeds to a rate possibly 75 times faster than current velocities.

2018 predictions overheard on SAP Radio: Cloud First for SMEs. New talent frameworks for real-time business. Governments delight citizens with digital services. IoT leads to Everything-as-a-Service. 5G networks bring new business opportunities.

These are some of the predictions heard on Game-Changers Radio 2018 Predictions, Part 4, Presented by SAP, the fourth installment of a five-part series, that aired live January 10, 2018. Host Bonnie D. Graham asked 15 leading experts, academics, and business influencers to share their predictions for what the coming year holds for industry, business, the world, and technology.  The predictions are the experts’ personal points of view and do not necessarily represent the views of SAP.

What these thought leaders foresee

1. 2018 will take us into the world of interface simplification. For example, my smart phone will become a really smart phone that knows my calendar and the traffic conditions; based on those conditions, it will set up my daily schedule automatically. What’s required is finding how we interact with machines and how these machines interact with us. Advances in AI are creating a new definition for machine interaction. In 2018 we’ll start seeing unprecedented products. We’ll see a progression from individual artificial intelligence (AI) and IoT products to systems that will plan and will make our lives easier.

– Amit Rustagi, Big Data Technologist, Western Digital

2. In the area of IoT, I foresee a lot of things (8 IoT Predictions for 2018). The IoT cloud platform market is going to consolidate quickly. The IoT hype is going to finish and we’re going to move into possibly a “trough of disillusionment” that precedes mainstream adoption. IoT architecture will evolve from data ingestion and analytics to an intelligent event-driven solution for end users. Digital twins will evolve from concepts to blueprint and implementation for data sharing within and across companies.

– Tom Raftery, Global Vice President, Futurist, IoT Evangelist, SAP

3. I foresee three things and they are all related. 1.) Cloud first: There will be more cloud business, especially in small and medium enterprises, and more companies will bring apps into the cloud. 2.) IoT: Traditional business will change a lot, even to something like Everything-as-a-Service (EAAS). The pros include things like artificial intelligence and machine learning, but the negatives might be crime-as-a-service, the darknet, or even ransomware-as-a-service. 3.) Security: We’ve heard about the chip meltdown and we’ll see more events like this in 2018.

– Frank D. Geisler, CEO and CMO, ERPsourcing AG

4. Social selling will go beyond the basics. Sales reps are now asking how they can bring in stuff like AI and machine learning into the sales process to improve engagement and provide a better experience for the customer. There will also be a greater emphasis on how Marketing needs to transform to fill the needs of the Sales organization. There needs to be a complete and total alignment in the digital transformation between Sales and Marketing. That will hopefully happen in 2018.

– Kirsten Boileau, Head of Regional Engagement and Social Selling, SAP

5. In 2018, knowledge work productivity will increase through machine learning due to automation.

– Chandran Saravana, Senior Director, Advanced Analytics, SAP

6. We need to combine transactional and analytic systems into transalytic systems and stop thinking about these as two separate systems. 2018 is going to be the year we’ll see major corporations collapse these two systems together, so that you have simplified architecture and can move at the pace of business. Second, in the automotive market, the evolution continues towards autonomous assets. There will be a lot of movement in the vehicle-to-vehicle area and the vehicle infrastructure will become more pervasive. The driving factor will be a price drop for lidar, the technology the vehicles use for situational awareness.

– Bill Powell, Director of Enterprise Architecture, Automotive Resources International (ARI)

7. It will be a year of digital transformation, but it will be pushed by human needs, consumerism, and emerging tech. 2018 is going to be the year when more organizations use empathy to start digital transformations. They need to know what their clients’ needs are and really know their customers. This touches on emerging technologies like AI, chatbots, and IoT. Think of devices like Alexa and digital assistants; according to IDC, we’ll have about 4,800 of these interactions every day, or about one every 18 seconds. How does this affect government? How do you delight citizens? In the UK, for example, they’re using Alexa to report crime. In Mississippi, they’re using a chatbot called Ask Missi as a system to get information.

– Michele Hovet, Client and Partner Innovation Director, KSM Consulting

8. We’ll see more vertical and geographic specialization into micro-verticals. For example, providing not just healthcare solutions for South Africa, but also solutions for clients in neighboring Botswana and Nambia. Second, we’ll see increasing specialization in our partner firms, for example, specifically doing work for omnichannel ecommerce for consumer products companies. Third, we’ll see increased interoperability of capability – meaning partners that are able to combine solutions, like artificial intelligence around managing complex supply chains in the apparel footwear industry. Lastly, companies that can link their core mission to doing well in the local community and the world will win.

– Roger Quinlan, Senior Vice President of SAP Global Partner Managed Cloud and Cloud BPO

9. There will be an increase in the number of organizations that recreate their talent frameworks to support the 21st century world of work. The game-changing organizations of 2018 will be the ones brave enough to move away from their traditional performance frameworks. The winners in 2018 will be those who focus on underpinning their people frameworks more concretely in impact and contribution, which as a consequence will enable them to map and deploy people in a real-time manner.

– Nathan Ott, Co-founder and CEO, The GC Index

10. We’re going to see more incentives for people to have certain behaviors. You need to be able to target what behaviors or actions you want, and then be able to incentivize, whether it involves salespeople, consumers, or buyers. There will be more incentives, but with AI and other technologies, you will be able to know exactly what you should be incenting on and what results you want to get.

– Cara DeGraff, Vice President of Product Management, Vistex, Inc.

11. Data analytics will mature to continue transforming and improving the way we communicate with customers. To make this happen, in 2018 companies will need to begin thinking about transformation as evolutionary. Instead of initiatives and projects that have a one-and-done basis, we’re going to think about them on an evolutionary basis. In 2018, we’re going to get better at predicting customer behaviors. We’re going to get better at personalizing every experience to give customers what they want most: speedy, painless, real-time interaction 24/7.

– Vanessa Edmonds, Leader of Research, Innovate, Materialize (RIM) Solutions at TMG

12. In 2018 customers will leverage technologies in new ways to advance their state of maturity. There’s been a lot of focus on buying new technologies, like in-memory data platforms, AI, and predictive analytics. I foresee the application of those solutions in such a way that technology isn’t really a question anymore. I foresee a lot of innovation, particularly in enterprise performance management, where it was difficult in the past to automate manual tasks. In 2018, they’re going to focus on automating the analysis of the data that is now at your fingertips.

– David Den Boer, Founder, Column5 Consulting

13. We’re going to see companies reassess their strategic technical plan – possibly even stopping some of the roadmaps and re-evaluating options for the cloud, as well as moving forward with ERP transformations and improving total cost of operations by streamlining business processes and technical architecture. There will be quite a bit around end-to-end transformation and being more innovative and proactive in business processes.

– Windie Wilson, Solution Advisor, Supply Chain Management, SAP

14. At a personal level, we’re getting closer to the world of the movie “Her” with digital assistants and cognitive computing – but going mobile. For example, we’re going to see headphones that you really can’t see but that will have digital assistants built in and will last all day. Things are going to conform to you – for example, your rental car will adjust to you. In business, we’re going to see identity and trust move to the forefront. With the European General Data Protection Regulation (GDPR) and the number of data breaches that are being reported, people will care more about trust and reputation. For a company to be successful, they’re going to have to show how they react and how they maintain your privacy to encourage you to trust and buy from them. At the same time, we’re going to see that the Internet of Everything, machine learning, and AI will become how businesses allow their employees to focus on their work versus keeping the lights on.

– Brian Katz, Enterprise Architect, Oath Technology

15. For 2018 in the automotive world, we’ll see more Connected Autonomous Shared and Electrified (CASE) strategy. 5G networks will finally come online, affecting vehicles, drones, robots, and sensors. The providers of the structures, hardware, and networks predict Internet speeds that are 75 times faster than currently available. For a lot of manufacturers, that means better productivity and an increase in the data monetization. The second prediction relates to autonomous vehicles, where we’re getting closer to level 4 and 5 autonomy in the automotive space. For regular drivers, what’s more important is the move to advanced driver assistance systems (ADAS), which we will see more of in higher-end vehicles.

– David Parrish, SAP Senior Global Marketing Director for the Industrial Machinery and Components Industry

You can hear the full show at
SAP Game-Changers Radio 2018 Predictions, Part 4

SAP Game-Changers Radio 2018 Predictions Special Upcoming Shows

For dozens of other insightful predictions that can impact you and your business in 2018 and beyond, listen to all five episodes of SAP’s Game-Changers Radio 2018 Predictions Special.

In case you missed previous episodes, you can listen to recordings of Part 1, Part 2, and Part 3 of the series. You can listen to the shows live here.

Experts’ predictions have been edited and condensed for this space.
Top image via Shutterstock

This article originally appeared on SAP News Center.

About Jacqueline Prause

Jacqueline Prause is the Senior Managing Editor of Media Channels at SAP. She writes, edits, and coordinates journalistic content for SAP.info, SAP's global online news magazine for customers, partners, and business influencers .

From Digital To Intelligent: Making The Most Of Machine Learning

Dr. Markus Noga

Businesses are no longer just digital – they are becoming increasingly intelligent. A recent survey of 360 organizations by the Economist Intelligence Unit and SAP showed that, on average, 68% of them use machine learning to enhance their business processes. Now, businesses are moving beyond just improving performance across the existing business, instead moving towards developing entirely new business models, optimized processes, and value propositions.

For businesses, machine learning can enable software to adapt and improve the execution of tasks and processes autonomously. This saves time and money while empowering employees to focus on value-adding, strategic, and creative tasks. Businesses that have already benefitted from the power of machine learning are called Fast Learners, and they experience benefits from improved customer satisfaction and increased profitability. Some have improved customer support with machine learning chatbots, and nearly half of all Fast Learners expect revenue growth of more than six percent from 2018 to 2019.

But what sets Fast Learners apart from their competition? What makes them so willing to take the perceived – yet much lower than expected – risk of embracing this new technology? As I work with them in implementing machine learning across their businesses, five key traits become more obvious every day:

The five traits of fast learners

  1. C-level, strategic priority: Fast Learners’ senior-most management sees the strategic value of machine learning and fosters a workplace environment that is not afraid of change.
  1. Increased competitive differentiation: Fast Learners see machine learning as a pragmatic yet innovative way to stand out in a crowded market, not as a gimmick or fad.
  1. New revenue and profitability: Machine learning is a valuable source of revenue and profitability for Fast Learners. They look to bring about fundamental, rather than incremental, change and believe machine learning’s potential in business model innovation is enormous.
  1. Key processes close to home: Spending money on locally sourced business functions is important to Fast Learners – they spend more on local functions than they do on ones from low-cost regions.
  1. Enterprise-wide strategy: Fast Learners look at what machine learning can do for their business in a holistic way rather than forcing it into a purpose that may not be the best fit.

Of these traits, I believe that C-level strategic priority and enterprise-wide strategy are the most important. These two traits often go hand in hand – where senior management is aware of the opportunities and limitations of machine learning, they are more likely to look at what the technology can do for their business in a holistic way with enterprise-wide strategy. The other traits simply follow naturally.

Embracing the hype for improved business practices

Equally apparent are the reasons businesses do not implement machine learning. Most commonly, they lack those aforementioned traits. But often, there are also misconceptions about the effort and cost required to implement machine learning solutions. Many simply don’t know where to start or are afraid to fall victim to yet another technological fad.

But the machine learning hype is well-warranted. Fast Learners who began their machine learning journey before most people had ever even heard of the technology have since created a lasting impact across the breadth of their organizations that goes far beyond hype. For example, one Chinese shoe company used machine learning to enable customers to design their own shoes and wear them within one week. Your business can launch such lasting innovations, too.

As you embark on your own machine learning journey, I recommend taking a closer look at what other organizations in your space have done. Are they using it to better connect with customers through smart marketing campaigns? Are they better responding to customer concerns after integrating it with contact centers? You’ll soon realize that there are plenty of low-risk machine learning initiatives you can pilot as you test the waters.

Interested in learning more about the five traits of Fast Learners? Read the study here

Dr. Markus Noga

About Dr. Markus Noga

Dr. Markus Noga is vice president of Machine Learning at SAP. Machine learning (ML) applies deep learning, machine learning, and advanced data science to solve business challenges. The ML team aspires to building SAP’s next growth business in intelligent solutions, and works closely with existing product units and platform teams to deliver business value to their customers. Part of the SAP Innovation Center Network (ICN), the Machine Learning team operates as a lean startup within SAP with sites in Germany, Israel, Singapore, and Palo Alto.

Higher Education Is A Business – Is That So Bad?

James Krouse

There is little question that education is one of the most important services to ensure we evolve and grow as a society. Education can be considered a service, and services require resources. Within the modern, free-market, capitalist world, “resources” means money. The higher-education mission remains the purpose, but do not confuse it with the practicality of survival or the ability of the institution to continue to deliver that service to the student (customer).

Yet, the economic challenges facing institutions of higher education are significant, and the divide between revenue and expenditures continues to separate. These challenges increasingly demand creative management to ensure viability and sustainability, just like a business.

Taxes across many jurisdictions are pronounced, and donations, grants, and charity are largely insufficient to support the costs and maintenance of higher education institutions and teaching staffs. So, the primary means to pay for college lie with continual escalations of tuition and fees borne by the student body. The student body is finding it difficult to absorb those increasing costs, especially in the face of questions on the return on investment and preparation for the job market.

So, what are institutions to do? They must think creatively and adapt to meet changing economic and environmental factors and students’ expectations. That means more focus on costs and expenditure, just like business. It also means exploring alternate revenue streams and creative finances, just like business. Increasingly, advanced technology provides the key to creative business thinking. Institutions are utilizing new, aggressive technologies to improve efficiency and reduce resource waste. They are utilizing technology to improve the student (customer) experience by embedding analytics to manage systems and support in real time, all focused toward maximizing successful outcomes.

Finally, the economic squeeze for economic resources in higher education is breeding competition for those customers across the institutional landscape. Institutions are increasingly leveraging advanced marketing and social media technologies to maximize outreach to prospective students. The new costs are reviewed as a necessary investment, or a cost of doing business. Without a solid and broad enrolled student foundation, the institution could not exist. It may have been unheard of 20 years ago for a university or college to go out of business, but institutions are failing, and in increasing numbers, today.

Institutions that are creative, open to the advances that technology can provide in maximizing economics, and seeking to manage their operations with an eye toward efficiency will survive and thrive and continue their purpose-driven mission to educate. Other institutions may be lost as a necessary corrective market action.

Learn more, download this higher-education whitepaper to get a more in-depth understanding of how your institution can embark on your digital transformation journey.

Join us at SAPPHIRENOW to hear from leading experts on how they are shaping their journey enabled by SAP.

James Krouse

About James Krouse

James Krouse is the director of Global Solutions Marketing at SAP. He is the global strategic marketing lead for the healthcare and higher education industry groups and is responsible for tailoring GTM strategies, analyst relations, government relations, positioning, and messaging.

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.


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.