Elevating Women To Build A Workforce Of Equal Opportunity

Johann Wrede

March 8, 2017, was labeled A Day Without a Woman. It was initiated to highlight the economic power of women and call attention to the injustices they continue to face. Women were encouraged to take the day off as a form of protest. In the midst of this, I found myself reflecting on the many women who have shaped my journey.

My single mother, who showed me how to persevere even when the odds were stacked against her. The art teacher who showed me that you can manage conflict with grace and creativity. The English teacher who showed me what a sharp and curious intellect looks like. The U.S. Air Force Captain who showed me that compassion and mental toughness are not mutually exclusive. And the many women with whom I work with today who have taught me about managing complexity, communicating clearly, and what it means to be an advocate.

A Day Without a Woman was certainly effective in underscoring the consistent, significant, and vital impact that women have on everyone around them. But there is so much more to the story than a single day of awareness can tell.

Research has proven that companies with gender and ethnic diversity perform up to 35% better financially than companies without. While this is true, it remains that only 16% of senior leaders are women, and that’s in the United States – the statistics get even worse as you go around the globe (12% in the UK, six percent in Brazil). Women continue to be one of the largest pools of untapped labor: globally, 655 million fewer women are economically active than men.

The adverse effect this has on companies (and the global economy) becomes clear when you consider the link between the presence of women in top management teams and the performance of a company. The 2017 Women Matter report from McKinsey looked at 300 companies around the world and found a difference in return on equity of 47% between the companies with the most women on their executive committees and those with none. Going a step further, these companies had better results in key areas of organizational performance, such as accountability, direction, work environment, and employee motivation.

These results, consistent with my own experiences in business, are evidence that women consistently demonstrate certain characteristics that are beneficial to organizational success. Professional development, expressing expectations, rewarding success, role-modeling, inspiration, and participative decision making are all qualities that women possess more often and have been shown to strengthen overall business performance.

It’s time for men to become allies and advocates of female colleagues. Not just because it’s the right thing to do, but because everyone’s outcomes are better when we do. We must finally acknowledge the concrete value that diversity and inclusiveness bring to a team and to an organization. With that value as a foundation, the case for female leadership becomes obvious, permitting real progress towards closing the gender gap and providing women with equal career-growth opportunities.

Take a moment to think about the impact we could have if every man took the time to encourage a woman to apply for that new job, speak up in an executive meeting, or generally reassure them of their capabilities and value on a regular basis.  As you consider that, understand that the historical pattern of discrimination against women in the workplace has perpetuated a lack of self-confidence compared with their male peers. My first-hand experience as a leader has taught me that it’s not just enough to ensure that women have an equal shot at new opportunities. I actively need to help them overcome a pattern of behavior that has been handed down over generations so that they can confidently pursue those opportunities.

Truly implementing this change will require more than talking – it will take action – and I’m very proud to be part of an organization that is championing this type of societal change. Just this past October at the SAP Hybris Global Summit in Barcelona, a panel of my female colleagues discussed what it means to be a woman in the technology industry specifically, as well as their advice for how women can take action to gradually make progress.

The insight they shared is valuable. Among the steps they saw as making the biggest impact: women empowering other women, teaching confidence to girls at a young age, building female relationships through personal connection, and trusting your own instincts. With these shared values in mind, we can all do our part to empower and build confidence in the women we care about. My colleague Shalini Mitha noted that feelings of inferiority are often replaced with confidence once women feel empowered.

It will take time and concerted effort, but it is my hope that as we move into the new year, every person, whether a leader or individual contributor, will renew their efforts to create an environment that values all genders equally. Let’s commit to work together to support our female colleagues and understand that when they thrive, everyone wins.

Watch the panel on female empowerment led by women warriors in technology here.

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


About Johann Wrede

Johann Wrede is the Global Vice President, Audience, Brand, & Content Marketing, for SAP Customer Engagement & Commerce. He leads a team of audience marketers to produce original content and events which feed our global marketing campaigns and digital channels to drive both awareness and pipeline.

Technology In The Public Sector: Possibilities And Challenges

Regina Kunkle

The governments of nations around the world have responsibilities to their citizens. For example, they need to encourage economic growth, keep their citizens safe, and provide public services. Yet governments at different levels often encounter challenges in achieving citizens’ expectations. Advances in technology can help, but challenges must first be overcome.

Technology within government

In a S.M.A.C. Talk podcast, Dante Ricci of SAP’s Global Public Services team discussed how governments are using technologies like the Internet of Things (IoT), analytics, and blockchain. Dante noted that machine learning is one area the public sector is adopting within enterprise apps. Another example is adding sensors to help traffic flow better.

Governments that adopt a digital core are able to bring together different sources of data. This helps them better understand information between different agencies and levels to make more informed decisions.

Governments can also adopt blockchain to provide transparency while keeping transactions secure. Blockchain holds broad potential for governments to perform functions such as transferring funds and keeping records within a secure ledger, Liz Farmer explained in Governing. Dante added that artificial intelligence (AI) could “automate everything from the constituent service experience to accurately detecting fraud, waste, and abuse better.”

For the World Economic Forum, Gregory Curtin noted that “augmented reality, or AR, has been called the next big paradigm shift in computing.” It’s expected to have future potential for changing governments as well. This advanced technology could provide AR windshield displays, for example, with real-time data for public transportation and emergency service vehicles. Augmented reality could help police practice their responses to emergency or disaster scenarios and help citizens prepare.

The needs and timeline for implementing different technologies vary, depending on the level of government and agency. For example, Dante expects city governments to progress technologically more quickly than state and federal government agencies.

Challenges of the public sector

While the public sector is taking advantage of technology that’s popular in other industries, it also faces unique challenges. “A lot of governments, regardless of technology, are not able to fulfill the mission the way the citizens expect,” said Dante in the S.M.A.C. Talk podcast. He offered examples such as citizens’ struggles to engage and workers’ struggles to make their voices heard.

Regulatory restrictions can complicate progress. Governments must secure citizens’ and classified information. The breadth of agencies and organizational silos between departments adds still more complexity.

The U.S. government must modernize legacy systems to take full advantage of innovative technologies like the Internet of Things and machine learning. In FCW magazine, Mike Conger and Michael Preis pointed out that within the next 10 years, the government could save more than $110 billion by eliminating operations and maintenance of outdated systems. The shift would also create a more efficient government that offers its citizens better digital services.

Dante explained that the public sector also needs to adopt policies that enable the use of technology. Even if an agency adopts advanced technology, it may be unable to use it if policies do not support its use. Critical to success is policy change that happens in accordance with the adoption of new technology. This requires agreement on questions such as whether to enable citizen services on mobile devices and utilize anticipatory alerts. Further, agencies do not always have the resources to help them adopt new technologies. Overall, Dante believes that to be successful, policies must be made with citizens’ needs in mind.

While many unresolved details remain, emerging technologies could help governments become more efficient and better connected to their citizens.

To hear Dante’s full overview of technology’s potential in the public sector, listen to the S.M.A.C. Talk podcast.


Regina Kunkle

About Regina Kunkle

Regina Kunkle is responsible for the State and Local/Higher Education (SLED), as sub-industry of the U.S. public sector industry, at SAP. Regina is dedicated to helping governments transform to respond to changing regulations and citizen needs, streamline and simplify processes, and share vital information across agencies for enhanced decision making and performance.

Mapping A New Strategy To Fight Opioid Addiction

Scott Campbell

Opioids infiltrate nearly every facet of U.S. society—they are found in every community and every walk of life—and addiction and overdose deaths are on the rise. Opioid addiction has become such a pervasive epidemic that the drugs are now a bigger killer in the United States than breast cancer, guns, and vehicle-related accidents.

Synthetic opioids such as fentanyl, fentanyl analogs, and tramadol accounted for two-thirds of the 63,000 drug-related deaths in the U.S. in 2016, according to the National Center for Health Sciences. Opioid addiction knows no racial, socioeconomic, or geographic lines, which makes the problem even more difficult to solve.

Today, more and more communities are looking toward technology to address the opioid problem. Specifically, innovative new solutions involving geographic information systems (GIS) are helping communities identify where and how opioid usage is most pervasive, enabling them to better allocate resources to prevent and/or mitigate addiction.

Esri, an SAP partner, has developed a spatial mapping and analytics software solution that leverages data residing in an in-memory computing platform, and this tool is now being implemented in jurisdictions across the United States.

With this approach, a powerful geographic platform allows users to leverage tools like “story maps” that turn raw data resources into powerful insights on any topic. “Whether it’s the opioid crisis, refugees in Bangladesh, or tracking a killer whale population, mapping technology has the ability to help organizations and businesses make more intelligent and evidence-based decisions,” said Dr. Este Geraghty, chief medical officer and health solutions director at Esri.

“The idea is to use geographic data to improve decision-making and resource allocation. In the health sector, we can help organizations transform the way they do business,” said Geraghty, a physician who joined Esri more than three years ago. “While we all love data, it’s not very inspiring to simply talk about the amazing things you can do with geographic data. Rather, we get motivated by helping people solve real problems and we know the power of geographic intelligence to do that. Our health and human services team focuses their energy on tackling the big issues that impact our personal and community’s health and well-being.”

To Geraghty, the opioid epidemic is a personal mission. One of her colleagues, Jeremiah Lindemann, lost his brother to a drug overdose in 2007. Lindemann used his technical background to launch a crowdsourced story map called “Celebrating Lost Loved Ones,” where people can contribute a photo and narrative in memory of someone who died from opioids. So far, more than 1,500 people have added the names of mothers, fathers, sons, daughters, and friends.

“When you explore that map, you begin to notice that the opioid crisis is affecting people from all walks of life—people who had families. People who loved them and were loved by them. It’s heartbreaking. The story map is meant to allow people to share their grief, but it also reminds us that opioid addiction can happen to anyone, anywhere,” Geraghty said. “Jeremiah inspires all of us. He’s spent the last several years broadly sharing his story and his perspective on the topic. He’s even testified before Congress about the dangers of opioids. Our focus on this issue and our drive to be a force for good all began with him.”

Mapping a problem to find a solution

The first step in the GIS mapping technology is to identify where overdoses and deaths are happening. That information is mapped to help police and other organizations determine where to best allocate their resources.

“Everything happens somewhere, but so many people still take ‘somewhere’ for granted. It takes a little practice to think spatially and use location information strategically,” Geraghty explained. “But after a while, it becomes natural to consider relevant questions like, What’s the geographic extent of the problem? Where is it worse? Where is it not so bad? Are there patterns in the data? What can I learn from less impacted communities?

“Most communities don’t have the means to evenly spread resources across every jurisdiction. That means that they need to make strategic decisions about where to do more and what kinds of interventions will have the greatest impact in different places. It’s never the same thing from one place to another.”

Sometimes a geographic analysis will show that one or more epidemics overlap. We call that a syndemic. Understanding these spatial relationships can be critical to formulating the best strategy, Geraghty said.

For example, when one California county experienced a major Hepatitis A outbreak, GIS indicated that the disease correlated spatially with opioid and homeless epidemics in the county and that interventions would require consideration of those co-occurring issues. Public restrooms in the county were mapped and put on a regular cleaning schedule, sidewalks in and near homeless encampments were sanitized, handwashing stations were placed in relevant areas, and vaccinations were offered to high-risk populations in their neighborhoods. These methods and more were deployed in the right places to help the right people and ultimately stop the outbreak.

“You want to get out ahead of it. To do that, you need to understand where people are dying, when, and why,” Geraghty said. “Information can be a powerful weapon for communities to help people.”

For example, in one U.S. county, the highest cluster of overdoses comprised males in their 30s who died between 6 pm and 8 pm. The GIS technology showed that most of the county’s resources were working between 8 am and 5 pm and had been targeting females at risk. “We showed them the spatial data, and now they’ve allocated their resources more effectively,” Geraghty said.

The benefit of understanding the geographic context for such complex problems cannot be understated, according to Geraghty. For example, one community leader promised to eliminate all the “pill mills” in his jurisdiction. That effort was successful, but an unintended consequence was that addicts turned to other drugs, such as heroin, which then became a bigger problem. “Taking a broader view of the community might have helped avoid that consequence,” Geraghty noted. “If you cut off the supply of the drug without also considering the continuing demand, you can run into trouble. Our solution includes a tool for providing resources that could decrease opioid demand, like providing alternative pain control modalities that prevent addiction and identifying treatment options for those already addicted.”

Developing a solution—together

Increasingly, local governments are recognizing that help from the state and federal level may not be enough to address the problem. They need solutions that help them do more with their resources.

“The big thing with the opioid crisis is that it’s a concern across multiple organizational sectors. This is not just a public health or a mental health or a health care problem. It’s also a police problem, pharmaceutical company problem, an emergency personnel problem, and a government funding and policy problem, to name a few. A lot of different entities are involved in addressing the crisis. Communication and collaboration are critical, and spatial mapping and analytics can facilitate those needs,” Geraghty said.

One challenge is getting many disparate organizations and communities to share information, a problem exacerbated by protections around personal health information. “Organizations tend to be reluctant to share data, even when it is appropriate and legal. Saying ‘no’ to sharing is undoubtedly the best answer to avoid potential data breaches, but it doesn’t help in addressing the problem,” Geraghty said. “Cross-sector collaborations and appropriate data-sharing agreements need to increase if we’re going to make any real progress. In turn, in-memory computing helps democratize and distribute the data.”

“Opioids have such a dramatic impact on our lives. Addiction affects our families and our society, it diminishes tax revenue and productivity. It even impacts the needs of our foster care systems. It can cripple an entire community,” Geraghty said. “We’re a B2B company and we’re passionate about providing authoritative health organizations with outstanding tools to support their populations. This is important to all of us.”

This article originally appeared on SAP Innovations4Good. 


Scott Campbell

About Scott Campbell

Scott Campbell is a senior IT channel communications specialist at CommCentric Solutions, a Tampa, Fla.-based content marketing company, where he writes blogs, research reports and other content on a regular basis. Prior to that, he spent more than 20 years as a journalist, most recently as an editor at CRN magazine, where he earned several national writing awards.

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.