Boost Employee Engagement With Artificial Intelligence And Machine Learning

Stephan Amling

Employee engagement is a dynamic and fluid metric. It has a direct bearing on productivity and business goals. Which is why, it is on every business agenda today. In truth, annual surveys do not match up to the real result, because what an employee feels during the annual survey is not the same say, after the most recent direct manager interaction. That’s why business leaders need real-time data and insights on Employee Engagement to respond in a much more timely and individualized manner.

Let’s take a look at some of the Artificial Intelligence (AI) and robotics technologies that exist today and could help HR and the business to get a more real-time insight into the state of the workforce. It is worth mentioning that some of those technologies immediately raise various concerns and might not be considered appropriate, but it is a matter of fact that they exist, that they are already being used outside of the workplace, and that HR and business leaders need to start looking at them and make an educated decision if and how to leverage them.

Algorithms that measure your smile

Thanks to various sophisticated face recognition technologies, understanding shopping customers’ but also employees’ state of mind is now a real capability. Such technologies that are partly even available as public cloud services, can analyze headshot photos of employees as they enter, walk or leave the workplace, identify gender, classify the age group, and most importantly, rate the emotional state of the respective individual in real-time on a scale from very unhappy to very happy using Machine Learning (ML).

“Email sentiment analysis” using ML can mathematically calculate the emotional attachment of an employee to his/her organization based on his/her emails. From deducing who is highly engaged with the company’s strategy to who is most likely to resign in the near future, which allows the organization to initiate preventive interventions.

In an excellent example, Unilever is experimenting with using algorithm-based evaluations, video techniques as well as automatic data gathering for the hiring process. If successful, it will help to free up a significant portion of their recruitment team capacity and re-deploy them to advisory tasks, leaving only the final interviews to human recruiters.

In another example, SAP is using ML technologies in their SuccessFactors solutions to identify the unconscious bias in developing job postings or in calibrating team performance, or chat-bots to automate service request processes and evolving traditional system user interfaces to use written and even spoken natural language. 

Robots: Moving from shop floor to service desk

From doing repetitive tasks on the shop floor, robots are now performing tasks that so far have been done by knowledge workers in the services industries. From tax accounting and auditing to drug research – the contribution is significant. In India, one of its leading banks has robots to service branch customers. Chatbots are already able to handle the majority of HR service requests in a shared service center and they can even do recruiting interviews.

Robot teachers for one-to-one learning

Improving engagement scores doesn’t stop with gathering analytics; what lies in the root of the matter is innovative learning methods that boost employee productivity. Likewise, recommendations are no more synonymous only with online shopping. They empower HR or people leaders to identify the best matching training requirement for an individual, while robots monitor learners, adjust the content, recommend repetitions, and draw conclusions about the learner’s effectiveness. 

Gamification and virtual reality for the Pokemon era employee

Employee engagement scores soar when learning is fun, engaging, multi-sensory, and goal-oriented. This is where gamification plays a crucial role. It makes the whole experience holistic, and the competition boosts learners’ motivation. Similarly, there are virtual reality technologies that offer seamless experience on the training task outcome during the training sessions. Social learning and video-based learning makes learning community driven and continuous, a part of the daily job, rather than a separate activity.

The road ahead for HR with AI

None of the above examples are science fiction anymore. They are available today or are coming soon, most likely even in a disruptive way. But how exactly this change will look like, is up to us as business leaders to define and to design. Hence we need to understand those technologies, evaluate their capabilities as well as their risks, and then make a conscious decision how to use them. As always, they are just tools, and tools are there to support and serve a business purpose. It is up to us to stay in command of this process and decide how we want to leverage those technologies for the benefit of our people and for the benefit of our businesses. SAP, for example, is currently not having any plans to leverage video information for automated mood detection or email sentiment analysis to understand the engagement of its employees.

Turn insight into action, make better decisions, and transform your business. Learn how.

Stephan Amling

About Stephan Amling

Stephan Amling is a Senior Vice President at SAP SuccessFactors, based out of Singapore. Prior to that, Stephan was Chief Operating Officer (COO) for SAP’s Human Resources function and the lead of SAP’s global HR Business Transformation Program. He is bringing 30 years of management consulting together with his expertise and innovative thinking in HR as well his deep practical experience in cloud-based HR technologies. On that basis, Stephan is passionate about helping organizations to develop an ambitious people vision while actively supporting them in executing their digital transformation journeys and delivering on their initial objectives, through the use of state-of-the-art technologies.

Is Employee Burnout Fatal To The Workplace?

Bruno Kindt

Psycho-social risks and work-related stress are among the most challenging issues in occupational safety and health, significantly impacting the health of individuals and organizations. According to EU-OSHA, about half of European workers consider stress common in their workplace; it contributes to roughly half of all working days lost. Like many other issues surrounding mental health, stress is often misunderstood or stigmatized. However, when viewed as an organizational rather than an individual issue, psychosocial risks and stress can be just as manageable as any other workplace safety and health risk.

Conditions that can lead to stress, burnout, and depression include excessive workloads; conflicting demands and lack of role clarity; lack of involvement in making decisions that affect the worker; lack of influence over the way the job is done; poorly managed organizational change; job insecurity; ineffective communication; and lack of support from management or colleagues.

Even if we could effectively determine the number of employees impacted by burnout, this would measure only its economic impact. The real priority is to understand if there is a problem, who are the colleagues impacted and why.

According to Securex, our partner in health and well-being management, two out of three Belgian employees from all industries suffer (negative) stress at work. Around 10% have or will develop real burnout. This number is increasing as the causes of stress at work multiply.

The good news is that the causes of stress and burnout are increasingly recognized, and by addressing them, we can reduce their impact.

At the same time, identifying stress or burnout early benefits both employee and employer: Employees benefit from support and coaching, and employers are made more aware of their responsibility.

This is why, after several managerial training sessions on this topic, SAP BeLux decided to organize workshops to train its managers to identify and react at an early stage to those critical situations. Simultaneously, the training provided an opportunity for leaders to evaluate their own stress level and act to protect themselves.

How to introduce the problems

  • (Re-)define stress, burnout, depression: How does it occur, and why does recovery take so long?
  • Identify the level of risk: look at the balance between stressors (workload, work intensity, work environment, changes, disruptive behavior) and energy sources (values, culture, team, direct management, autonomous work, purpose) in your own organization.
  • Let participants work on their own situation, and enable leaders to evaluate whether they are presenting signs of burnout.

From experience to an action plan

We shared the concrete actions and decisions we were making to keep our organization as healthy as possible (keep the aquarium water transparent), and we discussed our ability to react as leaders on three levels:

  • Primary prevention: preventing risks
  • Secondary prevention: preventing damages
  • Tertiary prevention: limiting damages

We worked on identifying our own skill gaps and experimented with being able to effectively address the three stages listed above. We did not come up with any big theories, but focused on practical aspects—this is where the sharing experience was fruitful.

During our discussions, we learned about how to focus on the desired situation, using the DESC method:

Description: Focus on facts, name what you see, gain acceptance, do not forget positive aspects

Emotion: Express your evaluation and concerns with regards to the facts

(Desired) Situation: What are the specific expectations for changes

Consequences: Be specific about the impact for the work environment and the person itself

The type of support and engagement required varies, depending on the employee and their specific situation. It ranges from prevention discussions while employees are in the workplace, keeping in contact, and preparing follow-up to support an employee’s return to work.

Tips for leaders

  • Be a role model
  • Create space for dialogue
  • Pay attention to colleagues’ signals (emotions, behavioral and social relationships changes, use of and reliance on substances)
  • Give positive feedback
  • Make workload a subject of discussion
  • Stimulate self-motivation
  • Use people for their talents

In conclusion, treating the training sessions as “real-life” workshops was an effective approach.

This article originally appeared on LinkedIn Pulse.

Bruno Kindt

About Bruno Kindt

Bruno Kindt is Human Resources Director for the Belgium Luxembourg region at SAP. After studying information technology, he started his career developing HR-related software. He joined SAP in 1995 as a human capital management (HCM) consultant, implementing SAP solutions for the first Belgian and Luxemburgish customers. After having been HCM presales and head of the training organization, he joined the HR department as a manager and later appointed HR Director for the Belux region, with additional responsibilities as regional board area HR business partner for analytics.

What If Employees Were Engaged To Work Like Consumers Are Engaged To Buy?

Gina Nodar

One of my guiltiest pleasures is online shopping – not because I love buying things (well, maybe partly), but because the experience is so much more convenient. It combines the ease-of-use of mobile apps with intelligent technology that allows me to virtually try on a new outfit or see how a paint color will look on my walls. I can order via voice automation and enjoy quick, free shipping for a frictionless experience that keeps me coming back for more.

We’ve heard of such experiences used to highlight the impact digital technologies are having on the market (think of how Airbnb disrupted the hospitality industry) – but it’s also disrupting the workplace and employee expectations.

Imagine if employees were engaged to work like consumers are engaged to buy. The rise of technology has influenced a change in business operations and shifted how we interact with brands as consumers – and more importantly, how we expect to be engaged as employees. Just as I expect a seamless experience ordering something via a mobile app, I expect that same experience when using any internal process at work.

Why is employee engagement important now?

Because disengagement costs almost $400B per year in the United States alone (according to a Gallup study). Technology has made employees more productive and simplified work, but it has also created the “always-on” employee. We can communicate with colleagues all around the world at any time of day. While this brings tremendous collaboration capability, it also has the power to disconnect us or even burn us out. This is making employee well-being more of a priority than ever before.

Organizational design and role-mapping are also changing in a digital world. The rise in virtual and contingent employees has redefined how roles, relationships, and resources are aligned now that they’re no longer dictated by geography, but by skills and organizational goals.

These trends are all contributing to the need for an inclusive, integrated culture, enabled by intelligent technology. And fostering this type of digital mindset starts with an organization’s leaders.

What is a “digital mindset,” and what does that mean for the workplace?

In its simplest form, a digital mindset means:

  • Embracing enterprise transformation as a business imperative, not just endorsing it as an IT project
  • Forging the gap between next-gen technologies and employee adoption so technology becomes fundamentally integrated into the employee and customer experience
  • Willingness to try new things with relentless curiosity

The third blog in our series, “Curiosity And Change: A Never-Ending Conundrum,” explored how organizations create real-time digital workplaces while ensuring user adoption and managing change. Now, we’ll discuss leadership’s role in creating the digital workforce – and how digital leadership nurtures a digital mindset among all employees and increases engagement.

I connected with Claude Silver, chief heart officer of VaynerMedia, and she shared how her team is executing this concept with a people-first strategy. From Claude’s perspective, a digital mindset is an empathetic one. It starts with “internal honey,” as she calls it – with each employee feeling empowered to be their own leader in an inclusive organizational culture that embraces technology and the human element. Claude said, “If we get this right, we’ll be able to deliver that experience to our customers and ultimately to the end-consumer.” This approach creates an end-to-end digital experience from employee to end-consumer that is centered on empathy, attention, and culture (and don’t you just love how even her title reflects this mentality?).

What makes a leader a digital leader?

Digital leaders drive better outcomes. Oxford Economics calls them “digital winners,” reporting that they achieve greater revenue, higher profit, and have a more sustainable talent pipeline. They have the power to guide organizations in the shift from just doing a few digital things to operating as a digital enterprise.

As an individual leader, you can use new technology with your team to get work done. Exhibit empathy to connect with your team to drive outcomes. And try new things, even if they are out of your comfort zone!  For example, Claude’s team embraces a “feedback as an act of kindness” philosophy – supplying employees with feedback in real-time using tangible examples so they have the opportunity for development in the moment.

Companies can be digital leaders too. In 2018, we surveyed 100 leaders from leading companies around the world and found that they:

  1. Focus on true transformation: Embracing a cross-functional versus incremental approach enables consistent change, viewing transformation as an opportunity to reinvent business models, processes, and how employees work across teams.
  1. Transform customer-facing functions first: Understanding a reinvigorated customer experience drives success in the digital economy so they proactively align customer-facing initiatives with internal processes across the organization – and extend them to their ecosystem.
  1. Are talent-driven: Recruiting top talent and continuous/targeted development opportunities are a priority.

Employees seek purpose at work, so creating meaningful work tied to the organization’s mission is key. Intelligent technologies support a digital infrastructure, but it’s an organization’s employees who empower the application of digital to see viable business growth. People are a company’s most valuable asset. And as we work to redefine the customer experience, we must also redefine the employee experience – engaging them as they’re engaged as consumers.

Watch for our fifth and final blog about managing talent in the digital workforce.

For more insight on digital leadership, see How To Succeed In Today’s Digital Economy.

Gina Nodar

About Gina Nodar

Gina Nodar is an Integrated Communication and Enablement Specialist at SAP, where she gets to tackle topics like digital transformation, cross-industry trends, digital workforce, social selling, and other strategic priorities to enable SAP’s sales teams and accelerate the ultimate success of their customers. Gina is also part of the Digital Workforce Taskforce, a team of SAP leaders whose mission is to help companies succeed by understanding and addressing workforce implications of digital technology.

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.