Some Top AI Trends For 2018: Self-Driving Everything, Algorithm Whisperers, And More

Timo Elliott

Here’s my take on the interesting trends in artificial intelligence and machine learning in 2018.

Self-driving everything

This year will see the rapid growth of products, services, and business processes that use the power of machine learning algorithms to automatically get better as more people use them – just as self-driving systems get better at navigating roads over time by recognizing patterns and learning from any mistakes.

This can and will impact all areas of life, such as:

  • Self-optimizing cities. Imagine traffic lights that constantly and automatically adapt in real time to improve traffic flow.
  • Self-driving homes, with home automation systems that adapt to your rhythms, such as automatically turning the heat on half an hour before you get home.
  • “Lights-out” finance organizations with processes that learn and improve from every exception that requires human interaction, getting ever-closer to closing the books automatically.
  • Marketing that proactively adapts to prospects, automatically maximizing exposure to content that interests them while minimizing anything perceived as spam.
  • Human resources departments that automatically get better at shortlisting candidates based on successful hires.
  • Business intelligence tools that automatically propose answers rather than always waiting for you to ask a question.

And many, many more.

Ethics everywhere

Artificial intelligence is designed to maximize certain behaviors (“get to the destination without crashing the car”), based on the data provided (camera, lidar, traffic rules, etc). But bad data or badly chosen KPIs can lead to unethical and biased results.

For example, if your new, automated HR processes are taught using prior hiring data that was full of human bias, the resulting algorithm will also be biased. And we often observe sub-optimal behavior from human beings because of badly designed incentive plans – the principle difference with AI is that it will do the bad things much faster and more effectively!

We can’t just outsource our responsibilities to machines. Someone needs to be clearly responsible for any decisions made by algorithms, with the power and resources to make changes when problems arise. To do this effectively, there must be transparency, with the ability to monitor and track the resulting effects of any automated tasks.

And 2018 is likely to see more high-profile cases of “algorithm abuse,” leading to organizations investing in specialized roles around AI adoption.

Algorithm whisperers

Because AI is highly dependent on the data it is fed and the patterns we train it to look for, we need people who have the skills to do this right. Call these people “algorithm whisperers” who can make sure that these technologies do only what they’re supposed to do.

An algorithm whisperer’s job is to have a deep understanding of the context of algorithm use. It’s about understanding the data and the algorithms that are being used and interpreting the results. At the end of the day, bad data means bad results – it’s critical to have someone with the skills to tell what data has been collected, when it doesn’t make sense and why, and who understands the impact this will have on results.

Anscombe’s Quartet famously illustrates some of the problems – these data sets all have the same statistical characteristics (mean, variance, correlation, etc.) but would result from very different types of processes.

This kind of data analysis is what data scientists specialize in, but what really distinguishes an algorithm whisperer is creativity. For example, data scientists working on the 9/11 memorial in New York initially determined that it was impossible to achieve the level of adjacency that had been requested to memorialize all of the people impacted. Yet data artist Jer Thorpe managed it by using all resources available, such the physical characteristics of the place, the length of the names, and the choice of font.

Algorithm whisperers can also use their deep expertise to figure out what the results of predictive studies mean. For example, a subway authority wanted to use an algorithm for predictive maintenance to figure out in advance when a machine might break down. But looking closely at the data, it turned out that every time a single machine broke down, the machine next to it would also break down within a day. It was almost as if they were “catching” the breakage from each other, like a common cold. It didn’t seem to be logical. Was it a data quality problem with the same machine counted twice? Maybe machines were installed together and tended to break down together after a certain amount of time? The answer was that this was a result of repair teams with strict service level agreements. If they missed the window for fixing a machine they were paid less. So, if there was a broken part, they would order a new one, but replace it immediately using a part from the machine next to it. They would go back the next day to fix the “new” breakage. It took an algorithm whisperer with a full understanding of the context of the data to successfully and correctly interpret what was actually going on.

It’s unlikely that “algorithm whispering” will be a mainstream job title any time soon, but in 2018, data scientists will get even better at the creative aspects of their role, while business people will adapt the way they work to the new opportunities.

Data sovereignty

AI is only as good as the data you have available. But the question of who owns and controls data is far from being a neutral question, and the issue of data sovereignty will reach new visibility in 2018, with at least four different levels of discussion:

  • Data is the foundation for the business models of the future, and the biggest opportunities are often where different organizations collaborate and share information across a “digital supply chain” and compete as an ecosystem. It’s clear that this can create added value for society as a whole, but it’s less clear how to deal with corresponding issues of joint ownership of data, the possibility of intellectual property leaks, and more. We’re seeing these issues play out in the Internet of Things space, as various organizations experiment with new business models for gathering and sharing data across companies that traditionally compete with each other.
  • Within companies, the traditional approach of a single “data warehouse” that tries to bring together all relevant business data needed for decision making has been discredited. Instead, there’s a rise in systems that “orchestrate” data flows across different sources inside and outside the organization. This is essentially a “federation” approach to data ownership with a compromise between individual silos and the needs of the organization as a whole.
  • Consumers are increasingly aware of just how much privacy they have been giving up. There may be a backlash about how “their” personal information is being collected, controlled, and monetized. There have been advances in systems that allow consumers more control over how their information is used. And various models have been proposed for “personal data sovereignty” that flip the equation so that individuals have control over their data and can choose to provide it to vendors in return for compensation – but it is hard to see how large-scale adoption of such models would be feasible.

We’re likely to see a lot of talk this year in all these areas – but, unfortunately, little or no resolution of the complex underlying issues.

Technology is about being human

What’s the real killer technology to get the most out of AI? It’s people!

The biggest effect of increased artificial intelligence and automation in 2018 will be the rising importance of human skills. To weather the coming storm, it’s important to know what AI is capable of, but the key isn’t to compete with it – it’s to double-down on the human skills that got you where you are today.

For example, when more of the medical diagnoses are being handled by algorithms, the doctors that stand out will be those with the best patient skills. And finance teams will reward the people that make sure that the company as a whole actually uses that data to further the needs of the business, rather than those who are good at collecting and processing data.

For decades, the power of technology has been advancing quicker than our ability to adapt to its use. This is our opportunity to optimize our non-technical skills, including change management, leadership, and corporate culture.

And finally, we need human judgment more than ever. AI is a very powerful tool, but just because something is now feasible doesn’t mean it’s a good idea. We all need to support organizations such as the Partnership on AI and its mission to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.

Learn how to build an intelligent enterprise with artificial intelligence (AI) and machine learning software to unite human expertise and computer insights.

This article originally appeared on Digital Business & Digital Analytics.

Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence.

Artificial Intelligence: From Novelty To Practical Workplace Application

Sven Denecken

Over the past few years, businesses have pondered what an intelligent workplace powered by artificial intelligence (AI) might look like. Well, the day is finally here, and it’s not as daunting or as intimidating as many might have anticipated.

While some organizations were apprehensive that the arrival of AI would result in worker displacement or business process confusion, most now agree that AI will ultimately benefit their businesses. In fact, Gartner predicts that AI augmentation will recover 6.2 billion hours of worker productivity by 2021.

Still, many companies are left wondering how these technologies will work to actually boost productivity and positively impact the bottom line. The good news is that there are a number of ways to implement AI based on your business objectives and goals.

How to foster an intelligent workplace

Businesses can start implementing useful AI technologies such as digital assistants into everyday operations to not only increase employee productivity, but also streamline business processes. The beauty of AI is that it can be rolled out to various departments on a case-by-case basis. If the sales team is struggling to streamline certain functions, then it might make sense to make the AI investment with sales first and go from there.

  • Accounting: In accounting, employees can use machine learning and predictive analytics technologies to match incoming payments to the applicable invoices, reducing time spent manually matching payments, and reducing the risk of human error.
  • Sales: Sales teams can use machine learning to identify probable orders and predict sales volume for more precise forecasting. They can also use predictive analytics to get a glimpse into the probability that a deal will close — helping them adjust revenue goals as needed.
  • Human resources: AI technology can streamline routine tasks in the hiring process, such as answering basic questions and checking off a candidate’s qualifications. This allows HR professionals to spend more time getting to know candidates on a human level by gauging body language, tone of voice, etc.
  • Sourcing and procurement: Machine learning tools can review purchase order confirmations and proactively alert users to potential shipping and logistics problems and automatically contact customers when deliveries are at risk of not arriving on time.

Incorporating AI on a personal level

Given that the B2B sector is largely influenced by trends taking place in the B2C world, it’s no surprise that Amazon announced Alexa for Business last quarter in response to business needs. You can use a “personal assistant” that combines machine learning, natural language processing, and predictive analytics to do more than simply turn on the lights or dial into conference calls. However, in order to break into the next level of productivity, enterprises need to understand how to train such technologies to adapt to specific business settings, and to employees’ daily tasks.

One way employees can train their digital assistants is by integrating them with their work calendars. A digital assistant that is fully integrated with your work schedule could, for example, inform you that, after your last in-person meeting at 3:00 p.m., you should have time, based on current traffic conditions, to drive home and take your last call of the day from your house. That information helps you avoid rush hour traffic and still finish everything you needed to do for the day. A true win-win situation.

The importance of education and training

As previously mentioned, having AI technology at the fingertips of your enterprise is great, but only if your employees know how to work with it. A Pew Research survey found that 87 percent of workers believe that it will be essential for them to receive ongoing training and develop new skills throughout their work lives in order to keep up with the changes in the workplace, especially those changes brought about by AI.

An investment in AI systems such as digital assistants and predictive analytics tools also requires an investment in employee education and training so you can be sure that the technology is used correctly. Whether the C-level executive team or the IT department takes the lead in education efforts, any training you offer will pay off down the road as AI becomes more integrated in the enterprise.

Organizations will have to embrace digital transformation if they want to truly become intelligent businesses. It’s an exciting time to get started with the phenomenon that will only continue to gain ground in the enterprise environment. Start by envisioning how AI can be successfully implemented within your company, then begin planning how to make it happen.

Gone are the days of implementing AI just for the sake of staying relevant. It’s critical that businesses deploy AI applications that serve practical, functional purposes in the workplace to garner the best results.

This article originally appeared on CMS Wire.

For more on this topic, see How Artificial Intelligence Can Increase Your Business Productivity.

Sven Denecken

About Sven Denecken

Sven Denecken is Senior Vice President, Product Management and Co-Innovation of SAP S/4HANA, at SAP. His experience working with customers and partners for decades and networking with the SAP field organization and industry analysts allows him to bring client issues and challenges directly into the solution development process, ensuring that next-generation software solutions address customer requirements to focus on business outcome and help customers gain competitive advantage. Connect with Sven on Twitter @SDenecken or e-mail at

How Can Machine Learning Help Eradicate Modern Slavery In Supply Chains?

Shelly Dutton

Hidden in the dark corners of everyday supply chains is a US$51 billion illegal market, comprised of 40.3 million enslaved people – 75% of whom are women. From agriculture and manufacturing to domestic servitude, women are trapped in a supply chain that contains an inescapable cycle of forced labor with little to no pay, daily violence, threats, substandard conditions, and long hours.

Investigative journalists have for years exposed modern slavery in many leading brands’ supply chains. And often the response from reported companies is met with shock, denial, and silence.

But if we look closely at their released statements or lack of acknowledgment, a persisting underlying problem emerges: a lack of transparency across the entire supply chain, including the suppliers of their suppliers.

Unraveling the pervasiveness of forced labor practices in supply chains

The more connected and globalized our world becomes, the riskier supply chains become. Competitive realities are forcing businesses to make decisions that balance cost and quality as well as access and sustainability. But in an environment where cost and access are typically valued more, companies act unknowingly as an accessory to modern-day slavery.

According to Justin Dillion, CEO and founder of Made in a Free World, the best way to eradicate this problem is to help consumers and businesses buy better. During the SAP-sponsored Webcast Monitoring Ethics Deep in the Supply Chain, he said that businesses need to be empowered to “look at these issues more specifically – and do that through the lens of spend data.”

Consider, for example, a global firm with a supply chain consisting of 60,000 vendors and resellers. Such a high volume of suppliers in its network would likely include at least a couple hundred vendors that could expose operations to instances of women in forced labor. And this is not a fictional hypothetical. In fact, Thomson Reuters’ 2016 Global Third-Party Risk Survey revealed that only 36% of surveyed businesses thoroughly monitor their suppliers for risks, while 61% have no knowledge of the outsourcing activities of their third parties.

Overcoming growing complexity to end supply chain exploitation

Businesses can overcome exposure with a platform that gives a clear view of the entire supply chain. Access to real-time intelligence data from online news, media, and government organizations enable buyers, procurement managers, and supply chain leaders to make better-informed sourcing strategies. When drilling down into the data, everyone who touches the supply chain can identify high-risk exposure based on a variety of factors.

With a sense of the risks behind their purchases, they can remediate known sources of modern slavery to improve brand integrity and document it to comply with reporting regulations – but this is just the beginning. Machine learning can further extend insights by uncovering unknown, otherwise invisible, events through the detection of patterns.

Self-learning procurement processes empower businesses to connect the dots quickly between primary, secondary, and tertiary supplier relationships. This application of machine learning not only roots out hidden enslavement practices deep in the supply chain, but it accomplishes it in a way that minimizes supply chain disruption and keeps costs low.

Machine learning turns a noble purpose into a business opportunity

Machine learning gives procurement and supply chain organizations a strategic weapon for freeing millions of women from modern slavery. However, this digital approach is more than just a noble purpose. It’s a strategic investment for identifying effective supply chain practices that meet the demands of customers, investors, and employees that want to see slavery eradicated for good.

It doesn’t matter how far in the supply chain modern slavery resides. Every business along the value chain – from the originating supplier to the final seller – will feel its impact. By “walking the walk” on ethical supply chain operations, businesses are not just doing good – they’re lifting their bottom lines with new sources of growth and innovation.

For more on this topic, see Combating Modern Slavery: It’s More Than Compliance, It’s Ethics!

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.


HR In The Age Of Digital Transformation

Neha Makkar Patnaik

HR has come a long way from the days of being called Personnel Management. It’s now known as People & Culture, Employee Experience, or simply People, and the changes in the last few years have been especially far-reaching, to say the least; seismic even.

While focused until recently on topics like efficiency and direct access to HR data and services for individual employees, a new and expanded HR transformation is underway, led by employee experience, cloud capabilities including mobile and continuous upgrades, a renewed focus on talent, as well as the availability of new digital technologies like machine learning and artificial intelligence. These capabilities are enabling HR re-imagine new ways of delivering HR services and strategies throughout the organization. For example:

  • Use advanced prediction and optimization technologies to shift focus from time-consuming candidate-screening processes to innovative HR strategies and business models that support growth
  • Help employees with tailored career paths, push personalized learning recommendations, suggest mentors and mentees based on skills and competencies
  • Predict flight risk of employees and prescribe mitigation strategies for at-risk talent
  • Leverage intelligent management of high-volume, rules-based events with predictions and recommendations

Whereas the traditional view of HR transformation was all about doing existing things better, the next generation of HR transformation is focused on doing completely new things.

These new digital aspects of HR transformation do not replace the existing focus on automation and efficiency. They work hand in hand and, in many cases, digital technologies can further augment automation. Digital approaches are becoming increasingly important, and a digital HR strategy must be a key component of HR’s overall strategy and, therefore, the business strategy.

For years, HR had been working behind a wall, finally got a seat at the table, and now it’s imperative for CHROs to be a strategic partner in the organization’s digital journey. This is what McKinsey calls “Leading with the G-3” in An Agenda for the Talent-First CEO, in which the CEO, CFO, and CHRO (i.e., the “G-3”) ensure HR and finance work in tandem, with the CEO being the linchpin and the person who ensures the talent agenda is threaded into business decisions and not a passive response or afterthought.

However, technology and executive alignment aren’t enough to drive a company’s digital transformation. At the heart of every organization are its people – its most expensive and valuable asset. Keeping them engaged and motivated fosters an innovation culture that is essential for success. This Gallup study reveals that a whopping 85% of employees worldwide are performing below their potential due to engagement issues.

HR experiences that are based on consumer-grade digital experiences along with a focus on the employee’s personal and professional well-being will help engage every worker, inspiring them to do their best and helping them turn every organization’s purpose into performance. Because, we believe, purpose drives people and people drive business results.

Embark on your HR transformation journey

Has your HR organization created a roadmap to support the transformation agenda? Start a discussion with your team about the current and desired state of HR processes using the framework with this white paper.

Also, read SAP’s HR transformation story within the broader context of SAP’s own transformation.

Neha Makkar Patnaik

About Neha Makkar Patnaik

Neha Makkar Patnaik is a principal consultant at SAP Labs India. As part of the Digital Transformation Office, Neha is responsible for articulating the value proposition for digitizing the office of the CHRO in alignment with the overall strategic priorities of the organization. She also focuses on thought leadership and value-based selling programs for retail and consumer products industries.