2018 Outlook For Machine Learning – An Innovation In Its Teen Years

Katrin Schneider

A year ago, Gartner, the world’s leading research and advisory company, named artificial intelligence (AI), machine learning (ML), and conversational systems three of the top strategic tech trends for 2017. In May last year, SAP launched the SAP Leonardo Machine Learning portfolio at SAPPHIRE NOW in Orlando, Florida, and thus demonstrated that it’s on the pulse of innovation. Today, it is about time to sum up the latest developments and give an outlook on the potential of intelligent technologies.

Trend #1: ML platforms

Deep learning, neural networks, and natural language processing elevated ML to new levels. Thanks to mature ML algorithms, higher processing power, and the availability of huge data sets, machines are becoming intelligent and able to process unstructured data, like pictures, text, or spoken language – often even on a superhuman level. Additionally, deep learning is now stable enough to potentially establish ML as a standard commodity across businesses worldwide. Those who are interested in tailor-made and customized solutions require a ML platform to combine ready-to-use services to create their own intelligent applications.

Trend #2: Intelligent applications

Intelligent apps automate routine tasks that take time from value-adding activities and can provide precious insights into structured and unstructured enterprise data. This helps companies make better business decisions and increase productivity in several lines of business, like finance, HR, sales and service, and more. For enterprises lacking in-house ML expertise, intelligent applications are available that allow, for example, building self-driven customer service to enhance customer experience, automating financial services by matching incoming bank statements to open receivables, or helping marketing executives maximize sponsorship ROI.

Trend #3: Conversational systems

Thanks to great strides in natural language processing, conversational AI has fundamentally changed how we interact with computers and electronic devices. Today, millions of people use intelligent interfaces to satisfy their daily consumer demands. They serve users with music selections, vacation planning, pizza ordering, and much more. We are on the cusp of a world where conversational assistants will be retrievable at any time and place – including at work. These capabilities will connect data, processes, applications, devices, and people and will build the foundation for a new digital experience in the workplace.

What will the future bring us?

Technologies augmenting the human potential at work are no longer dreams of the future. But how will they evolve, and what will the trends in 2018 look like? I believe that some of these technologies might be about to reach maturity, but there is still a great deal of innovation potential, especially in the enterprise context. According to Gartner, 59% of organizations are still gathering information to start building an AI strategy for their organization – a huge competitive advantage in 2018 for those who have already started to adopt AI in their systems. I am convinced that more and more companies will leave the concept stages this year and really begin to apply ML. The hype around deep learning will flatten out as it becomes a commodity, but the efficiency and robustness of the underlying models will be the differentiator and therefore a relevant issue for enterprises to address in the upcoming year.

For Gartner, a rock-solid ML foundation, intelligent apps, and conversational platforms will make the difference between profit and loss for companies in the race to digital transformation in 2018. Platforms and solutions will evolve significantly, handling increasingly complex tasks. Further, Gartner predicts that over the next few years, every application will contain AI, creating an intelligent layer between employees and enterprise systems.

Overall, technology will become even more human-centric and will increase transparency between people, organizations, and things. In business and private life, augmented reality, virtual reality, or brain-computer interfaces will create immersive experiences beyond virtual assistants and chatbots.

Moreover, business will experience a shift from standalone intelligent objects to swarm AI – an approach that goes back to the behavior of animals that amplify their group intelligence to solve problems or make decisions. Swarm AI is the self-organization of systems for collective decentralized behavior that enables human swarms by bringing information of diverse groups into a single emergent intelligence. In an enterprise context, swarm AI helps to improve logistics or transportation, HR, or customer feedback gathering – for example, by eradicating the influence of preceding valuations on the voter. All of these AI developments will have an impact on the way enterprises are doing business, and not only on an operational level. I expect the emergence of new, AI-driven business models and a transition of innovation and research expertise, everywhere from universities to industries.

Explore machine learning applications and AI software with SAP Leonardo technology.


Katrin Schneider

About Katrin Schneider

Katrin Schneider is communications lead for SAP Leonardo Machine Learning at SAP. She is a graduate of the University of Mannheim with bachelor's and master's degrees in Business Administration and Culture.

How Not To Lose Your Job To Robots, Part 2

Paul Dandurand

Part 2 in a 2-part series. Read Part 1

In our last post, we talked about three steps you can take to keep your project management job as your firm starts to use AI for projects. In this post, we’ll wrap it up with a couple more steps.

Think outside the ROBOT box

Have you ever been in a situation where you felt you needed a creative solution and spent hours thinking without any result? That’s because creativity largely stems from our unconscious mind. You’re more likely to get to these Aha! moments when you’re not actively thinking about the situation. It’s a beautiful concept that even animals can benefit from, but alas, our friends the robots are not so lucky.

AI software can indeed create something new. Some can compose new songs and create art. But this takes a lot of computing power and still requires human input to be truly cohesive and pleasurable for humans.

What is more common today is the use of conversation bots with unstructured natural language. However, someone still needs to set up the bots to consider the many variations of user questions that are typed in the system.

You know how your phone can suggest words or responses to automatically complete the sentence you’re writing? It does this by scrutinizing all the text messages and emails you write on a daily basis to analyze and identify patterns. If you type “What” on your phone right now, you’ll see a suggestion of the word you use most often. You’re basically training the AI every time you type.

Researchers used this same concept, but instead of having the computer analyze texts and emails, they fed it several dozen movie scripts and then asked it to write a whole script of its own. They then hired a crew to shoot the movie. The final result was basically gibberish. The words are in English, and the grammar and the syntax are correct, but much of it is meaningless and incoherent.

You can see that computers have a long way to go before they become innovators, and that’s where humans can continue to provide value. Project managers are forced to show creative problem solving consistently. The key here is to build a project culture that fosters idea thinking for both small and big innovation designed to make future projects much better. If you learn how to do this, you’re sure to keep your job.

Always be three steps ahead

Before I make my point, I need to clear something up: prediction and planning are two completely different things. Predictions are made based on past or present data (or trends) and present the likeliest outcome based on these events if nothing changes. Planning, however, is an account of what should be done in the future. You can plan an event based on past events, or choose to go in a completely different direction due to a multitude of external factors.

This is another area where project managers can benefit from AI to make better decisions and therefore provide more value. Computers fall short in the absence of information or when they must imitate human interaction. Planning ahead usually involves uncertainty, interpersonal skills, creativity as well as predictive analysis in order to ensure the best decision is made. Once again, the most value is provided by humans combining their ideas, empathy, judgement, and creativity with the AI robots.

As you can see, the arrival of AI in your workplace could be a negative, unless you are able to understand where AI complements your intelligence instead of replacing it.

If you show leadership skills in your project management role, and your bosses see this, you’ll most likely keep your job!

This article originally appeared on the Pie blog and is republished by permission.


Paul Dandurand

About Paul Dandurand

Paul Dandurand is the founder and CEO of PieMatrix, a visual project management application company. Paul has a background in starting and growing companies. Prior to PieMatrix, he was co-founder of FocusFrame, where he wore multiple hats, including those of co-president and director. He helped position FocusFrame as the market leader with process methodology differentiation. FocusFrame was sold to Hexaware in 2006. Previously, he was a management consulting manager at Ernst & Young (now Capgemini) in San Francisco and Siebel Systems in Amsterdam. Paul enjoys photography, skiing, and watching independent films. He earned a B.A. degree in Economics from the University of California at Berkeley.

Edge Computing And Cloud For Remote Operations, Part 1

David Cruickshank

Part 1 in a 2-part series

In this post, I continue to touch upon the topic of machine learning, but now more within the context of edge computing. Examined simply through the lens of a single lab, there is a plethora of project work occurring across multiple industries generating critical data through asset-intensive remote operations. Here, the goals and objectives of digital transformation include how to optimize operational integrity pairing the edge and the cloud.

The edge and the cloud for remote operations

With the continuous surge of Industrial IoT (IIoT) data – both raw and processed – driving formation and implementation of all digital business processes, the need today is for compute to be as close to where the data originates as possible. This is achieved through edge computing and local processing of the data that matters most. It offers the chance for process industries to improve end-to-end operational integrity for remote operations requirements in real time. The goal is to remedy asset issues, keep workers safe, and persistently and correctly abide by industry, environmental, and other government regulations.

By monitoring the assets at the edge, customers reduce operating costs and downtime and can dispatch repairs or replace equipment components before they fail. When you consider an upstream oil and gas operation like offshore drilling, real-time data and what it can tell you is critical to operational integrity. This is where things like packet delays can be disruptive to the business or demonstrably harmful to both assets and workers. Remote operations in oil and gas represent a fast-paced, decision-driven environment ready to benefit from better data and the advanced analytics capabilities that can make sense of it.

The ability to take local action with better data

Remote operations, whether found offshore or in some other isolated wilderness, must be capable and prepared to take local action as necessary even when cut off from the mainland. What these environments require in processing critical data originating at the edge does not mean compromising the benefits of cloud computing. Some argue that provided you can readily act on the data most critical to a remote operation in real time, this is the maximum value of the data collected and that once acted upon, it then can be discarded.

With immediate value obtained from the data first processed at the edge, it then allows IT/OT network managers more backhaul options to move edge data to the cloud. It is ultimately best for the data originating at the edge to move to the cloud, where it can be widely accessed and take advantage of other integration services to serve many applications. There is, for example, a significant role for ERP in IIoT, provided companies and their edge and cloud providers can demonstrate the ability to orchestrate operational and business processes seamlessly across multiple applications, platforms, and networks.

Cloud capabilities factor in where you perform Big Data analytics on the corpus of data generated representing your critical equipment located in important geographic regions, disconnected from centralized business systems. It is the cloud where you most effectively train the machine learning algorithms you expect to deploy at the edge. There is a need for edge computing at each remote operation, but the cloud is where you bring the relevant edge data from something like multiple rigs (multiple edges) deployed in the Gulf of Mexico.

Edge computing is essential for optimizing industrial data at every aspect of an operation pertinent to operational integrity. With effective edge computing, remote sites act upon the data that matters to a location’s real-time situation and how its business processes are optimized to act on insights gleaned from collected data.

The additive value of cloud

Does a firm need to collect and store all edge data? This may remain debatable over the foreseeable future relative to dimensions like data value, edge-data storage costs, or moving data. Yet this is where cloud capabilities factor in. This is where centralized computing power integrates Big Data originating from all remote locations and their networks to provide insights into operations. It is the cloud where you most effectively train the machine learning algorithms you deploy at the edge. There is immense value in your ability to learn from data originating from all remote locations. Machines and systems in any remote location can learn and become optimized from what is learned from other edge data.

If you wish to consume some solid knowledge about edge computing and cloud, there are many links to click and sources to draw from. My intent is to describe some current and ongoing project work that illustrates the most important dimensions of edge computing and cloud working together to meet the operational integrity needs of remote sites in process industries.

In Part 2, I will introduce you to an SAP Co-Innovation Lab project focusing on connected assets for asset health monitoring and maintenance. The focal point of this multi-phase co-innovation project seeks to enable persistent and accurate operational visibility at the edge for both headquarters and on-site operations. It aims to demonstrate real-time situational awareness and “insight to action” for workers at the point of work execution in remote regions.

For more insight on emerging technology, see Smarter Edge Industrial Manufacturers Need To Serve The Segment Of One.


David Cruickshank

About David Cruickshank

David Cruickshank is senior director for strategy and operations for the SAP Co-Innovation Lab. He leads the lab's efforts in Silicon Valley to enable ecosystem-driven co-innovation between SAP, its partners, and customers. Additionally, he manages all operational aspects necessary to run a multimillion-dollar data center to provision private cloud infrastructures to deliver productive SAP landscapes consumed by co-innovation projects seeking a faster track to market for commercially successful innovations.

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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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.