How Are Drones Influencing The Business World?

Mark Chesterman

Drones have long been viewed as little more than cool toys for adults (or at their worst, weapons of war). But people are now beginning to understand that drones can also be useful business tools.

How? They collect data, and data collection is big business. Collected efficiently and analyzed properly, the right data helps companies make good decisions faster than ever before.

Drones fill a perfect niche, easily accessing the airspace above the ground and below the clouds—that middle place that’s above surface of the earth but below airplanes and satellites. This perspective enables drones to scan, map, and record surfaces and objects in three dimensions with a level of clarity and detail never before possible.

Dronelife predicts that in 2017, companies will transition from exploring drones’ potential to implementing their use. Companies have already been purchasing drones to learn about their capabilities in the field. Now business owners are approaching developers with ideas about what drones can do for their companies.

Automated work processes

Automation is a primary area drones can be utilized. For example, instead of driving for hours to inspect crops, farmers can send a drone to take a look and record how things are progressing. Ranchers can check on their cows without riding the plains in search of them. Construction companies can keep an eye on construction sites and expensive equipment. Property inspectors and insurance agents can survey properties for damage or code violations.

According to Entrepreneur Magazine, drones can be cost-effective as well. A job that may have previously taken up to six hours can now be done in one, freeing employees to work more efficiently. Such productivity gains will quickly offset the $2000-$3000 investment in the cost of a roof drone.

Employers and insurance companies are always looking for safer ways to perform difficult and dangerous tasks. A roof drone can safely inspect and record information on a damaged roof, for example, enabling the insurance inspector to analyze and report on recorded data from the safety of the office.

Disaster cleanup companies find drones very useful for estimating damage from flooding, rock slides, and other emergencies, guiding cleanup workers and helping them conduct rescues and repairs with minimal risk.

Better delivery and service

According to Business Insider, the drone industry will quickly change the way many companies do business. As an example, the 2016 Consumer Electronics Show was filled with quad-copters promising faster package delivery.

Emergency medical researchers have successfully tested an AED (automated external defibrillator), which can be quickly and efficiently dispatched via drone by 9-1-1 personnel to assist heart attack victims. The drone, which resembles a large spider, carries the AED in its body compartment. When the drone lands, the 9-1-1 caller (or others on the scene) simply follow voice commands issued by the unit to implement potential life-saving measures before the ambulance arrives.

Advertising tools

Drones are also changing the marketing industry. Since 2014, drones have been routinely used to display advertising banners. Since then, some restaurants have experimented with using them to serve food and for stunts such as hovering mistletoe over tables at Christmastime and displaying ads outside office building windows at lunchtime. Florists have dropped roses over city streets on Valentine’s Day. Many businesses agree that drones offer unique, innovative, and effective ways to advertise their products and services.

If a company needs an aerial photograph or a video of a major event, a manufactured prototype, or a new location site, a drone will do the job nicely without scaffolding or helicopters. Drones create stunning promotional photos and videos, while also showing the company as modern and innovative.

Regulatory concerns

Amidst the excitement, many companies are also taking a serious look at the insurance and liability aspects of drone flying. This quickly evolving issue has already resulted in insurance companies adding clauses to their contracts. Regulatory and safety concerns regarding drone use will undoubtedly cause many company owners to move cautiously.

Although few doubt that the commercial drone industry is poised for takeoff, many bugs still need to be worked out with the Federal Aviation Administration. The question of who has the right to use the air space above the earth has been a push-and-pull issue for decades.

To illustrate the importance of drone regulation, consider the recent Oroville Dam crisis, where government drones were used to monitor the erosion of the broken spillway ramp. Facing a seven- to ten-day window to clean the debris at the bottom of the ramp, government workers prohibited private drone owners, including media companies, from flying drones over the area. Any glitch in the cleanup timetable could have delayed the reactivation of the electric power plant downstream and caused the evacuation warning to remain in effect.

The quickly evolving nature of drone technology creates many questions about their commercial use and safety concerns. Recent crashes between drones and power lines and a highly publicized landing on the White House lawn put pressure on the FAA to develop a new set of regulations that will need to be updated frequently.

As entrepreneurs invent new ways for drones to impact the business world and society in general, companies will respond, using drones to reduce costs and increase safety and efficiency. As a bonus, the publicity that comes with innovative drone use could offer them a publicity boost.

For more on the role of drones in digital transformation, see Drones: Poised For Takeoff In The Digital Economy.

Image: Flickr


About Mark Chesterman

Mark Chesterman is a drone enthusiast. He aspires to becoming a force to be reckoned with in this field. His passion for drones started after buying a simple quadcopter model and getting a passion for aerial photography. Mark recently started the Droneista project. The website offers useful advice for anyone who wants to learn how to choose a drone or how to fly with it. Also, Droneista focuses on extensive reviews.

Higher Education Is A Business – Is That So Bad?

James Krouse

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

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

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

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

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

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

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

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


James Krouse

About James Krouse

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

As Machine Learning Remakes Industries, Leaders Must Transform Enterprise IT

Jim McHugh

From cars that autonomously navigate dark and icy roads, to MRI scanners trained to spot brain abnormalities, to warehouses managed by sensors, drones, and robots, machine learning is already transforming industries in profound ways.

These applications are emerging amid a faltering Moore’s Law, which has run up against the laws of semiconductor physics. For four decades, we could count on the doubling of computational power every two years. Now, traditional semiconductors can only deliver about 10% performance gains in this timeframe. That means the performance gains that sustained advancements in the use of information technology through the PC, mobile, and cloud eras can no longer be relied upon to propel the promise of machine learning.

Instead, graphics processing units (GPUs) – chips evolved from those that power image-intensive video games and professional visualization applications – will provide the computational power needed to drive the machine learning revolution. A new computing model, called accelerated computing, takes advantage of the GPU’s faster processing speeds to train the complex algorithms used in machine learning software.

However, most companies’ data centers, where the algorithm training must take place, run on servers with traditional processors. This is hardly surprising, given that machine learning has only recently verged on mainstream business operations. An enterprise that intends to transform itself using machine learning will need to invest in the necessary combination of hardware and software to tap the vast promise of AI.

The power behind the algorithms

Machine learning is poised to change the way business is done across a range of industries. Consider the following examples.

Transportation. Automakers, at the forefront of AI’s transformation of the $10 trillion transportation industry, are racing to show how AI can differentiate their brands. Enhancing safety will be high on the list, as each year there are tens of millions of accidents worldwide and over a million fatalities. Companies worldwide are using a compact, GPU-powered supercomputer in the vehicle that is capable of guiding autonomous cars.

The same holds true for truck manufacturers and logistics businesses. GPU-powered servers in the data center are being used to train, virtually, autonomous trucks and other vehicles how to drive on millions of miles of high-definition mapped roads in a broad range of weather, road, and traffic conditions. Through such simulated driving efforts, the algorithms that run autonomous vehicles will be able to learn continuously from data collected from actual driving situations to make real-time decisions.

Healthcare. Medical imaging alone is estimated to become a $49 billion market worldwide by 2020, making it the biggest source of data in healthcare. Radiology, a prime area for machine learning advances, accounts for a large portion of medical images. According to Academic Radiology, the average radiologist must interpret a CT or MRI examination every three to four seconds to meet workload demands. In an eight-hour workday, that adds up to 8,000 images per radiologist a day.

AI algorithms can be trained to spot abnormalities using real and simulated medical images. This makes devices such as MRI scanners the first line of defense in spotting disease. These and similar devices can speed diagnosis, greatly improve accuracy, and allow doctors to concentrate their energies on the most difficult cases.

Manufacturing and agriculture. Advances in image recognition are creating a range of industrial Internet of Things opportunities. For example, IoT is becoming central to warehouses and fulfillment centers. Machine learning – fueled by image recognition, data, and sensors – steers robots among humans in warehouses.

Manufacturing companies are using connected machines such as drones and robots to inspect industrial equipment, which offers companies potential savings of tens of millions of dollars annually. Industrial farming won’t be left behind. Images taken from drones and satellites will be treated with machine learning to boost crop yields. Farming companies can use images and algorithms to process all the data captured by satellites to monitor the soil conditions and overall crop health. Analytics can track and predict weather changes that could impact crop yields.

An infrastructure for machine learning

All told, the nascent business opportunities enabled by massive data collection and the implementation of algorithms will require rethinking the data center. Without investments in enterprise IT infrastructure, machine learning can’t deliver what it promises.

A critical step toward business transformation is to make sure an organization’s data center can support compute-intensive workloads. GPU-accelerated computing redefines the economics of data center computing, replacing racks of CPU-based servers with far less hardware installation, power, and cost. For example, a company could potentially replace 300 CPU-based servers with one or two GPU-based servers, for a cost savings of more than 85%.

Those managing a company’s data center infrastructure need to ensure they have enough accelerated computing power and storage to handle all the data needed. This involves evaluating the whole picture to understand the incredible savings that can come from modernizing your architecture for the AI world.

Business leaders who perform due diligence to ensure their hardware is a match for their company’s machine learning ambitions will quickly understand the value of GPU computing.

To learn more about the technology requirements for deep learning, check out this webcast on May 24, 2018 and this white paper.


Jim McHugh

About Jim McHugh

Jim McHugh is vice president and general manager at NVIDIA with over 25 years of experience as a marketing and business executive with startup, mid-sized, and high-profile companies. He currently leads NVIDIA Deep Learning Systems – NVIDIA DGX Systems and GPU Cloud. Jim focuses on building a vision of organizational success and executing strategies to deliver computing solutions that benefit from GPUs in the data center. He has a deep knowledge and understanding of business drivers, market/customer dynamics, technology-centered products, and accelerated solutions.

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.


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.