Will Government Regulations On Driverless Cars Benefit The Technology?

Megan Ray Nichols

Self-driving cars are here, but unless you’ve been living beneath a rock for the last 24 months, you knew that. The thing is, they’re not on the road. Yes, a few corporations have run tests and promotions designed to demonstrate the potential of this watershed technology, but even these have been in largely controlled environments.

The path forward now is not creating the technology, but refining and understanding how “autopilot” should be used. We do that by penning laws, and the creation of the first laws to regulate automated driving will follow in the wake of Internet regulation laws as one of the most noticeable legal actions in our lifetimes.

Why regulation is a must for automated driving

The need for regulation comes out of arguably the largest caveat in creating an entirely automated road system. That is, the safety benefits from automated driving are only realized when every car on the road is automated and cars are able to communicate their planned routes in real time. Even a single human driver on the road with interconnected cars could cancel out safety wins entirely.

Be this as it may, the thought of turning an entirely automated road system on in a single step is intimidating and challenging because of the sheer number of scenarios one must account for. This means that we will likely see a gradual transition to automated cars, and the government has divided the stages in this transition into five parts.

The fascinating third stage of transition is defined as a car that switches off between human and computer control. This has proven more of a challenge for automakers than either of the later “more advanced” stages, with programmers struggling to appropriately instruct the car on when to make the change.

Many cars on the road today are already at stage one or two, with powerful cruise control systems with features like collision avoidance. Stage four requires the ability for fully automated driving. In stage five, all capacity for human input is removed from the car.

New laws will serve as a mandate

That we would eventually come to this seems like a matter of course, but it should be noted that endorsement from the government has effectively made automated cars a part of our society. Lawmakers could have chosen to forsake the technology if the safety and efficiency benefits didn’t prove compelling enough.

Even governmental officials admit there will be scenarios we’re not prepared for. For this reason, much of the law being written now is vague. The lack of definition will allow innovators to continue to introduce new ideas, with the government stepping in where more clearly defined boundaries are needed.

Pressing forward

Despite negative PR following a fatal collision in which a man was killed driving his Tesla Model S, Elon Musk’s company is committed to continuing to develop the autopilot feature. The National Highway Transport Safety Administration (NHTSA) has not yet come to a ruling on whether Tesla’s autopilot feature was to blame in the tragic wreck.

It’s not just pioneers like Tesla making strides these days, though. Major American brands Ford and GM have climbed aboard, with Ford committing to deliver an autonomous ridesharing service by 2021 and GM using automated prototypes as transport vehicles on company grounds.

What exactly will the new laws define? It’s likely that part of the regulation introduced will inform the way that cars communicate between one another. For a fully autonomous roadway of the sort that we believe could save over 30,000 lives a year, each car on the road must be in communication with all of its counterparts. Laws could help define a standard for this communication.

Additionally, the amount of data coming from the cars is sure to be a point of interest for the private sector. Whether car-to-car info will become the target of marketing efforts and Big Data mining campaigns still remains to be seen, but it certainly doesn’t sound far-flung.

To think that not so long ago it was questionable that a car might even have cruise control is shocking, but this is the world we live in. Hold on to those classics folks! You know, the ones you can drive by yourself.

Learn more about how Ford and other companies are using tech innovations today; check out the research infographic How Ford, Airbus, and GE Use 3D Printing for Competitive Advantage.

About Megan Ray Nichols

Megan Ray Nichols is a freelance science writer and the editor of "Schooled By Science." She enjoys researching the latest advances in technology and writes regularly for Datafloq, Colocation American, and Vision Times. You can follow Megan on Twitter.

The Realities Of Innovation In Manufacturing

Kevin Jinks

Many leaders in manufacturing say, “There are things on our shop floor we wouldn’t want anyone to see.” When they say this, they mean the sheer amount of manual effort needed to keep things running. They mean paperwork, Post-it notes, and a lack of automation at the operational, tactical level. Throughout the industry, my customers are talking about operational efficiencies. I’ve learned that no matter the size or location of your business, there are plenty of people who share your problems.

Operational efficiency: goodbye Post-it notes and paperwork

If you’re like most of my customers, you’re taking a hard look at how well your shop or plant functions. Demand forecasting, for example, is a tough call when data about markets is not tightly aligned to production.

A single-system approach can be part of the answer to these problems. New technologies give you solutions that are end-to-end, capable of automating your shop floor and then delivering cognitive technology to help you figure out failures in advance. This gives you fresh insight into demand.

Realistically, the best way to help improve your businesses is with pre-built solutions; these become the core to your success because the commodity work that you normally do on your nickel has already been done. And when you can remove risk, it’s a good thing.

To predict demand, you don’t need 40 systems; you need one

Even the best-run shops struggle with how to predict demand. Let’s say your forecasting is way off – you completely underestimated demand and market. This missed forecast becomes a missed profit opportunity. And your competitors will be quick to step in.

That’s why a digital approach works so well for manufacturers. Technology is a powerful tool here, because it can deliver input into the demand side. If you can do this well, you won’t undermake or overmake. You’ll optimize profits.

The latest “digital core” solutions for manufacturing are robust. They’re provable. And when you have the building blocks into which you can add robotics, prepare to be surprised. The best solutions use AI to mine data that you’re not thinking of, including strong social media mining. This is the kind of information that lets you refine your products – or launch new ones.

You don’t need 40 ERP systems to improve your business; you need one. Imagine a plant that uses the IoT and real-time sensors to read equipment and perform predictive maintenance; you’ll keep the lines running. Plus you’ll keep track of how many products you’ve created and can sell. You’ll be able to tell which products consumed lots of raw materials, which means that plant managers are armed with the information they need to proactively understand what’s going on. Again, it’s not about the number of systems you have in place, but the effectiveness and reliability of just one.

The realities of change

You can’t gain a lot of insight if you have 40 different systems. So when you’re investigating a single-system approach, make sure that you’re adopting best practices, rather than designing best practices. Your projects should involve adopting processes, not creating them.

Today, digital transformation is not a design/build project. After all, there are already solutions available that determine how you take an order, process that order, store your inventory, pick it and ship it. So don’t spend your time on an accounts-receivable aging report. Instead, seek out technology that lets you take advantage of AI, cloud, security and deep, predictive analytics. You’ll surround great code with advanced tech to get you light years ahead of the competition.

Learn more

To take advantage of all the benefits described in this post, request your HANA Impact assessment today. IBM will be at SAPPHIRE NOW and ASUG Annual SAP Conference this June 5-7 in Orlando. Visit IBM at booth #612 and talk to IBM-SAP experts – check out our event website to see what we’re doing at the event.

Kevin Jinks

About Kevin Jinks

Kevin Jinks is Vice President & Partner / Industrial Sector SAP Leader for IBM Global Business Services. With more than 22 years of IT consulting and client management experience, he has extensive knowledge in ERP systems, architectural design, system development and implementation management for major clients globally.

Natural Resources Companies Ignore Data At Their Peril

Anton Kroger

The natural resources sector was born very early in mankind’s existence. Driven by a desire to find hidden treasure, the allure of gold, silver, diamonds – and later coal, oil, and steel – was the stuff that dreams were made of. Indeed, through hard-work and a strong “can-do” attitude, many men (and women) made their fortunes, which attracted others, and soon the industry thrived.

Technology, however, is about to change our course in history, and the impact of the digital revolution cannot be ignored by mining companies. Dr. Michael Rosemann from Queensland University of Technology (QUT) offers interesting insights about revenue resilience and opportunity thinking. Essentially, industries that are seeing their revenue streams disrupted are those heavily focused on adopting digital technologies to survive; i.e., they see digital as an opportunity. Natural resources companies, by contrast, appear to have the highest revenue resilience, meaning they feel that their revenue streams are unlikely to be disrupted. They view technology primarily as a means to reduce costs or risk.

What resources companies fail to understand are the second, third, and fourth order effects of disruptive technology in industries downstream or adjacent to their core business.

I see five ways disruptive technology is changing our everyday lives that will ultimately impact resources companies:

  1. They change how we socialize and what we want to be associated with (e.g., social media)
  1. They change how we learn and explore (hackathons are changing how we connect experts to problems)
  1. They change what we buy and how we buy (trading networks, individual to customer, business to consumer, and business to business)
  1. They change how we do things (e.g., the impact of the smartphone)
  1. They change who and what we trust (studies have shown that people develop an opinion of a company or person by doing research on the Internet before they have met them)

How we socialize and what companies stand for will disrupt the way we recruit talent

In the future, a company’s brand will not only impact how it attracts customers, but also how it attracts employees. Employees will shift their preference to shorter-term contracts that afford them the flexibility to create their own work-life balance, with brands that they perceive as being good to be associated with.

Uber may the most well-known example of a short-term, flexible workforce. It’s almost completely recruited as temporary labor, with most drivers citing flexibility as the reason they chose to work for them. In Europe, the manufacturing industry is beginning to see a shift towards shorter-term contracts, too, especially among the younger millennial/Gen-Y population, affording employees more work time sovereignty or flexibility through the various life stages (see “Re-Imagining work 4.0” by Germany’s Federal Minister of Labour and Social Affairs). The consequence of this trend is that resource companies are going to have to become smarter in how they recruit talent and manage large migratory (or contingent) workforces.

The second point is brand image. We are already beginning to see some second-order effects impacting higher education. Gavin Yeates sums this up quite well in his paper At the Brink, Again!“: 

“Projections for mining engineering graduates (S Hall 2017) show that, for 2019 and 2020, the number of students graduating will fall below demand. First year enrolments at [mining education Australia] universities have declined dramatically since 2012. It is also apparent from anecdotal feedback that the mining industries’ social image is impacting its ability to attract current generation of students (millennials). It is well documented that millennials are more prone to making value-based decisions and are heavily influenced by perception (true or not).”

In essence, this means millennials don’t see the resources industry as particularly interesting and are looking to work elsewhere. Horror stories related to dam collapses, fracking, and river pollution have no doubt played a role. Despite efforts by individual companies, we have not done a fantastic job promoting the industry’s exciting technological achievements.

The bottom line is that natural resources companies need to understand what is attracting employees to other industries. The disruptive digital trends transforming those industries are creating exciting and attractive new opportunities for young employees. Failing to understand these trends and develop a transformation strategy that creates better flexibility and a better brand will mean that the industry risks losing the war on talent.

Data is the new currency

How we learn and explore data will change, and our ability to quickly adapt these insights into our business processes will allow us to capitalize.

With the advent of hyperconnectivity, in-memory computing, and supercomputing, Big Data has now hit the mainstream. Data has been described as the new currency, one that has helped companies like Google and Facebook make lots of hard currency. It’s a little ironic that the resources industry has probably collected more data over time than any other industry, but has failed to turn that data into new revenue streams.

While resources companies were debating whether they should retain all their data or only the data they deemed to be useful to make operational decisions, tech companies discovered that access to more data is the most important thing. They offered a range of free services to obtain more data without necessarily knowing what they would ultimately do with it.

As technology has evolved and things like mobile, machine learning, and AI have improved, the “how” to commercialize this data has become apparent. The type of data these tech companies have been collecting – data about individuals – is also quite interesting and perhaps a little different from the data that resources companies have collected.

The companies that play this game well are seeking to uncover innovative ways to learn more about the people who consume their products – to understand exactly how they use them, when they use them, and what compels them to use them. We have seen new technologies enter our homes through things like Nest, Google Home, and behind-the-meter sensing technology. Retailers track everything we buy, which informs how they position new products to us. Natural resources companies will need to get a lot better about gathering more data through their supply chains to get a better idea of how changes in consumer behavior will impact their business.

This may mean that they need to get in on the data-trading game to be able to develop insights beyond their business boundaries and identify opportunities and risks emerging far up and down their supply chains. For example, consumers are starting to seek out brands that demonstrate ethical fashion and electronics. Resources companies will not escape this trend, as consumers will demand that end products are free of conflict minerals.

Blockchain will be one of the technologies that makes all this possible, as Jennifer Scholze explained in a recent article. Similarily, consumers are also seeking to move to “Green Power” with energy retailers finding new ways to sell green power rather than power produced by fossil fuels. These ethical trends are very powerful and can quickly change entire industries, but today’s resource companies have little intimate knowledge of it.

How we explore data is also changing. Traditionally, this has been the dark art of data scientists, analysts, and geoscientists, a closely guarded secret sauce that many resources companies see as a compelling differentiator in exploration and cost reduction. However, with the advent of mainstream hackathons, it’s become clear that the smartest people may not reside within your own organization, and the solution to your problem may be best solved by an individual or group of individuals that know absolutely nothing about what your company does. This is starting to redefine how resources companies need to look at data.

The more innovative companies are finding smart ways to identify what data is sensitive and are finding clever ways to anonymize and expose it to a large community to help solve a problem without giving its secrets away. Companies like Unearthed are helping resource companies use hackathons as a very effective way to solve problems, and connecting them with communities of talent that exist outside their organizations.

While data and insights are certainly a great first step, it’s really about what companies do with that insight that creates real value. In the future, the most successful resources companies won’t differentiate themselves necessarily by the insights they can generate from data, but rather their ability to execute and derive value from that data. The most powerful data platforms of the future will allow companies to quickly pull data together from a range of different data sources, expose that data to different insight-generating channels (both internally and externally), then quickly consume that insight by embedding it into their business systems or adapting their business process.

Perhaps the best example of this within resource companies is the maintenance space. Predictive maintenance has become a major buzzword in the industry, with many service companies offering services to generate insights based on data from machines. While that insight is great, you need to be able to change your maintenance and supply process to take full advantage of the insights at hand. If you can’t fundamentally service maintenance demand with your maintenance, repair, and operations (MRO) supply chain, your ability to derive value is compromised. The consumer and manufacturing industries figured out a long time ago how to optimize their supply chains and production plans based on changes in demand signals.

Roy Hill, an iron ore miner in Western Australia, has taken lessons from the manufacturing industry and defined a new way to better service maintenance demand by deploying manufacturing principals, as Indrasen Naidoo explains in “Reframing Maintenance Repair And Overhaul Supply Chains Within An Industry 4.0 Context.”

Wrapping up

The overall lesson here is that there is a huge opportunity for resources companies to think beyond the boundaries of their own companies. Perhaps one way that resources companies could explore this would be to treat their machines like customers. Those machines generate demand in the same way individual customers do. The demand requirements for those machines need to be met in the same way a manufacturer would approach it, producing maintenance jobs as products. If resources companies could learn to think in this way and apply that thinking beyond their boundaries, I have no doubt they would uncover a range of opportunities.

At SAP we are actively developing new technologies that help natural resource companies manage transient workforces; recruit, train, and retain the best talent; and turn insights into action. Learn more about SAP’s Talent Management solutions.

Anton Kroger

About Anton Kroger

Anton Kroger is an Energy and Natural Resources industry solution specialist for SAP based in Australia. Anton has worked in the resources sector for 16 years and has field operations and management experience, both locally in Australia and internationally. He now works with Energy and Natural resources companies across Australia and New Zealand to help them run better, more innovatively and imagine new ways of doing business. He is an advocate for clean energy and resources and believes that innovation is critical to the future of this industry. Anton believes that despite the disruption taking place in the industry today there is still a lot of opportunity for existing companies in the future.

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.

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.