CIO Priorities For 2018: IT Thought Leaders Share Their Predictions

Rod Tansimore

For a provocative look into the near future of technology applications in business, we asked leading members of the SAP ecosystem to share their prophecies. For CIOs, who are rapidly evolving into an advisory role, the demands increasingly call for tough decisions on where to invest: what technologies, what skills, what new business models? Here’s what our gurus have to say.

Mala Anand, SAP, President, SAP Leonardo and Analytics

2018 will be the tipping point for cloud analytics with the growth of cloud data and applications. This trend will fuel demand for end-to-end cloud analytic platforms that deliver a rich set of analytic capabilities to discover, plan, predict, visualize, prepare, collaborate, model, simulate, and manage while leveraging common data logic. The software-as-a-service (SaaS) model offers business a way to take advantage of continual product innovations in a seamless experience with a common user experience, and will address analytical requirements throughout the organization at a lower TCO vs. fragmented solutions that cause inconsistencies and distorted data views.

Christine Ashton, SAP, Digital Office ERP Cloud, Global Chief Digital Officer

2018 will see a fundamental shift in the role of the CIO in businesses that recognize the value of instant, limitless scalability and immediate access to innovation. Postmodern CIOs will become innovation leaders with a seat – and a key voice – among the C-suite. This will mean:
• Moving from operating IT to innovating business operations
• Swapping owning software for leveraging interlocking cloud ERP solutions across an ecosystem
• Shifting from managing teams to growing an inclusive, confident workforce
• Changing the focus from keeping the lights on to delivering differentiation

The driver behind each will be to equip successful, business-wide digital transformation.

Mark Barrenechea, OpenText, CEO and Chief Technology Officer

As IoT-connected devices push the limits of the cloud, a new paradigm where the cloud and edge computing meet will emerge. Edge computing moves processing power closer to the source of data, sending only data worth keeping to the cloud. Reducing costs and stored volumes of IoT-related data, the device itself will process time-sensitive data, quicker and with reduced network latency. As the number of devices continues to increase, edge computing will push the cloud into a supporting role. Together, the cloud and edge computing will provide the infrastructure needed to support the ever-expanding IoT universe. @markbarrenechea, @opentext

Brian Berns, Knoa Software, CEO

Most companies have little to no insight into how their employees are interacting with their ERP software. This lack of visibility is very costly, as it directly leads to a decrease in employee productivity. In 2018, businesses will increasingly adopt user analytics to gain insight into employee engagement with their software suites. This trend will be accelerated by the move to cloud and mobile enterprise solutions, as well as by the increased importance of user experience for a new generation of knowledge workers. User analytics are bound to become a key technology for IT organizations, which will enable them to measure application usage and identify inhibitors to adoption such as clunky user interfaces, process complexity, and heavy customization.

Orlando Cintra, SAP, SVP, SAP Cloud Platform, Latin America & Caribbean

Companies and C levels will realize that IT is the solved part of the equation. That said, they will turn a lot of attention to proving real business cases with high value so they can innovate with real purpose. Companies in an on-premise model will start to see their costs increasing dramatically compared with the cloud, since all the major players’ investments are going to cloud. Finally, innovation experts, data scientists, tech gurus, and specialists with good track records with success innovation projects will be in demand. The market will not produce talent to meet the expected demand. Companies In the niche to prepare new professionals will have big growth.

Ratnang D. Desai, Deloitte Consulting LLP, Managing Director

2018 will be the year when rapid evolution of technology will force organizations to “Reimagine Everything.” It is not enough to simply reengineer a business process. IT will play a critical role in helping organizations reimagine how disruptive technologies such as cognitive, cloud, and blockchain will fundamentally transform all functions. IT will take the next big step in its transformation, as well. IT and the business will work as true partners and collaborate seamlessly to quickly harness the power of disruptive technologies and turn it into a sustained competitive advantage. @ratnangdesai, @DeloitteSAP

Archana Deskus, Hewlett Packard Enterprise, Global Chief Information Officer

We live in a hybrid data and compute world, requiring flexibility and new architectures with sophistication to span edge to core, enabling distributed machine learning systems to distill end-to-end data insights. Increasingly, we will see the use of machine learning to turn that insight into action, in real time, enabling the hypercompetitive enterprise to disrupt business models and industries to create new experiences and revenue models. None of this will be possible without powerful, future-proof software-defined and memory-driven infrastructure that is consumed in novel, flexible ways. Outcomes as a Service will be the ideal IT delivery model going forward.

Mark Dudgeon, IBM, Global SAP Chief Technology Officer

I expect to see adoption of SAP S/4HANA ramp up significantly in 2018, with SAP S/4HANA Cloud providing an increasingly relevant option for organizations. Blockchain will be an increasingly hot topic, with a number of cross-industry and line-of-business use cases starting to come into mainstream – resolving issues with fractured ecosystem networks, providing transparency and single version of the truth across enterprises. Augmented reality (AR) and virtual reality (AR) are niche technologies in the world of business IT, but I expect to see the use cases expanding beyond training and education into areas such as service and maintenance.
Twitter: @MarkPDudgeon
LinkedIn: MarkPDudgeon

Mike Golz, SAP, Senior Vice President and Regional Chief Information Officer, Americas

As digital transformation takes hold and disrupts more industries, companies need to move from declaring it as critical (78% according to a recent Oxford Economics study) to truly committing to a digital strategy (currently at a dismal 3% in the same study). Whether the change primarily affects a company’s business model, its business processes, or the way people work, the underlying technologies, like machine learning, human/digital interfaces, IoT, or blockchain, are fairly well understood by now. Look no further than your phone. Chances are that you are using them as a consumer today. 3-D printing might look like an exception to consumerization; however, my insoles are based on iPhone pictures uploaded to an app.

Paul Lewis, Hitachi Vantara, Chief Technology Officer, Americas

During 2018, the nature of the CIO’s job will change from the role of “delivery executive” to that of “IT business executive,” realigning the focus from project status and infrastructure uptime to delivering on the three business imperatives. These are: operational efficiency, new customer experiences, and diversified business models of the corporations’ digital transformation strategy. People development will also become the primary consideration for innovation in IoT, AI, and cloud, which are creating a necessity to upskill, re-skill, and replace expertise and experience across disciplines by utilizing platforms to access partner ecosystems of talent, technology, and information.

Follow Paul on his BlogTwitter, and LinkedIn.

Greg McStravick, SAP, President of Database and Data Management

Data will continue to explode in 2018. Gartner predicts that 95% of new products will contain IoT capabilities, and that means companies will have to contend with a deluge of information even more voluminous than we see today. Organizations that have taken a wait-and-see approach to data management will be bulldozed over by those that made the early investments to make sense of it. Systems that enable data sharing, pipelining and governance along with intelligent machine learning and artificial intelligence in one connected landscape will be a game changer for every organization that wants to remain competitive. @McStravickGreg

Nathan Pearce, Capgemini UK, SAP Practice, Business Development and Innovation Lead

In 2018, we will witness another year of disruptive technologies. In particular, machine learning, combined with AI and data, will change the game in consumer engagement and personalization to help drive loyalty and advocacy. There will also be further developments upon VR and AR across many sectors. @npearce111

Manik Narayan Saha, SAP, Regional Chief Information Officer, APJ

Customers’ digital experience will continue to drive prioritization for companies around engagement, purchase decisions, and brand loyalty. I expect to see higher polarization between companies offering a great digital experience. AI will start to have an impact on traditional business processes – especially relating to back-office, shared services, and mid-office functions. Cloud will become the de facto standard to run the digital enterprise. Except in regulated industries, there is now even less of a compelling argument not to move to cloud-based services, and benefit from scale, agility, and speed. And I will be closely watching to see how the tech industry responds to EU GDPR, and helps customers make a successful transition. @maniksaha

Thomas Saueressig, SAP, Chief Information Officer, Global Head of IT Services

Machine learning and artificial intelligence will grow out of its experimental, early-adopter stage and hit the tipping point to broad adoption. Intelligent services will expand from consumer focus and supporting processes into the core of the enterprise and will become mainstream by 2019. The most underestimated theme I see will be the EU’s General Data Protection Regulation (GDPR). It will gain momentum in 2018 and eat up a fair amount of enterprises’ innovation capacities. Finally, advances in technology will not only shift our focus to other aspects of the services we offer, but will drastically change the way we work. New innovations are disrupting the status quo with exponential speed, requiring us to continuously adapt and learn.

Ronald van Loon, Simplilearn, Advisory Board Member & Big Data and Analytics Course Advisor

In 2018, machine learning applications will continue to mature, with each vendor featuring a domain-specific solution. Organizations need fully integrated, end-to-end data management platforms to handle increases in different data streams, including deep learning applications, while having the ability to transform this data into actionable insights. AI and deep learning applications in voice recognition and video analytics will also accelerate. Edge analytics will progress, corresponding with the massive increase in connected devices. It provides real-time analytic solutions at any point where data is generated, addressing data management challenges related to large amounts of data that can’t be centrally analyzed.

We invite you to stay tuned to the Digitalist Magazine to see where 2018 takes us. Best wishes for a productive and prosperous New Year.

Where will technology take finance in the coming year? See How Finance Is Thriving In A Digital World: 17 Experts Share Their 2018 Predictions.


Rod Tansimore

About Rod Tansimore

Rod Tansimore is a senior director of IT Technology Programs at SAP. He started his career as a systems engineer at IBM and moved on to hold numerous leadership roles in product management, product marketing, sales, and market development for large and small technology companies. Rod has B.S. in Engineering from Northwestern University and an MBA from Columbia University.

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.

Innovate The Future With An IT Landscape That Can Save Your Business

Peter Klee

Much has been written about how digital technology has changed every aspect of our world. In just a few short years, e-commerce has become the king of retail and customer engagement, large desktop computers have shrunk into more-powerful handheld devices, and data has become attached to everything from customer behavior to healthcare to traffic patterns.

And this is only the beginning of what’s to come. By 2025, every technology, process, and experience will be propped up with a maturing set of emerging technologies – such as machine learning, artificial intelligence, and blockchain – running behind the scenes.

How can businesses lagging behind catch up and take hold of this rising wave of innovation? The answer lies in how you architect your IT landscape.

Innovation and the digital core: A perfect partnership

Enterprises that are leading or keeping up with the digital wave understand that their success depends on their ability and willingness to adapt to change. Disrupting the norm nowadays is about seizing endless opportunities delivered by breakthrough technologies and platforms. By harnessing machine learning, predictive analytics, blockchain, cloud service, and the Internet of Things (IoT), CIOs can innovate and reimagine the business model to engage, serve, and wow customers in ways their competitors can’t.

This feat may seem overwhelming, but the key is to connect the ERP system to every aspect of the business. Visualize ERP as the digital core holding all your data. Around this core are a digital periphery of solutions that create an interconnected web of insight. These insights become an essential asset for innovation by enabling real-time decision-making, on-the-fly resolution of operational and customer issues, and immediate identification of new opportunities.

Companies can further accelerate these advantages by integrating cloud line-of-business solutions to build a unified, end-to-end platform. The core is then strengthened with embedded capabilities without the need for a separate installation. Meanwhile, redundancies and integration efforts decrease while integrated functionalities advance.

Take, for example, your favorite sporting goods store. The business can get a real-time view of critical financial and resource availability by connecting its digital core with human capital management solutions running in the cloud. Core processes and analytics work as a unified unit to help find better ways to hire, retain, and engage the best store managers, merchandisers, and sales associates; understand local interests; and streamline the supplier network to ensure the right inventory levels to reduce costs. But more important, the company can focus today more on innovating future growth, and for years to come.

Unifying the digital mindset and platform to build future success

Innovation initiatives in most companies often run in fragmented silos that go in different directions. While a new process or business model may make sense to one team or organization, it does not necessarily mean that it’s beneficial for the entire enterprise. All innovation projects need to come together, using the same unstructured and structured information, technologies, and transactional data. Otherwise, any specific change introduced anywhere in the enterprise can be meaningless.

With a digital core serving as the engine for most processes, the application of new innovations further enriches the capabilities of the core and its connections. Removing parallel platforms that run redundant capabilities such as data storage and processing enables the business to build an IT architecture that is highly scalability, intelligent, mature, and low in total cost of ownership. Over time, this new dynamic opens the door to adopting more advanced technologies such as machine learning, predictive analytics, the IoT, and blockchain.

However, technology alone will not help a business achieve this state of unification. Most – though not all – cloud services alone do not deliver the DNA needed to deliver the right business processes and leverage the proper data to guide a smart innovation strategy. By tapping a digital business services provider, CIOs can shift an outdated ERP platform toward an architecture that supports a responsive evolution with simplification, predictability, and continuous connectivity.

For more on developing a digital strategy, see A Fresh Look At ERP Brings New Growth To Small And Midsize Businesses.


Peter Klee

About Peter Klee

Peter Klee has 23 years of professional IT experience and 10 years in enterprise architecture. He joined SAP in 2011 and works in SAP Digital Business Services as Chief Service Architect. Peter’s current focus is developing and delivering strategic roadmap services, helping customers to transition towards modern digital enterprise architectures.

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.

Tags:

Machine Learning In The Real World

Paul Taylor

Over the past few decades, machine learning has emerged as the real-world face of what is often mistakenly called “artificial intelligence.” It is establishing itself as a mainstream technology tool for companies, enabling them to improve productivity, planning, and ultimately, profits.

Michael Jordan, professor of Computer Science and Statistics at the University of California, Berkeley, noted in a recent Medium post: “Most of what is being called ‘AI’ today, particularly in the public sphere, is what has been called ‘machine learning’ for the past several decades.”

Jordan argues that unlike much that is mislabeled “artificial intelligence,” ML is the real thing. He maintains that it was already clear in the early 1990s that ML would grow to have massive industrial relevance. He notes that by the turn of the century, forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and logistics-chain prediction and building innovative consumer-facing services such as recommendation systems.

“Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success — these are the advances that have powered companies such as Google, Netflix, Facebook, and Amazon,” Jordan says.

Amazon, which has been investing deeply in artificial intelligence for over 20 years, acknowledges, “ML algorithms drive many of our internal systems. It’s also core to the capabilities our customers’ experience – from the path optimization in our fulfillment centers and Amazon’s recommendations engine o Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience, Amazon Go. “

The fact that tech industry leaders like Google, Netflix, Facebook, and Amazon have used ML to help fuel their growth is not news. For example, it has been widely reported that sites with recommendation engines, including Netflix, use ML algorithms to generate user-specific suggestions. Most dynamic map/routing apps, including Google Maps, also use ML to suggest route changes in real time based upon traffic speed and other data gleaned from multiple users’ smartphones.

In a recent article detailing real-world examples of ML in action, Kelly McNulty, a senior content writer at Salt Lake City-based Prowess Consulting, notes: “ML isn’t just something that will happen in the future. It’s happening now, and it will only get more advanced and pervasive in the future.”

However, the broader uptake of ML by enterprises – big and small – is less much less known. A recently published study prepared for SAP by the Economist Intelligence Unit and based on a survey of 360 organizations revealed that 68 percent of respondents are already using ML, at least to some extent, to enhance their business processes.

The report adds: “Some are aiming even higher: to use ML to change their business models and offer entirely new value propositions to customers…… ML is not just a technology.” The report’s authors continue, “It is core to the business strategies that have led to the surging value of organizations that incorporate it into their operating models – think Amazon, Uber, and Airbnb.”

McNulty notes that there are both internal and external uses for ML. Among the internal uses, she cites Thomson Reuters, the news and data services group, which, after its merger in 2008, used ML to prepare large quantities of data with Tamr, an enterprise data-unification company. She says the two partners used ML to unify more than three million data points with an accuracy of 95 percent, reducing the time needed to manually unify the data by several months and cutting the manual labor required by an estimated 40 percent.

In another example of enterprise use of ML, she notes that GlaxoSmithKline, the pharmaceuticals group, used the technology to develop information aimed at allaying concerns about vaccines. The ML algorithms were used to sift through parents’ comments about vaccines in forums and messaging boards, enabling GSK to develop content specifically designed to address these concerns.

In the financial sector, ML has been widely used for some time to help detect fraudulent transactions and assess risk. PayPal uses the technology to “distinguish the good customers from the bad customers,” according to Vadim Kutsyy, a data scientist at the online payments company.

PayPal’s deep learning system is also able to filter out deceptive merchants and crack down on sales of illegal products. Additionally, the models are optimizing operations. Kutsyy explained the machines can identify “why transactions fail, monitoring businesses more efficiently,” avoiding the need to buy more hardware for problem-solving.

ML algorithms also underpin many of the corporate chatbots and virtual assistants being deployed by enterprise customers and others. For Example, Allstate partnered with technology consultancy Earley Information Science to develop a virtual assistant called ABIe (the Allstate Business Insurance Expert). ABIe was designed to assist Allstate’s 12,000 agents to understand and sell the company’s commercial insurance products, reportedly handling 25,000 inquires a month.

Other big U.S. insurance companies, including Progressive, are applying ML algorithms to interpret driver data and identify new business opportunities.

Meanwhile, four years ago, Royal Dutch Shell became the first company in the lubricants sector to use ML to help develop the Shell Virtual Assistant. The virtual assistant enables customers and distributors to ask common lubricant-related questions.

As the company noted at the time, “customers and distributors type in their question via an online message window, and avatars Emma and Ethan reply back with an appropriate answer within seconds.” The tool was initially launched in the U.S. and UK but has since expanded to other countries and reportedly can now understand and respond to queries in multiple languages, including Chinese and Russian.

In the retail sector, Walmart, which already uses ML to optimize home delivery routes, also uses it to help reduce theft and improve customer service. The retail giant has reportedly developed facial recognition software that automatically detects frustration in the faces of shoppers at checkout, prompting customer service representatives to intervene.

Among SAP’s own customers, a growing number are implementing ML tools, including those built into SAP’s own platforms and applications. As SAP notes, “Many different industries and lines of business are ripe for machine learning—particularly the ones that amass large volumes of data.”

The manufacturing, finance, and healthcare sectors are leading the way. For example, a large European chemicals company has improved the efficiency and effectiveness of its customer service process by using ML algorithms to automatically categorize and send responses to customer inquiries.

In the mining sector, Vale, the Brazilian mining group, is using ML to optimize maintenance processes and reduce the number of purchase requisitions that were being rejected causing maintenance and operational delays in its mines. Before implementation, between 25 percent and 40 percent of purchase requisitions were being rejected by procurement because of errors. Since implementation, 86 percent of these rejections have been eliminated.

Elsewhere a large consumer goods company, the Austrian-based consumer good company, is using ML and computer vision to identify images of broken products submitted by customers from the over 40,000 products in the company’s catalog. The application enables the company to speed up repairs and replacements, thereby improving customer service and the customer experience.

Similarly, a global automotive manufacturer is using image recognition to help consumers learn more about vehicles and direct them to local dealer showrooms, and a major French telecommunications firm reduced the length of customer service conversations by 50 percent using chatbots that now manage 20 percent of all calls.

But not every enterprise ML deployment has worked out so well. In a highly publicized case, Target hired a ML expert to analyze shopper data and create a model that could predict which female customers were most likely to be pregnant and when they were expected to give birth. (If a woman started buying a lot of supplements, for example, she was probably in her first 20 weeks of pregnancy, whereas buying a lot of unscented lotion indicated the start of the second trimester.)

Target used this information to provide pregnancy- and parenting-related coupons to women who matched the profile. But Target was forced to modify its strategy after some customers said they felt uncomfortable with this level of personalization. A New York Times story reported that a Minneapolis parent learned of their 16-year-old daughter’s unplanned pregnancy when the Target coupons arrived in the mail.

Target’s experience notwithstanding, most enterprise ML projects generate significant benefits for customers, employees, and investors while putting the huge volumes of data generated in our digital era to real use.

For more insight on the implications of machine learning technology, download the study Making the Most of Machine Learning: 5 Lessons from Fast Learners.