How Digital Transformation Is Rewriting Business Models

Ginger Shimp

Everybody knows someone who has a stack of 3½-inch floppies in a desk drawer “just in case we may need them someday.” While that might be amusing, the truth is that relatively few people are confident that they’re making satisfactory progress on their digital journey. The boundaries between the digital and physical worlds continue to blur — with profound implications for the way we do business. Virtually every industry and every enterprise feels the effects of this ongoing digital transformation, whether from its own initiative or due to pressure from competitors.

What is digital transformation? It’s the wholesale reimagining and reinvention of how businesses operate, enabled by today’s advanced technology. Businesses have always changed with the times, but the confluence of technologies such as mobile, cloud, social, and Big Data analytics has accelerated the pace at which today’s businesses are evolving — and the degree to which they transform the way they innovate, operate, and serve customers.

The process of digital transformation began decades ago. Think back to how word processing fundamentally changed the way we write, or how email transformed the way we communicate. However, the scale of transformation currently underway is drastically more significant, with dramatically higher stakes. For some businesses, digital transformation is a disruptive force that leaves them playing catch-up. For others, it opens to door to unparalleled opportunities.

Upending traditional business models

To understand how the businesses that embrace digital transformation can ultimately benefit, it helps to look at the changes in business models currently in process.

Some of the more prominent examples include:

  • A focus on outcome-based models — Open the door to business value to customers as determined by the outcome or impact on the customer’s business.
  • Expansion into new industries and markets — Extend the business’ reach virtually anywhere — beyond strictly defined customer demographics, physical locations, and traditional market segments.
  • Pervasive digitization of products and services — Accelerate the way products and services are conceived, designed, and delivered with no barriers between customers and the businesses that serve them.
  • Ecosystem competition — Create a more compelling value proposition in new markets through connections with other companies to enhance the value available to the customer.
  • Access a shared economy — Realize more value from underutilized sources by extending access to other business entities and customers — with the ability to access the resources of others.
  • Realize value from digital platforms — Monetize the inherent, previously untapped value of customer relationships to improve customer experiences, collaborate more effectively with partners, and drive ongoing innovation in products and services,

In other words, the time-tested assumptions about how to identify customers, develop and market products and services, and manage organizations may no longer apply. Every aspect of business operations — from forecasting demand to sourcing materials to recruiting and training staff to balancing the books — is subject to this wave of reinvention.

The question is not if, but when

These new models aren’t predictions of what could happen. They’re already realities for innovative, fast-moving companies across the globe. In this environment, playing the role of late adopter can put a business at a serious disadvantage. Ready or not, digital transformation is coming — and it’s coming fast.

Is your company ready for this sea of change in business models? At SAP, we’ve helped thousands of organizations embrace digital transformation — and turn the threat of disruption into new opportunities for innovation and growth. We’d relish the opportunity to do the same for you. Our Digital Readiness Assessment can help you see where you are in the journey and map out the next steps you’ll need to take.

Up next I’ll discuss the impact of digital transformation on processes and work. Until then, you can read more on how digital transformation is impacting your industry.


About Ginger Shimp

With more than 20 years’ experience in marketing, Ginger Shimp has been with SAP since 2004. She has won numerous awards and honors at SAP, including being designated “Top Talent” for two consecutive years. Not only is she a Professional Certified Marketer with the American Marketing Association, but she's also earned her Connoisseur's Certificate in California Reds from the Chicago Wine School. She holds a bachelor's degree in journalism from the University of San Francisco, and an MBA in marketing and managerial economics from the Kellogg Graduate School of Management at Northwestern University. Personally, Ginger is the proud mother of a precocious son and happy wife of one of YouTube's 10 EDU Gurus, Ed Shimp.

IoT: Five Challenges Executives Face

Jennifer Horowitz

The Internet of Things (IoT) creates a plethora of opportunities, but it is not without challenges. Executives who want a head start in the IoT game should be prepared to overcome these.

Computer processor giant Intel recently announced that Intel Capital, the company’s investment arm, will allocate $72 million in funding for 12 startups to join its portfolio. So far this year Intel has invested $115 million in IoT ventures.

Executives are learning that along with its challenges, IoT also offers potential opportunities for solution providers who can successfully address issues such as scalability and system implementation. Let’s look at some of the most significant IoT challenges that are affecting leaders across the supply chain and logistics in the retail, oil and gas, agriculture, construction, telecom, governments, healthcare, and financial services industries.

Scaling

Scaling IoT device networks is a major issue for companies. While many companies can manage overall scaling issues, scaling the footprint for IoT presents some new challenges. Leaders should also be aware of issues in working with software, legacy devices, and the need for specialized solutions. Determining the departments responsible for IoT-related gateways, analytics, networking equipment, sensors, and software and hardware is also essential.

Implementation

Another challenge for many leaders is implementing IoT solutions. One of the main hurdles has been lack of support for deploying production quality. There is also the question of whether IT, operations, the C-suite, or some other department should handle implementation.

Data protection

Figuring out connectivity management is also a challenge. Executives face the issue of whether they should integrate connectivity management across any kind of environment. Additionally, determining if connecting the IoT ecosystem to disparate networks is required was also a factor. In a survey by IoT world, 36.73 percent of executives said that they were not confident “their organization could secure and protect all data within their IoT ecosystem.”

Security

Executives also noted security in the IoT world survey, with 72.16 percent of leaders stating that they embed and incorporate security into the product life and design of IoT devices. However, companies are still struggling with IoT security. The IoT world survey found that more than one-third of executives will not be staying with security patches and fixes, while 57.6 percent said they had not trained IT staff on the latest IoT security updates. Many executives do not even conduct vulnerability testing to identify network weaknesses.

Using blockchain to make IoT deployments

Many companies are considering the use of ledger technology for creating more secure and efficient IoT deployments. Some of the most important reasons for leveraging blockchain are to reduce the risk of collusion and tampering, developing trust for users, accelerating the rate of data transactions, and reducing IoT overhead costs.

For more insight on IoT, see Integrated Cloud And Analytic Solutions Are Driving IoT Adoption Rates.


Jennifer Horowitz

About Jennifer Horowitz

Jennifer Horowitz is a journalist with over 15 years of experience working in the technology, financial, hospitality, real estate, healthcare, manufacturing, not for profit, and retail sectors. She specializes in the field of analytics, offering management consulting serving global clients from midsize to large-scale organizations. Within the field of analytics, she helps higher-level organizations define their metrics strategies, create concepts, define problems, conduct analysis, problem solve, and execute.

For Mobile Network Operators, Simplification Is Everything

Virginie Debris

When I started in the messaging business 18 years ago, I remember asking my boss why mobile network operators don’t all enable access to their system through the same protocol.

It was then when I learned there was no such thing as a full normalization in our messaging business, and when I truly realized what a company like ours could bring to the business: Manage complexity, drive simplification.

Today, our operators’ customers must tackle a new challenge: How to face new technology investments like 5G while existing revenues are flattening?

Enterprise messaging could be a good answer to the need for additional revenues – with minimal pain and investment from the operators’ side.

There again, and as in the past 18 years in our industry, the two different worlds of telecommunications (Americas and the Rest of the World) are looking for different solutions.

Americas: Mobile network operators want simplicity

Due to the CLEC (competitive local exchange carrier) and the ability to get a 10-digit phone number, the OTT (over the top) players made money thanks to the businesses that couldn’t cope with short codes option and needed more flexibility and lower rates.

Within CTIA (Cellular Telecommunications and Internet Association), North American operators decided that 10-digit long code (10 DLC) could transmit enterprise messaging (A2P) as long as it follows certain rulesets. This is what operators called “messaging curation.”

MNOs in the U.S. would then request a specific tagging of the SMS terminated onto their network in order to classify it as peer-to-peer (P2P) or A2P under certain criteria.

But the key question there is “who will tag the SMS?”

The MNOs, quite rightly, don’t care – they just want to be able to identify those SMS. The content providers, quite rightly, don’t care either – they just want to be able to send SMS.

The rest of the world: Show me the money

The messaging world is disturbed by the SS7 network access in the rest of the world. The enterprise messaging providers managed to get a direct link to MNO’s network via direct SS7 connection or via another “partner MNO.”

When such “backdoor” SMS comes through using SS7 network, most of the time, the MNO receiving the SMS doesn’t get any revenue – only network usage and additional load. It’s “money left on the floor” for the end operator.

The operators then have to police their SS7 network messaging traffic in order to let the P2P SMS only go through SS7 while the A2P traffic is blocked or redirected to a payable content.

Operators then have to implement an SMS firewall and ensure that they’ll get this A2P traffic redirected through their VAS direct SMPP accounts.

Can this happen easily? Implement an SMS firewall is one thing. Monitoring this traffic and staying up to date with the latest findings from fraudsters is something else.

Also, when a firewall is implemented, how do you ensure that the A2P traffic will be directed to the MNO VAS accounts and makes money for them?

Managing complexity: Run simple

Whether it’s the rest of the world or the Americas, complexity is an issue that all mobile network operators face. The answer to solving this puzzle is selecting a company that can help you solve your SMS messaging challenges, not add to them.

At SAP Digital Interconnect, we believe that simplification is everything. Solving complexity is part of our DNA, and part of our value-add. Learn how we can help you simplify here

This article originally appeared on The Future of Customer Engagement and Commerce.


Virginie Debris

About Virginie Debris

Virginie Debris is the Vice President of Global Product Management at SAP Digital Interconnect. She has been working on the messaging business for 15 years and provides her expertise through different groups and forums including various GSM Association and CTIA workgroups. Virginie holds a Master of Science in Telecommunication Systems Engineering and a MBA in Marketing.

The Human Angle

By Jenny Dearborn, David Judge, Tom Raftery, and Neal Ungerleider

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.


This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.


The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

How Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.

Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!


About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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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.