The Problem With Exponential Innovation: People

Timo Elliott

We’re linear humans in a world of exponential change.

“Exponential emerging technological changes run counter-intuitive to the way our linear brains make projections about change, and so we don’t realize how fast the future is coming.” Jason Silva

The concept of exponential technologies has been popularized by organizations like Singularity University, which defines it as technologies where the power and/or speed doubles each year, and/or the cost drops by half.

One of the big challenges with exponential technology change in business is that – by definition – it starts off slow. In the early days, pilot projects can look like they’re failing compared to the incremental improvements of more established approaches.

The result is that the projects look like a bad deal, resulting in organizations taking too long to invest – until it’s too late to catch up with those who adopted early.

The trick, of course, is to know which technologies are going to be truly exponential, and when is the right point at which to invest.

Artificial intelligence is a great example: the opportunities have long been clear, but the results have been disappointing for decades – until now, when advances in computing power and the availability of data mean that there are almost daily breakthroughs.

Blockchain is following a very similar path. So far, there’s been a big contrast between an amazing vision of the future and the current, very real practical limitations – I heard one Gartner analyst last year call it the most over-hyped technology he’d ever seen. But technology changes fast, and organizations have realized that if they don’t start investing now, they’ll be left behind when it starts to scale.

The problem of human linearity also applies to the adoption of new technology. People don’t like change, so it takes time for organizational processes and cultures to adapt to new ways of doing things.

Simply being aware that fundamental changes are happening isn’t enough. New startups, with the ability to create new organizational cultures from scratch, are often the first to achieve scalable uses of the new exponential technologies, even as incumbents have massive theoretical advantages. The car industry is a great example, with Tesla clearly setting the standard with a modern vision of electric-powered transportation while the rest of the industry tries to catch up.

So how do we fix the “problem” of exponential technology? We need to be able to implement organizational change at the same rate as technology change. And that means we need to invest more in people.

Paradoxically, this means that getting the most out of technology means spending less time on technology. At least, that’s one of the lessons behind the rise of new digital innovation systems that are designed to help organizations actually succeed with digital transformation, rather than just implementing digital technologies.

What do you think? Can we leverage today’s exponential technologies to help us linear human beings to adapt to technology at exponential speeds?

More quotes about exponential technology and human limitations:

“Our intuition about the future is linear. But the reality of information technology is exponential, and that makes a profound difference. If I take 30 steps linearly, I get to 30. If I take 30 steps exponentially, I get to a billion.” Ray Kurzweil

“Technology has advanced more in the last 30 years than in the previous 2000. The exponential increase in advancement will only continue.” Niels Bohr [he said this more than 50 years ago!]

“Technology advances at exponential rates, and human institutions and societies do not. They adapt at much slower rates. Those gaps get wider and wider.” Mitch Kapor

“People need to understand how exponential technologies are impacting the business landscape. They need to do some future-casting and look at how industries are evolving and being transformed.” Peter Diamandis

Learn how SAP Leonardo can help companies navigate the new, digital renaissance and become digital businesses. 

This article originally appeared on Digital Business & Business Analytics.


Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in publications such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

Professional Services Firms: Innovating At Low Risk With Cognitive Technologies

Catherine Lynch

Professional services firms are facing challenges to their traditional business models due to new digitally enabled business models. Opportunities to innovate at low risk are arising with the emergence of cognitive technologies such as machine learning and conversational UI and are helping to drive significant efficiencies and higher employee engagement.

How mature are professional services firms in terms of adopting machine learning? As is the case for cloud adoption, professional services firms are early adopters of cognitive technologies to help improve employee and customer engagement and to meet the expectations of the high percentage of millennial employees in the workplace.

According to IDC, businesses using a cloud-based travel and expense (T&E) platform spend 49% less time on travel planning and 70% less time creating expense reports. Those hours can add up and affect cash flow, especially if a business lacks visibility into employee travel. “With cloud penetration approaching 85%, travel and expense apps have become an effective first step for businesses to digitally transform. Businesses can expect more automation of travel and expense processes in the coming years, thanks to integration with third-party applications and embedded machine learning capabilities.” says Jordan Jewell, senior research analyst IDC, Enterprise Applications and Digital Commerce.

Embedding a chatbot into a time and expense application

A leading global professional services firm has deployed a preconfigured cloud-based Time and Expenses solution to more than 90,000 users worldwide and is reaping benefits including enforcement of travel policies, eliminating duplicate payments and fraud. To assist with the global deployment and to provide a responsive support service to new users, the firm embedded a chatbot within the T&E application to help resolve frequent questions with the application. The return on investment in the chatbot was achieved in months.

By leveraging machine learning, user questions and problems are accumulated in a Frequently Asked Questions database to ensure that optimal and consistent answers are provided. Chatbots reduce the need for service desk calls and most commonly asked questions are easily resolved using natural language with direct interaction to chatbots.

Improving the onboarding experience with chatbots and virtual reality

EY is putting people at the center of its talent management program and harnessing digital tools such as VR and cognitive technologies to enhance the employee experience and engage differently with an increasingly diverse workforce. 

EY is intensely focused on employee engagement and hires hundreds of millennials straight out of college every year. The firm has invested significant resources in improving the onboarding process for new hires (and return hires) to improve the onboarding experience for an increasingly diverse workforce.

By combining virtual reality techniques to simulate a visit to the office and adopting conversational UI, the firm has increased employee engagement levels significantly. The 1,000 most commonly asked questions by new hires have been captured in an FAQ database and are managed by the chatbot. New employees can engage via their mobile phone interactive voice response chatbot to guide them through the new hire process. Significant cost savings can be achieved by reducing the onboarding process, even by a single day, by leveraging cognitive technologies.

Interestingly, the three most common questions from the new hires are related to payment of their first paycheck and expense payments. An average of 5.58 questions are asked by the new hire in the first few weeks.

Gartner predicts that by 2020, 85% of all customer interactions will be handled by chatbots.

Are you attending SAPPHIRE?  Join us at the SAP Industries Experience Area during the event and check out the Professional Services sessions on the agenda builder.


Catherine Lynch

About Catherine Lynch

Catherine Lynch is a Senior Director of Industry Cloud Marketing at SAP. She is a content marketing specialist with a particular focus on the professional services and media industries globally. Catherine has a wide international experience of working with enterprise application vendors in global roles, creating thought leadership and is a social media practitioner.

The “Emotional” Enterprise

Maricel Cabahug

In my previous blog, Soft Skills in a Software-Driven World, I talked about the important role that people and their soft skills will play in the intelligent enterprise. Here I’d like to continue that discussion with my view on how natural language processing (NLP), virtual reality (VR) and augmented reality (AR) will make the experience of the intelligent enterprise more emotional for us as well.

Our expectations for intelligent systems to understand us, help us, and connect with us on an emotional level will increase exponentially in the coming years. We will be conversing and interacting more and more with machines, expecting them to sound and react in a way that is convincingly human. We already see these technologies developing rapidly for the world of commerce, and consumer trends have driven and will continue to drive our expectations of software in the workplace. NLP, AR, and VR will make business tasks more personal, conversational, visual and visceral – thus more emotional. Once people get a taste for a richer and more compelling user experience for recreational purposes, the demand will increase for business tasks.

The rise of affective computing

Abstractions of today’s digital age such as the keyboard and computer mouse are neither natural nor particularly intuitive. Like reading and writing, they are learned rather than innate. These kinds of “meta” interactions will increasingly take a back seat, making room for more natural kinds of dealings with machines that rely on some of our earliest learned abilities and capabilities: seeing, hearing, and speaking. These innate abilities are connected at a very deep level with emotion. And it is emotion that is credited with being the magic key that unlocks learning, engagement, and memory.

The importance of emotion has not gone unnoticed by technology or industry, and it has given rise to a branch of computer science known as “affective computing,” pioneered by the MIT Media Lab in the mid-1990s. The goals of this new science reach in both directions: striving to make computers understand human emotion better as well as giving computers more emotional intelligence.

Business software makers have also taken notice, and are currently investing heavily in researching and crafting the personality attributes of digital assistants for the enterprise. There is also significant investment in how, using machine learning, digital assistants should adapt to user’s individual preferences.

Just as all politics is local, all emotional connection is personal. This is why personalization is so important. If you have ever talked to Alexa or any other digital assistant, you know that it is a very different experience from typing a search query into a search engine. The reason is that troupes of smart people are adding personality to today’s digital assistants. In the area of enterprise software, conversing with a digital assistant promises much more positive emotional potential than using a traditional GUI-based ERP system.

What’s next?

While we are making great strides in conversational user experience for business users (and of course, we are still in the early stages), we must continue to think ahead. The “next-gen” frontier will be immersive experience (IX), the term that bundles virtual, augmented, and mixed reality. Being transported visually and acoustically in time and space gets under your skin and goes directly to primitive centers of the brain. That level of tangibility surpasses anything we might experience today with a traditional GUI or even with simple video. Again, once people become accustomed to this in their private lives, they will demand more of it in their work environment.

My prediction is that work is about to get much more human and much more rewarding. As technology leaders, let’s embrace the future disruption and help everyone to succeed in the coming intelligent and emotional enterprise.

This article originally appeared on the SAP User Experience Community.


Maricel Cabahug

About Maricel Cabahug

As Chief Design Officer, Maricel is responsible for SAP’s overall design strategy and product design. At the heart of everything she is does is her goal to improve people's lives by making work delightful. She and her organization are passionate about co-innovation with customers to realize greater business value through technology that works for people. Maricel graduated with a Bachelor of Science in Mathematics and Computer Science from the Ateneo De Manila University in Manila (Philippines). She has an MBA from Lake Forest Graduate School of Management (Illinois, USA), where she graduated with honors. She also completed the program for high performers at the Harvard Business School.

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.