Digitalist Flash Briefing : How Companies Can Stop Human Trafficking In 2018: Be Vulnerable, Act Now

Bonnie D. Graham

Today’s briefing looks at what’s being done to help more than 40 million people worldwide who are toiling in forced labor – many of whom working at companies that most of us do business with in some way.

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About Bonnie D. Graham

Bonnie D. Graham is the creator, producer and host/moderator of 29 Game-Changers Radio series presented by SAP, bringing technology and business strategy thought leadership panel discussions to a global audience via the Business Channel on World Talk Radio. A broadcast journalist with nearly 20 years in media production and hosting, Bonnie has held marketing communications management roles in the business software, financial services, and real estate industries. She calls SAP Radio her "dream job". Listen to Coffee Break with Game-Changers.

Three Ways Digital Transformation Is Disrupting The Metals Industry

Jennifer Scholze

The metals industry is at a crossroads. It faces decreasing global demand, trade flow disruptions, widening workforce skill gaps, and declining resource quality. These challenges have hurt profits and reduced capital investments. The metals industry is ripe for change – and digital transformation is leading the way.

Stefan Koch, global lead for metals in the mill products industry business unit at SAP, recently spoke about the future of the metals industry on the S.M.A.C. Talk Technology Podcast. Koch addressed the three major ways digitization will change the industry. Machine learning will simplify production processes and streamline operations. Virtual reality (VR) will enable virtual plant operations, creating new business models. Blockchain will enable verified material tracking for purchases like green (recycled) steel. Together, these technologies can disrupt everything from extraction to production to sales.

1. Machine learning simplifies production processes, predicts quality outcomes

“Smart machines” are not a new addition to the metals industry. The industry already relies on sensor data to monitor machine performance and maximize uptime. For most companies, however, that’s the current extent of this data utility.

“It’s still very often that you see this island of information,” says Stefan Koch on the S.M.A.C. Talk Technology Podcast. “Somebody thinks of production. Another one thinks of, “Oh yeah, that’s my customers, that’s my sales.” In the future, everything will need to go together and work together in an integrated way.”

Machine learning will allow companies to do more with their data, optimizing everything from materials sourcing to process adjustments. For example, a company could link systems across multiple operations and operators. This company could then use machine learning to either eliminate or automate redundant processes like invoicing.

Koch predicts that machine learning will also enable more advanced metal production capabilities that are cost-effective and high-value for the end customer. Presently, identical production processes may still yield slightly different finished products. These differences are due to naturally occurring material variances. Machine learning will allow companies to “look into the future” and predict quality outcomes down to the slightest variation. Producers could then pre-assign products to specific customers, delivering greater value and increasing customer satisfaction.

2. Virtual reality enables remote plant operations and value chain control

Will metal companies of the future still own physical deposits? Perhaps not, says Koch. On the S.M.A.C. Talk Technology Podcast, Koch notes that some metal companies are already moving away from asset ownership. These companies are “contracting production, resources, logistics, and materials” in a bid to control the value chain.

Consider, for example, a company that shares tasks with suppliers in other countries. This company could use virtual reality contacts to enable repair and control. The company could also use virtual reality to exchange or integrate data, boosting collaboration across the value chain.

Koch predicts that virtual reality will play an important role in streamlining remote plant operation. “These are concepts we see already picking up.”

3. Blockchain guarantees supply chain validity and authenticity

A blockchain is a tamper-proof distributed ledger that maintains a historical record of all data. Since this record is independent of a central authority, it is inherently resilient. Algorithms enable continuous verification and validity calibration. Data can be signed, timestamped, and immutably recorded in the blockchain. Blockchain can then provide essential transaction validation and purity verification, guaranteeing authenticity.

Koch predicts the metal industry will use blockchain to “provide faster and more rapid ways to authenticate materials.” In the recycling industry, for example, not all parties involved communicate with one another every day. The lack of a closed loop supply chain creates authentication challenges. In fact, Koch characterizes the current metal recycling supply chain as “a pretty random list of partners who interact on a long timeframe.” Blockchain solves this challenge by providing an immutable authenticity guarantee at each step.

Why the future of metals depends on digital transformation

Digitization is more than using predictive maintenance to maximize machine uptime. It’s about disrupting outdated processes and creating new business models.

The World Economic Forum predicts that, by 2025, digital transformation will create more than $425 billion of value for the mining and metals industry. Companies that embrace digital transformation will be best positioned to capitalize on this value creation.

To learn more about how digital transformation is disrupting the metals industry, listen to the S.M.A.C. Talk Technology Podcast with Stefan Koch. Learn how to bring new technologies and services together to power digital transformation by downloading The IoT Imperative for Energy and Natural Resource Companies.


About Jennifer Scholze

Jennifer Scholze is the Global Lead for Industry Marketing for the Mill Products and Mining Industries at SAP. She has over 20 years of technology marketing, communications and venture capital experience and lives in the Boston area with her husband and two children.

10 Ways Utility Companies Can Use Artificial Intelligence And Machine Learning

Gavin Mooney

Artificial Intelligence (AI) and machine learning (ML) are poised to unleash the next wave of digital disruption. They are set to transform business in ways we’ve not seen since the Industrial Revolution, fundamentally reinventing how businesses run, compete, and thrive.

What are AI and ML?

These terms are sometimes used loosely. In 2016, when Deepmind’s AlphaGo took on Korean master Lee Sedol, media coverage used the terms AI, machine learning, and deep learning interchangeably, as if they all meant the same thing.

Artificial intelligence is the broadest term, having been coined as early as 1955. It refers to the ability of machines to exhibit human-like intelligence.

Current capabilities refer to Narrow AI, which is the ability of technology to simulate human intelligence for a specific task only, such as playing chess. We are still some way from creating General AI, which is computer intelligence that is level with human intelligence across a range of tasks.

AI encompasses several different technologies and systems of which machine learning is one. Others include natural language processing, computer vision, and speech recognition.

Machine learning refers to the practice of using algorithms to parse large volumes of data, learn from it, detect patterns, and make decisions or predictions based on these patterns.

Deep learning is a subset of machine learning. It is based on neural networks and is a technique for implementing machine learning that has recently proved highly successful. Again, it is dependent on massive datasets to “train” itself.

So rather than the traditional method of using conditional coding to capture all possible scenarios and the actions required, the machine is given the ability to learn how to perform the task.

Why is AI taking off now?

AI has had its ups and downs over the decades since it was first conceptualized, but now we are finally seeing real progress and it is set to transform our world. The main reasons for this are:

  1. Massive computational power is now available at low cost and can be provisioned in the cloud very quickly. Improvements in GPU design (now with thousands of cores therefore ideally suited to parallel workloads) have increased the training speed of deep learning algorithms by 50x in three years.
  1. Big Data – there has been an explosion in the amount of data we all create, coupled with near limitless storage capacity. Large and diverse data sets provide better training material for the algorithms.
  1. Algorithms are now better at finding patterns in the mountains of data, and AI and machine learning platforms from players such as Google, IBM, and Microsoft are making it much easier to develop applications. Investment in AI – in particular, machine learning and deep learning – is growing fast. Machines are already as good as or better than humans at some tasks, for example, playing games such as chess and go, transcribing audio, analyzing images, and diagnosing diseases.

However, adoption remains low. Business leaders are uncertain about what AI can do for them, where to obtain AI-powered applications and how to integrate them into their companies. Ultimately, the value of AI is not to be found in the models themselves, but in companies’ abilities to harness them.

When should you consider machine learning?

Machine learning is suited to business problems where:

  1. The decision or prediction to be made is complex; for example, face detection, speech recognition, spam filters.
    • You have high-quality, clean, and recent data, labeled (ideally) to allow the algorithm to make sense of it.
    • Some margin of error is acceptable.

10 ways AI can be used in utilities

The utilities industry has great potential to embrace artificial intelligence in the coming years. Here are some examples:

  • Load forecasting. Short-term load forecasts are vital for utilities. Machine learning could be used to forecast supply and demand in real time and optimize economic load dispatch. In the UK, Google’s DeepMind has teamed up with National Grid to predict supply and demand peaks and hopes to reduce national energy usage by 10%.
  • Yield optimization. With AI, power providers can optimize generation efficiency with real-time adjustments across their assets. GE Renewable Energy’s “Digital Wind Farm” concept includes software that monitors and optimizes the turbine as it runs, increasing energy production by up to 20%.
  • Predictive maintenance can be bolstered with drones for asset inspections, replacing time intensive and risky manual inspections. The drones are trained using deep learning algorithms to automatically identify defects and predict failures without interrupting operations.
  • Demand management can be automated and made smarter with machine learning. In the UK, Upside Energy uses machine learning to manage a portfolio of storage assets to support the grid, while Open Energi controls devices with flexibility in their energy consumption to shift demand in real time. It is estimated that machine learning could be used to help unlock up to 6GW of demand-side flexibility which can be shifted during the evening peak without affecting end users.
  • Energy theft is a huge problem in some developing countries, such as Brazil, where theft accounts for up to 40% of the electricity distributed. AI can be used to detect usage patterns, payment history, and other customer data that may signal irregular behavior.

On the retail side, AI can unleash market forces as well as transform the user experience:

  • Customer insights. Machine learning applications could allow utilities to craft electricity prices that maximize their margins while minimizing customer churn. AI could be used to create individual offers and services to help utilities retain their most profitable customers.
  • Energy trading. In this era of the prosumer generating their own renewable energy and sending the excess back into the grid, platforms are emerging to allow peer-to-peer trading between producers and consumers. As supply and demand continuously fluctuate, AI can be used to more quickly match producers with consumers. In the Netherlands, Vandenbron connects consumers with renewable energy providers.
  • Virtual agents will revolutionize call centers, being able to respond to consumer queries and provide instant assistance. They will be able to automatically segment consumers based on service history and provide early warning of bad debts. The development of natural language technologies will eventually unlock the capacity to fully automate customer service.

Consumers can also benefit from AI:

  • Supplier selection. Machine learning can help customers choose their energy retailer by learning about their preferences – such as energy generation type, how much they’re willing to pay and their consumption patterns – and then scanning the market for the most suitable offers. Lumator, using software developed at Carnegie Mellon University, does this and can also automatically make the switch without interrupting supply.
  • Consumption insights. Meter data can be analyzed to extract the consumption profiles of a household’s hungriest appliances and see how much each contributes to the energy bill. Bidgely takes this one step further. Their universal disaggregation algorithm uses machine learning and a database of over 50 billion meter readings from smart meters to extend the appliance profiling to homes without smart meters.

AI opens a wide range of exciting possibilities for utilities but the changes will not be simple. Early evidence suggests AI can deliver real value to serious adopters, but current uptake outside of the tech sector remains low. Few companies have deployed it at scale. The opportunities and rewards are waiting for those willing to take the plunge.

To learn more about SAP solutions using AI and machine learning, visit here.


Gavin Mooney

About Gavin Mooney

Gavin Mooney is a utilities industry solution advisor for SAP. From a background in Engineering and IT, Gavin has been working in the utilities industry with SAP products for 16 years. After working as a consultant with a number of Electricity, Gas and Water Utilities across the globe to implement SAP’s Industry Solution for Utilities, Gavin joined SAP in 2014. He now works with utilities across Australia and New Zealand to help them simplify, innovate and run better with SAP's latest products. Gavin loves to network and build lasting business relationships and is passionate about cleantech and the fundamental transformation currently shaking up the utilities industry.

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.