Smart Cities Are About Helping People, Civic Leaders Agree

Andy Canham

The hype around Jetsons-esque visions of “smart cities” seems to be settling down, and now public leaders and urban planners are turning their minds to how practical use of technology can benefit the only thing that really matters to cities: people.

That was the theme of SAP’s Third Annual Smart Cities Forum in Toronto on April 23, when the likes of Toronto, San Diego, Mississauga, Cambridge (Ontario), and Bolzano (Italy) came together to discuss how they are applying digital solutions to civic problems.

Stefan Gasslitter, CIO of the City of Bolzano, a medieval gateway to the Dolomites mountain range in the Italian Alps, told the story of how the city is using blockchain and cloud technology to automate government processes.

Blockchain’s guaranteed trust has enabled the city to store digitally approved copies of documents and do away with much of the paperwork associated with public administration. The project is now seen as a pioneering success that could pave the way for blockchain-based systems for paying taxes and student loans and voting.

Staying on the subject of pioneers, City of San Diego deputy chief operating officer David Graham shed some light on why the California beach town has been called “America’s smartest city.”

San Diego’s government has partnered with the region’s electricity companies and non-profit Cleantech San Diego to digitize utilities for citizens and make them more environmentally friendly, and the city was an early adopter of infrastructure for electric vehicles (there are more than 1000 charging stations in San Diego).

But Graham didn’t use his talk to show off. “There are a lot of trendy gadgets,” he said, “but the focus should be on addressing the challenges facing communities. If your 5G Wi-Fi-enabled network is not [doing this], it’s useless.”

He said cities should pay more attention to digital literacy and include underserved communities in their plans, along with partnering with academia and industry. “Universities are not just about academia; they can also contribute to improving people’s lives,” he added. “All cities should use that resource.”

Leaders from the City of Toronto would likely agree. Chief digital transformation officer Michael Kolm said the work he and the city’s ecosystem of tech innovators, academics, and urban planners are doing is about helping people by using data in more meaningful ways.

“If Smart City 1.0 was about the technology, Smart City 2.0 is about the social and economic perspectives,” he said. “We are not going to solve these problems alone, and if we don’t have an educated workforce and make use of the talent available to us, we will not be a smart city.”

CIO Rob Meikle added that the pressure on him and his team to innovate is incredible, and the demands to deliver faster are increasing every day. Partnering with “companies that have deep pockets” and moving to a cloud-based model is helping the city catch up to that demand.

Tech enthusiasts have been paying attention to Toronto’s Quayside project, a waterfront development that will bring about “a revolution in urban life” using “ubiquitous connectivity, social networks, sensing, machine learning and artificial intelligence,” according to Sidewalk Labs CEO Daniel Doctoroff.

And while Meikle sees the project as an exciting opportunity for the city, he’s just as focused on how technology and data can be used to tackle the biggest problems Toronto faces, such as affordable housing, transportation, and infrastructure.

Meikle wants Toronto to be a responsive city as well as a smart city, which means using data for insightful analysis that helps public servants respond to city-wide challenges more intelligently and inclusively.

Shawn Slack, CIO of the City of Mississauga, a city of 800,000 just down the road from Toronto, spoke along similar lines. He wants to bridge the digital divide in the city, which means supporting at-risk citizens, youth, new immigrants, and others in the local community by building a digital ecosystem that provides access to digital services and support.

The city’s plan is made up of several integrated elements: The Kit (laptops made available in the city’s libraries and other agencies), The Connection (Wi-Fi-enabled, voice-activated digital kiosks located throughout the city), The Hub (remote working and learning spaces), and The Ride (public transportation including e-bikes connecting to hubs and connections across the city).

While smart cities might have once been about simply making things more efficient or introducing cool gadgets, there’s now a new purpose: Using technology to tackle the biggest social and economic challenges faced by cities around the world. To echo the sentiment at the Smart Cities Forum, a city is nothing without its people, and it’s nothing if it’s not giving them all opportunities to succeed in the digital economy.

For more on this topic, see What Makes A Successful Smart City?


Andy Canham

About Andy Canham

Andy Canham is the managing director of SAP Canada.

Customer Experience: Keeping It Real

Jennifer Horowitz

Both authentic and artificial intelligence play an important role in the customer experience (CX), where humans drive evolution and innovation.

Artificial Intelligence (AI) can deliver speed, analysis, and efficiency to the customer experience, but most would agree that only people can provide empathy, ingenuity, and context. What if a strategy that involves a customer-focused approach to artificial intelligence and the ability to automate CX processes could improve interactions, increase engagement, and empower humans?

Tony Tsai, chief innovation and information officer at customer service and technology provider TTEC says, “Digital transformation focused around the customer experience is not just about technology implementation but also about how humans carry forward the output of that technology. Brands which employ a thoughtful approach to automation and AI, including the use of analytics and insights blended with the human touch at key moments of impact, will deliver on the promise of truly exceptional customer experience.”

From that perspective, the human touch should never be lost during this time of digital disruption. Companies must stay up-to-date with AI while also maintaining a human approach to the emotional environment we live in. The sophistication of AI and emerging technologies increases the importance of the human aspect of customer interactions.

Authenticity and trust play a significant role in the success of high-performing businesses. Companies should be able to support productivity and growth while also supporting their customers emotionally.

There are ways to practice authentic intelligence. For example, AI robots can be developed to display more “human” qualities, which improves communications. The key is to engage customers in interactions that feel “real.”

The future of CX involves more authentic AI technology. “AI is poised to transform business in ways we have not seen since the impact of computer technology in the late 20th century,” Paul Daugherty, CTO of  Accenture, stated in a report. “As AI matures, it can propel economic growth and potentially serve as a powerful remedy for stagnant productivity and labor shortages of recent decades.” 

For more on technology and the customer experience, see Digital Experience: The Key To User Delight.


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.

Edge Computing And Cloud For Remote Operations, Part 1

David Cruickshank

Part 1 in a 2-part series

In this post, I continue to touch upon the topic of machine learning, but now more within the context of edge computing. Examined simply through the lens of a single lab, there is a plethora of project work occurring across multiple industries generating critical data through asset-intensive remote operations. Here, the goals and objectives of digital transformation include how to optimize operational integrity pairing the edge and the cloud.

The edge and the cloud for remote operations

With the continuous surge of Industrial IoT (IIoT) data – both raw and processed – driving formation and implementation of all digital business processes, the need today is for compute to be as close to where the data originates as possible. This is achieved through edge computing and local processing of the data that matters most. It offers the chance for process industries to improve end-to-end operational integrity for remote operations requirements in real time. The goal is to remedy asset issues, keep workers safe, and persistently and correctly abide by industry, environmental, and other government regulations.

By monitoring the assets at the edge, customers reduce operating costs and downtime and can dispatch repairs or replace equipment components before they fail. When you consider an upstream oil and gas operation like offshore drilling, real-time data and what it can tell you is critical to operational integrity. This is where things like packet delays can be disruptive to the business or demonstrably harmful to both assets and workers. Remote operations in oil and gas represent a fast-paced, decision-driven environment ready to benefit from better data and the advanced analytics capabilities that can make sense of it.

The ability to take local action with better data

Remote operations, whether found offshore or in some other isolated wilderness, must be capable and prepared to take local action as necessary even when cut off from the mainland. What these environments require in processing critical data originating at the edge does not mean compromising the benefits of cloud computing. Some argue that provided you can readily act on the data most critical to a remote operation in real time, this is the maximum value of the data collected and that once acted upon, it then can be discarded.

With immediate value obtained from the data first processed at the edge, it then allows IT/OT network managers more backhaul options to move edge data to the cloud. It is ultimately best for the data originating at the edge to move to the cloud, where it can be widely accessed and take advantage of other integration services to serve many applications. There is, for example, a significant role for ERP in IIoT, provided companies and their edge and cloud providers can demonstrate the ability to orchestrate operational and business processes seamlessly across multiple applications, platforms, and networks.

Cloud capabilities factor in where you perform Big Data analytics on the corpus of data generated representing your critical equipment located in important geographic regions, disconnected from centralized business systems. It is the cloud where you most effectively train the machine learning algorithms you expect to deploy at the edge. There is a need for edge computing at each remote operation, but the cloud is where you bring the relevant edge data from something like multiple rigs (multiple edges) deployed in the Gulf of Mexico.

Edge computing is essential for optimizing industrial data at every aspect of an operation pertinent to operational integrity. With effective edge computing, remote sites act upon the data that matters to a location’s real-time situation and how its business processes are optimized to act on insights gleaned from collected data.

The additive value of cloud

Does a firm need to collect and store all edge data? This may remain debatable over the foreseeable future relative to dimensions like data value, edge-data storage costs, or moving data. Yet this is where cloud capabilities factor in. This is where centralized computing power integrates Big Data originating from all remote locations and their networks to provide insights into operations. It is the cloud where you most effectively train the machine learning algorithms you deploy at the edge. There is immense value in your ability to learn from data originating from all remote locations. Machines and systems in any remote location can learn and become optimized from what is learned from other edge data.

If you wish to consume some solid knowledge about edge computing and cloud, there are many links to click and sources to draw from. My intent is to describe some current and ongoing project work that illustrates the most important dimensions of edge computing and cloud working together to meet the operational integrity needs of remote sites in process industries.

In Part 2, I will introduce you to an SAP Co-Innovation Lab project focusing on connected assets for asset health monitoring and maintenance. The focal point of this multi-phase co-innovation project seeks to enable persistent and accurate operational visibility at the edge for both headquarters and on-site operations. It aims to demonstrate real-time situational awareness and “insight to action” for workers at the point of work execution in remote regions.

For more insight on emerging technology, see Smarter Edge Industrial Manufacturers Need To Serve The Segment Of One.


David Cruickshank

About David Cruickshank

David Cruickshank is senior director for strategy and operations for the SAP Co-Innovation Lab. He leads the lab's efforts in Silicon Valley to enable ecosystem-driven co-innovation between SAP, its partners, and customers. Additionally, he manages all operational aspects necessary to run a multimillion-dollar data center to provision private cloud infrastructures to deliver productive SAP landscapes consumed by co-innovation projects seeking a faster track to market for commercially successful innovations.

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.