Weathering The Storm: Risk Intelligence Edition

Scott Pezza

In recent years, we have seen numerous extreme weather events across the globe that have had significant impacts on both individuals and businesses. From hurricanes and tsunamis to earthquakes and volcanic eruptions, these events often come with little warning and cause serious and long-lasting logistical issues. In this blog, we’ll look at the challenges these events pose and highlight how technologies like artificial intelligence can help mitigate – or eliminate – their negative impacts on your business.

Recognizing the event

It may seem simplistic to begin a discussion of “extreme” events by talking about recognition. By definition, they are large-scale and obvious. The most important element in this area, however, is focused on when you recognize the event. Here’s a helpful breakdown that lays our foundation for further discussion based on three alternatives. You can recognize that the event:

  • Has occurred. This is purely backward-looking. This is how we normally recognize events, especially when they do not impact on immediate geography. As individuals, we gather this information manually from newspapers, websites, and social media based on the first-hand observations of others.
  • Will occur. This is forward-looking, though typically limited to a few days or hours before the event. Aside from the most extreme or large-scale events, this typically comes in the form of warnings and alerts from governmental organizations. We may get this information from weather services, local governments, local news services, or official social media channels.
  • May occur. Here, this forward-looking view is based on the conditions that are known to have resulted in extreme events in the past. For some types of events (for example, weather-related), these look similar to the category above and come from sources like publicly available services. For others (for example, geopolitical events), any advance warnings are more likely to come from non-public or subscription-based third-party data services.

Understanding the impact

At this point, we have recognized that something has, will, or may occur. The next step is to understand what that means from a practical point of view. Weather events like hurricanes and floods will disrupt transportation routes and threaten electrical infrastructure. The ash from volcanic eruptions, as we’ve seen, can significantly impact airline routes far beyond the local eruption. Geopolitical events can impact these same areas while also introducing the possibility of a political or policy-based prohibition on certain activities in a geographic region.

As before, we can break down our impact analysis into three categories:

  • Immediate impact. This is the starting point. Whether observing after the fact or predicting ahead of time, we need to know what the impact is on Day 1.
  • Potential to expand. This answers the question of “what next?” Forest fires can spread great distances from their point of origin, and storm systems can travel hundreds or thousands of miles after making initial landfall. This applies predictive models to current observations to plot out potential expansion.
  • Challenge to address. Based on the scale and severity of the impact, we need to understand what is required to regain operational stability and how long that process may take. Localized power disruptions can be remedied in hours or days, while repairing damage to infrastructure like roads and bridges will likely be measured in months or even years.

Making crucial connections

One final element to cover before bringing all of this intelligence together is determining which of your locations, suppliers, sub-suppliers, customers, or trade routes are – or will be – impacted. This is where the complex nature of global supply chains and logistics networks can make things difficult to ascertain. The easiest group to account for is the list of locations for your own business and subsidiaries. Direct suppliers come next, though with additional difficulty when your vendor master lists its business (or billing) locations rather than lesser-known locations like manufacturing or excavation sites. Moving beyond direct suppliers to their suppliers, and their suppliers’ suppliers, exponentially compounds the challenge.

Disruptions can come at any point along the chain. Your retail outlet may suffer stockouts if a distributor’s warehouse is affected. Your production line may meet a similar fate if your raw material or subassembly supplier is hit with delays. Without a comprehensive supply chain mapping complete with geolocation data, anything beyond an analysis of direct and tier 1 suppliers is likely impossible without the aid of some software tool – and that’s assuming that you have perfect vendor master information to work from.

Synthesizing information for action

With that background, let’s see how AI tools can help bring everything together to help mitigate the impact of an extreme event. Imagine that a strong weather system is developing off the east coast of the United States, 500 miles east of Florida. You operate 50 retail locations across the Northeast and Great Lakes regions of the U.S. What do you do?

  • You have enriched your vendor master to include not only billing but “ship from” locations for all suppliers. You’ve leveraged a third-party service to map all suppliers using a “global ultimate parent,” creating a hierarchy of related companies.
  • A weather service data feed sends text alerts with updates on the projected storm path. Using natural language processing (NLP), your system converts the alerts into data on projected locations and weather conditions.
  • Using a trained machine learning (ML) system, you project the expected severity of impact at each geographic location and predict how businesses in that region will be affected.
  • This process creates an impact map of geographic locations and expected disruptions. This is used as an overlay to your comprehensive supplier map to isolate and identify which suppliers are in the storm’s path.
  • You see that the storm is expected to turn north before making landfall, with a low likelihood of impacting your inbound shipments to the Port of Savannah. Your imports are safe.
  • The system sends an alert, however. Your Great Lakes regional distribution center is close to reaching an automated reorder point for an important piece of merchandise. That product is normally sourced from a distributor in Maryland that receives its imports from the Port of Baltimore – which is directly in the projected storm path.
  • Your supply chain collaboration software shows that this supplier’s existing inventory will not be sufficient to meet your upcoming order volume – and that the storm means that it is unlikely to receive enough inventory in time to fulfill your next order.
  • The system identifies a second supplier with adequate inventory to fulfill your order from California, leading to higher inbound transportation costs.
  • By integrating with your financial system, you know that each day of stockouts on this item will mean $50,000 in missed revenue. Based on the current storm projections, you expect at least a week of delays – or over a quarter of a million dollars in lost sales.
  • With detailed models of weather systems, supplier locations, inventory levels, and with integration to financial systems, you are confident in executing a replenishment order with the second supplier. An extra $25,000 in transportation cost will be well worth it to avoid 10 times that amount in lost revenue.

This is just one example of where new technologies like AI can help address problems that were incredibly difficult previously. There are many more use cases in risk intelligence and beyond, where cutting-edge solutions are redefining what is possible in the business context.

If you’re looking to learn more about what we’re doing in these areas, come to SAPPHIRE NOW in Orlando June 5-7. To give you a preview of just some of what we have in store, take a look at all of our procurement-focused sessions here at this link.


Scott Pezza

About Scott Pezza

As part of SAP Ariba's Digital Transformation Organization's Center of Excellence, Scott researches, compiles, and shares best-practice information to help SAP Ariba's customers get the most out of their investments. He has a dual focus on the emerging technologies (AI/ML, IoT, Blockchain, etc.) across the source-to-settle cycle, as well as a specific interest in the financial supply chain (invoice management, payments, discounting, and supply chain finance). His research helps inform strategic planning, performance measurement, and program execution. He has spent the past 17 years in the B2B technology space, in roles ranging from software development and support to research and consulting. Scott earned his BA in English and Philosophy from Clark University, his MBA from Boston University Graduate School of Management, and his JD from Boston University School of Law, where he served on the Executive Board of the Annual Review of Banking and Financial Law.

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

Peter Klee

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

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

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

Innovation and the digital core: A perfect partnership

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

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

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

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

Unifying the digital mindset and platform to build future success

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

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

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

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


Peter Klee

About Peter Klee

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

The Sorry State Of GDPR Readiness – And How You Can Turn It Around

Paul Clark

In the humdrum world of regulatory compliance, the European Union’s General Data Protection Regulation (GDPR) is equivalent to the storm of the century. An endless stream of forecasts has been warning about imposing fines and sanctions for any business caught off-guard. And millions of articles, white papers, and studies have been written by hundreds of analysts, thought leaders, and industry experts sharing their advice and best practices for successful compliance.

With the deadline coming in a matter of weeks, you would think that every company doing business in the European Union is ready to go, right? Not even close. In fact, 85% of companies based in Europe, the Middle East, and Asia are unlikely to be compliant in time – as well as many of the regulators who will police them.

Ready or not, here’s what you need to know about GDPR

Whether your business is already compliant or still scrambling to cover the basics, data protection is not a topic that can be taken lightly. In 2017, the Identity Theft Resource Center (ITRC) reported 1,293 breaches that put more than 174 million records at risk – a 45% increase over the previous year. And 2018 is shaping up to be no different as news headlines continue to detail malicious events such as lost information, misappropriated records, hacked applications, and ransomed systems.

The GDPR is designed to reflect our world where our personal data is captured, shared, and analyzed continuously by known and unknown entities. With this latest set of reforms, personal data privacy and consent will become our best weapon against misuse and exploitation.

Will the GDPR be your 2018 privacy firestorm or game-changing strategy for data protection? Check out the infographic below to take stock of the fundamental questions and readiness efforts you should consider now to secure compliant security practices for every area of your business.


Paul Clark

About Paul Clark

Paul Clark is the Senior Director of Technology Partner Marketing at SAP. He is responsible for developing and executing partner marketing strategies, activities, and programs in joint go-to-market plans with global technology partners. The goal is to increase opportunities, pipeline, and revenue through demand generation via SAP's global and local partner ecosystems.

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.