Embracing The New World Of Digital Procurement Requires A New Mindset

Tiffany Rowe

There is little argument anymore that the future of procurement is digital. New technologies, ranging from the Internet of Things to 3D printing to artificial intelligence, are changing the way companies of all sizes approach procurement and therefore changing the role of chief procurement officers (CPOs) and how they work.

While CPOs have accepted the inevitability of digital procurement, in many cases they have not yet determined exactly what it will look like for them, or what they need to do in order to successfully transition into this new world. The technology itself is still in development and advancing every day, but to take advantage of it, CPOs and other procurement professionals need to develop a new mindset and approach to their work.

Shifting from savings to value

For decades, the chief responsibility of procurement teams has been to purchase necessary goods and services for the company, with a priority on getting the best possible price for them. Because of procurement’s involvement in every aspect of a business, though, their role has expanded into one that’s integral to containing and reducing costs and maintaining a healthy bottom line.

In that vein, procurement has become more focused on value than on savings. Procurement teams must leverage the company’s purchasing power into not only securing the best price for goods and services, but also securing those goods and services within the terms and timeframe required by the company. This often means finding efficiencies and ways to take advantage of technology to streamline – and potentially automate – cumbersome paper-based transactions and ordering processes.

One way that many companies are taking a more strategic approach to procurement and improving value is by making procurement a more visible part of the company and developing more cross-functional teams to ensure a broader understanding of the business as a whole. This has meant bringing in staff from other departments to work in procurement for a stint to better understand those processes. It’s also meant adding procurement personnel to different teams and projects to both provide a purchasing perspective and learn more about other areas of the business. With these arrangements, purchasing becomes a more strategic process in which procurement decisions are made with an eye toward the value they can bring to the organization.

A strategic function

Shifting procurement toward a focus on value requires thinking strategically, something that hasn’t always been a priority for CPOs. Companies are now looking for procurement teams to have a better overall understanding of business fundamentals – even going so far as to encourage employees to enroll in online MBA programs to effectively learn about business strategy development and implementation. One area in particular that’s seeing a great deal of change is technology strategy, thus requiring a new approach.

In short, CPOs must turn their focus toward developing a technology strategy to deliver procurement services. The strategy must not only include a plan for developing and implementing a data architecture for procurement services, but also be focused on measuring and analyzing performance in this area. This usually means working closely with IT to develop a long-term strategy and a roadmap and a business case for investing in procurement technology, including specific goals, KPIs, and conclusions about how the technology can help achieve not only the overall strategic objectives for procurement, but for the organization.

Accepting artificial intelligence

Finally, moving into the world of digital procurement also requires an acceptance of the role of machine learning and artificial intelligence in procurement processes and learning to embrace to potential of these tools. Some have decried the rise of AI as a worst-case scenario, claiming that “robots” will eventually take over procurement and all purchasing will be automated.

While AI and technology do have potential to streamline certain processes, the likelihood of machines entirely replacing humans in procurement is highly unlikely. AI is a tool, one that can handle some of the most rote processes in procurement and provide data and insights that allow you to create more value and achieve the ultimate goal of protecting the bottom line. AI frees humans from processes that take away from their ability to innovate and solve problems, and therefore CPOs and their teams are better served to embrace the technology that’s already here and put it to use.

The rapid expansion of technology is changing virtually everything about the way we do business. By changing how you see technology and its role in your work, you can more fully embrace the technology and become an even more important part of your organization.

For more on digital transformation in procurement, see Integration: The Key To Digitizing Procurement Processes.


Tiffany Rowe

About Tiffany Rowe

Tiffany Rowe is a marketing administrator who assists in contributing resourceful content. Tiffany prides herself in her ability to provide high-quality content that readers will find valuable.

GDPR: Don’t Forget The Human Touch

Neil Patrick

If you’ve ever ridden a horse, you’ll be familiar with the phrase, “Dangerous at both ends and uncomfortable in the middle.” It applies just as well to the looming GDPR as it does to the equine world. The General Data Protection Regulation comes into effect on May 25, which for the complexity of the regulation – and depending on your level of readiness – is very soon.

We’ve all seen the considerable media coverage and the countless conferences dedicated to the technical measures and requirements. Much less, however, has been written about the human in the middle of it all. If you think about the human beings (otherwise known as your colleagues) in the midst of all this, there are at least three considerations shaping the human impact of GDPR – tone at the top, execution in the middle, and employee and contractor implications at the other end.

Tone at the top

It may sound like an obvious point, but unless there is executive sponsorship, a GDPR program will not reach deeply enough into the organization to be effective. It’s surprising how many organizations continue to make this mistake. Executive sponsorship ensures that the necessary change management and training programs will get properly funded, be adequately deployed, and have the necessary ongoing attention for a business as usual inclusion.

Sadly, a 2018 PwC study on the global state of information security found that less than a third of boards directly participate in a review of security and privacy risks. Without a solid understanding of the risks, boards are not well-positioned to exercise their oversight responsibilities for data protection and privacy matters.

Put bluntly, without executive sponsorship, GDPR programs are likely to become compliance tick-box programs, will not change how people behave, and are likely to ultimately fail.

Execution in the middle

Having a host of corporate policies and mission statements is one thing, but ensuring that named individuals are responsible for guaranteeing enforcement across the business is another. Article 5 of the GDPR requires controllers to demonstrate how they comply with the accountability principles. Article 83 talks about intentional or negligent violations. It is as much about certifying as guaranteeing.

The Information Commissioner’s Office (ICO) talks about rolling out the GDPR as “… a framework that should be used to build a culture of privacy that pervades the entire organization.” This requires middle management to push the message down and throughout the organization. People need to do this, not technology. People must take ownership of ensuring understanding and use of policies as standard operating procedures.

Execution also covers gap detection, escalation and mitigation, and disciplinary activities. People need training to understand what is acceptable and unacceptable within the parameters of the corporate data-privacy culture. There is frequently no single owner for developing a GDPR program. By virtue of its scope, GDPR is highly distributed and sits with legal, marketing, HR, procurement, customer support, analytics, R&D, and M&A.

Imbuing an organization with the correct data privacy culture will reduce the risk of breaches and sanctions. And of course, people come and go, get promoted, take temporary roles and sabbaticals, and go on holidays. The burden of ensuring that this is handled cost-effectively, consistently, and safely, in a “business as usual way,” lies with the people involved. In other words, preventing people from falling back on old habits and bad behavior sits with management teams and business process owners.

Execution also puts the equally essential bottom-up feedback channel back into the change management program. And if it is recorded digitally (software exists for this), an auditable trail of evidence of actions can persist to “police the police.”

Employee and contractor impacts

People who are deeply engaged with personal data, or who have access to systems and processes that contain personal data, need awareness and procedural training – with refresh enablement because GDPR is not a one-off occasion.

Every internal process, policy, and workflow ends up with a human being at the end who is required to perform an activity. Companies must ensure that this end-user behavior fits within the corporate data-privacy culture. (It’s surprising how many organizations make this assumption without checking or don’t have processes in place to confirm how well it is done.)

Former U.S. Deputy Attorney General Paul McNulty is often quoted saying, “If you think compliance is expensive, try noncompliance.” He’s right. The Ponemon Institute estimates noncompliance costs 2.71 times the cost of maintaining or meeting compliance requirements. Noncompliance costs come from those associated with business disruption, productivity losses, fines, penalties, and settlement costs, among others.

With a little planning, GDPR doesn’t need to be “dangerous at both ends” nor “uncomfortable in the middle.” The ICO has a great training checklist for SME organizations. In your pursuit of GDPR compliance, I’d urge you to consider the human being in the middle of your processes, policies, and technical requirements who will be on the receiving end of guaranteeing their adherence and enforcement.

This article originally appeared in Accountancy Age and is republished by permission.

Read 3 Reasons CFOs & Finance Professionals Should Attend SAPPHIRE NOW to learn about what’s happening at this year’s SAPPHIRE NOW and ASUG Conference – panels, keynotes, discussions, presentations, and endless ways to connect to people and gain new ideas for streamlining processes. Join SAP’s finance team and partners June 5–7, in Orlando, Florida.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube


Neil Patrick

About Neil Patrick

Dr. Neil Patrick is a Director of SAP Centre of Excellence for GRC & Security covering EMEA. He has over 12 years’ experience in Governance, Risk Management and Compliance (GRC) & Security fields. During this time he has been a managing consultant, run professional services delivery teams in the UK and USA, conducted customer business requirements sessions around the world, and sales and business development initiatives. Neil has presented core GRC and Security thought leadership sessions in strategic customer-facing engagements, conferences and briefing sessions.

People Are The Engine That Drives Finance Transformation: Part 1

Nilly Essaides

Part 1 of a 2-part series

You could have 500 robots and the fanciest predictive analytics tool. But that won’t move the needle on enterprise performance – not if your finance team isn’t receptive to and adaptive of smart technologies. This was the overriding message from The Hackett Group’s Best Practices Conference in early May. The theme, Unlocking Digital Value, was supported by multiple presentations about robotics, the formation of global business services centers (GBS), and adoption of artificial intelligence (AI). But speakers made one thing clear: Without the right talent-development initiatives, even the most sophisticated cognitive technology won’t yield transformative change.

To realize the promise of digital transformation, finance executives need to do two important things:

  1. Ask the right questions about the talent challenge
  1. Start preparing their staff for the future

Four key questions

Here’s what finance executives must ask themselves today:

  1. What skills will their teams require to support the business as it seeks to optimize its digital transformation?
  1. What will the finance workforce look like in the next 12 to 18 months and three to five years, given the prominence of the millennials and the prevalence of smart technologies?
  1. What new career paths must emerge to accommodate new finance organizational structures that lack the traditional “ladders” for advancement?
  1. What finance talent development plans are required to prepare incoming and existing talent for the challenges ahead?

One of the big problems is that it’s hard to imagine what those new roles will be. Nearly half of the companies in The Hackett Group’s 2017 EPM/Finance Talent Profile Quick Poll said they did not know what the future roles will look like. Half didn’t have a talent strategy.

That may explain that, while 91% of finance organizations expect digital transformation to have a major impact on their service-delivery model, only 35% in our 2018 Key Issues Study said they have the resources and competencies to execute on their digital strategy. It’s imperative that finance closes that gap if it’s going to realize the benefits of digital transformation.

Our 2017 Digital Transformation Performance Study charted which finance skills are both the most important and are also the hardest for finance to find (see below). Among the most critical and hardest to find are: storytelling, business acumen, problem-solving, and analytics. While tech savviness is difficult to find, it’s not the most important. That tells us that the automation of finance will only enhance the importance of our uniquely “human” skills.

In Part 2 of this series, we’ll look at the steps several companies are taking to prepare.

As artificial intelligence takes hold, the organizations that gain a competitive edge will be those that leverage The Human Angle.


Nilly Essaides

About Nilly Essaides

Nilly Essaides is senior research director, Finance & EPM Advisory Practice at The Hackett Group. Nilly is a thought leader and frequent speaker and meeting facilitator at industry events, the author of multiple in-depth guides on financial planning & analysis topics, as well as monthly articles and numerous blogs. She was formerly director and practice lead of Financial Planning & Analysis at the Association for Financial Professionals, and managing director at the NeuGroup, where she co-led the company’s successful peer group business. Nilly also co-authored a book about knowledge management and how to transfer best practices with the American Productivity and Quality Center (APQC).

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.