How Open Source Is Changing Software Innovation

Al Gillen

Open-source software has come a long way. After a period of maturation, most visibly to the wider public during the late 1990s and early 2000s – during the early days of Linux – the approach to developing technology under an open-use license, also known as open-source software, has now become mainstream.

In recent years, we have seen many commercially focused companies put both existing intellectual property and new projects that are being incubated into open source. This trend has been particularly strong with software used by developers as platforms or tools. Today, the vast majority of developer tools and platforms are open source or derived from open-source technology.

As enterprises across industries rush headlong into digital transformation, many have begun to emulate tech industry practices in how they use and produce software innovation and have thus increased adoption of, and even contribution to, open source. So what are the benefits of open-source engagement for the enterprise? Here are six broad benefits of using and contributing to open source.

  1. Not re-inventing the wheel. The most obvious reason to use open-source software is to build software faster without having to re-implement solutions to already solved problems. Companies must move fast to stay at the top of their game – and that means grabbing the best solutions contributed by a well-honed ecosystem and building their own added innovation on top of it. Doing anything else is suboptimal and will ultimately lead to a more burdensome maintenance and update responsibility, and ultimately falling behind competitors. Contributing software customization and adding value back to the larger open-source community can bring the benefit of better vetting and quality improvement of the code by the community.
  1. Ensuring strategic safety. It used to be that IT organizations bought important software only from large, established software vendors. Open source innovation now allows smaller players to provide viable solutions while assuring the buyers that they can have control over the technical direction of the software, thus avoiding unreasonable price increases or unnecessary product changes, and minimizing the potential for lock-in. The broadening of software supplies also expands the range of software solutions available to enterprises and keeps the larger software vendors on their toes. Using software that builds on an open source code-base also allows enterprises to participate in the technology’s evolution and maintain more control over the destiny of the products based on this software.
  1. Efficient experimentation and business acceleration. In the age of digital transformation, experimentation is the new mantra. Digital disruptors like Netflix, Uber, and Airbnb have revolutionized their industries and put the accelerated startup culture on the map with the famous “fail fast and often in the past” mantra. Modern enterprises have finally come to realize that to survive the disruption and keep up, they must adopt as much of that experimental culture as possible and overcome their fears of failure. Experimentation of this kind implies a speed in product and service development that building software in-house simply cannot deliver. By taking existing code developed by others, and leveraging this code in new offerings, enterprises can truly speed up their product cycles. Open source provides the digital shoulders that enterprises can stand on to compete. By further contributing new value back to open source, enterprises have a chance of generating mindshare for their offerings, also known as free marketing.
  1. The efficiency of standardized practices. Using open-source solutions means using somewhat standardized (in a de facto sense) solutions to problems. Such standardization of software patterns related to certain industries and verticals enforces a normalized and more optimized set of organizational practices that tend to be portable across that industry. This practice can simplify business process and allow companies to focus on competitive differentiation, rather than wasting resources on things that are not core to their business success.
  1. Cleaner and safer software. Creating software in open source means that engineers operate in daylight, enabling them to avoid the traps of plagiarized software and more easily stay clear of patents and copyrights. Additionally, the visibility open source provides can lead to more secure software and fewer vulnerability surprises, especially if a significant community evolves around the project and performs regular critical reviews. Many companies that create proprietary software have difficulties turning their large code-bases into open source because of the time-consuming intellectual property and security scrubbing processes needed to open the code. Open source IP-based businesses avoid this problem from the get-go. Starting new software initiatives in open source avoids these IP issues.
  1. Attracting, retaining, and motivating top developer talent. Beyond a good pay scale and a supportive work environment, there is little that can push developers to do high-quality work more than peer approval and the opportunity for recognition or even fame. Contributing software back to the community and allowing developers to enjoy the public recognition of their peers can be a powerful motivator and an important tool for employee retention. A similar dynamic is in play in the hiring process as tech companies compete with each other to build their software engineering teams. Offering the opportunity to be visible in a broader developer community, or attain a level of peer recognition, is potentially more important than paying top wages for star developers.
  1. Community-led innovation. With a diverse group of vendors and customers participating in open source efforts, open-source solutions tend to rapidly add functionality relevant to the audience faster than proprietary solutions can typically achieve. As a result, open source adopters are able to influence prioritization of capabilities added to open-source initiatives and quickly accomplish goals specific to their environment.

For more on future-focused digital innovation, see Business Networks: The Platforms For Future Innovation.


Al Gillen

About Al Gillen

Al Gillen is As Group Vice President of Software Development and Open Source at IDC. He oversees IDC's software development research portfolio. Research disciplines in this group include developer research covering census, demographics and developer activities; platform and cloud application services for developers, and developer lifecycle and quality assurance products. In addition, Al jointly oversees IDC's DevOps research program, and runs a program focused on the ecosystem of open source software pan-industry. In his 18th year at IDC, Al has participated in numerous IDC research areas, including infrastructure software (operating environments and virtualization software), enterprise servers, and developer software and services. He has long tracked open source software in infrastructure software markets, and now has expanded open source coverage to cover other market segments.

Top Ten Digitalist Magazine Posts Of The Week [May 21, 2018]

Shelly Dutton

300w x 200hDigitalist Magazine, online edition, covers a variety of topics around the challenges businesses face in the digital economy. Whether you’re interested in the future of work, customer experience, digital economy, the Internet of Things, or digital supply networks, we provide a vast array of thought leadership and real-life stories on the practical application of digital technology.

Each week on Digitalist Magazine, we publish a list of the top ten posts of the week from across our content categories. We hope you find these articles valuable, informative, and interesting.

A New Day At The Grocery Store

Data And The Reversal Of Digital Transformation Misfortune

From Consumer Products To Consumer Outcomes

Technology In The Public Sector: Possibilities And Challenges

Soft Skills In A Software-Driven World

A Fresh Look At ERP Brings New Growth To Small And Midsize Businesses

HR In The Age Of Digital Transformation

To Boost Customer Loyalty, Prioritize Corporate-To-Bank Connectivity

Why We Must Rethink The Global Food System

Why Communication Standards Matter In FP&A


What Makes A Finance Solution Intelligent?

Neil Krefsky

More than any other profession, the role of finance professionals has been defined by what technology will allow them to determine how they can add value and influence across an enterprise. What finance teams are able to do, how they are viewed within their stakeholder base, and where their strengths and weaknesses lie are very much dependent on that underlying technology capabilities (or limitations).

Instead of relying on computers for simple automation of rote administrative activities, new, more sophisticated software is being used to “cognify” – embedding the ability to “think” – into previously manual, time-consuming accounting and reporting processes.

There is a new reality for finance that allows professionals to determine how they work as opposed to being boxed in to a predefined role. These transformative and intelligent technologies like in-memory, machine learning, predictive analytics, cloud, and others are changing the way we work and the value finance is bringing to the organization. The impact is substantial. In a study from Oxford Economics, 73% of finance leaders agreed that automation is improving efficiency within their organization and throughout the company, freeing bandwidth for more strategic tasks.

At SAP, we understand that making our complete portfolio of financial solutions intelligent is key to enabling our customers to be the best-run businesses they can be. Let us explain what that looks like with some real-life examples.

Advanced automation and machine learning

Automating tasks no longer requires intensive human oversight. Intelligent finance applications can perform daily routines more efficiently than ever. New technology offers advanced automation, and machine learning handles critical, resource-intensive tasks more quickly and accurately than any human can. There are tremendous time savings associated with these features. Ventana Research estimates that one-third of the repetitive, rules-driven work performed by accounting and tax departments can be eliminated through machine learning, AI, and blockchain automation.

SAP is applying advanced automation and machine learning to the core financial functions that every finance team depends on. In one example, cash application software uses machine learning techniques to analyze historical data. The software uses this analysis to continuously seek out efficiencies, improve processes, and clear payments automatically. The more it is used, the smarter it gets – identifying mistakes, inputting errors, and other anomalies – before presenting just the exceptions for human action.

Prediction and analysis

Intelligent finance tools can scan colossal amounts of data and pull out specific information that illustrates trends and behavior patterns. They can bring together actuals, forecasts, and simulations to uncover market trends before they occur, thus increasing the accuracy of predictive scenarios. SAP benchmarking found that predictive analysis is guaranteed to produce a return on investment (ROI) – and this ROI compounds. Again, the more you use them, the greater the return.

SAP is using this type of intelligence in its core finance and analytical processes with an application that offers deep insight by predicting critical KPIs with an audit trail. This allows key figures to be traced back to transactions in the system that aren’t GAAP posting–relevant yet – for example, sales orders. This advance knowledge offers you greater opportunity to strategically adjust and redirect resources to proactively address any deficiencies early in the process.

Fraud detection

According to research from the Association of Certified Fraud Examiners, an organization’s typical occupational fraud loss is 5% of its annual revenue. Finance applications with intelligence built-in offer new options to combat these losses. For example, organizations can leverage machine learning and prediction to automatically detect and rank attributes that positively correlate with fraudulent system entries. This results in information that can be used to reduce risks in financial transactions.

It also has the capability of identifying noncompliance with corporate policies, which can help strengthen the controls that ultimately lead to better enterprise-wide governance.

Digital assistant for the enterprise

As machine learning matures, there are significant opportunities to complement with voice-activated, in-context collaboration tools to digitally assist with assist with financial, accounting, analytical, and risk-based tasks. The use of such tools, both at home and in the office, is growing exponentially. In fact, according to a Gartner Study, by 2020, the average person will have more conversations with bots than with their spouse.

In a business setting, digital assistants provide voice-activated, context-sensitive support within enterprise systems to boost the productivity of finance experts and key decision-makers. For example, natural language processing can create a human-like experience in interacting with your ERP system. Simply ask for the name of a supplier and immediately start collecting information such as open purchase orders or open disputes, payments, accounting postings, and more.

Intelligent technology has created a new reality for finance, defining its value across the organization. By embedding intelligence in finance applications and processes enterprise-wide, finance leaders can leverage improved levels of insight, prediction, and efficiency to drive growth and inform strategic initiatives throughout the business.

Don’t get left behind. Click here to discover how intelligent finance can enhance your business performance.

And 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 Krefsky

About Neil Krefsky

Neil Krefsky is a Senior Director of Product Marketing at SAP Finance LoB Solutions. He is responsible for the development and execution of the product marketing strategy for SAP's solutions for the Finance Line of Business including: SAP S/4HANA Finance, Financial Planning and Analysis, Accounting and Financial Close, Treasury and Financial Risk Management, Collaborative Finance Operations, Enterprise Risk and Compliance.

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.