How Blockchain Revolutionizes Supply Chain Management

Paul Brody

Part 1 in the 3-part series “Blockchain and the Supply Chain

Nearly all of the world’s leading companies run computerized enterprise resource planning (ERP) and supply chain management software. From connected manufacturing equipment to digital shipping notices and RFID scanning, products are tracked on computerized systems from their earliest origins, often all the way to the recycling bin.

Yet despite this huge investment in digital infrastructure, most companies have only limited visibility and insight into where all their products are at any given moment.

The culprit, in most cases, is the analog gaps that exist between systems within enterprises and across enterprise boundaries. Production may be recorded digitally, but the moment it moves to shipping, a PDF document is created for the shipping label that is little more than a software copy of a printout. The shipment may have its own digital number, but that number tells you where the box is and who signed for it, not what is actually in the box. And so on down the road: oceans of digital data but only islands of useful information.

This is not a new problem, and companies using systems like electronic data interchange (EDI) and XML messaging try to maintain information continuity across system and enterprise boundaries. But point-to-point messaging systems have their own issues, as they are often out of sync and move data only one stop down the supply chain. The result: inventory that seems to be in two places at once.

These systems were created for an era of big, vertically integrated companies with large, but mostly static supply chains. They were very relevant 30 years ago, but not so much today.

The advent of huge, dynamic ecosystems

Two big transformations have swept through global supply chains recently. First, supply chains are no longer traditional networks of OEMs and suppliers. Now they are vast ecosystems, with many product variants moving through multiple parties, all trying to coordinate work together. It’s not uncommon for a single company to have multiple contract manufacturers, all drawing upon a similar supplier network and feeding a range of distribution models, from traditional retail stores to online consignment services.

Secondly, supply chains and operations have become increasingly dynamic. Product lifecycles are shorter, and ramp-up and ramp-down periods are more intense.

Even as supply chains have transformed, companies have not updated the underlying technology for managing them in decades. With blockchain technology, companies can rebuild their approach to supply chain management at the ecosystem level and go from islands of insight to an integrated global view.

Trustworthy truth without trusted intermediaries

Everyone loves to hate middlemen, but it turns out they are really useful. Until the advent of bitcoin and blockchain technology, the only way you could get a large number of entities to agree upon a shared, truthful set of data, such as who has what bank balance, was to appoint an impartial intermediary to process and account for all transactions. Blockchains make it possible for ecosystems of business partners to share and agree upon key pieces of information. But they can do it without having to appoint an intermediary and deal with all the complex negotiations and power plays that come with setting the rules before handing over really critical business information. Instead of having a central intermediary, blockchains synchronize all data and transactions across the network, and each participant verifies the work and calculations of others. This enormous amount of redundancy and crosschecking is why financial solutions like bitcoin are so secure and reliable, even as they synchronize hundreds of thousands of transactions across thousands of network nodes every week.

The core logic of blockchain, applied to the supply chain

Apply that same security and redundancy to something like inventory, and substitute supply chain partners for banking nodes, and you have the foundation for a radically new approach to supply chain management.

The use cases for this new way of working are compelling. At its most basic level, the core logic of blockchains means that no piece of inventory can exist in the same place twice. Move a product from finished goods to in-transit, and that transaction status will be updated for everyone, everywhere, within minutes, with full traceability back to the point of origin.

Do you want to negotiate procurement deals based on total ecosystem volume—not just what you buy from a supplier, but what all your partners do as well? With a blockchain-based solution, you can calculate the exact volume discount based on total purchasing. You can mathematically prove the calculation is correct. And you can do so even while preserving the privacy of each company’s individual volumes.

Promising pilots

The added transparency offers proof about how goods were sourced and how they comply with regulations. The physical, financial, and digital information is brought together in one platform to reveal sources of value leakage—from everyday inefficiencies to fraud and abuse—and helps you hone new strategies to combat them.

Blockchains are still new technology, but the early results EY is seeing in pilots with clients suggest big benefits and the opportunity to recast how we approach these problems, from point-to-point integration to ecosystem-level thinking. We expect to see significant strategic transformations and fairly quick tactical returns as these solutions gain traction. I’ll examine both areas in more detail later in this series.

For more insight on advanced technology’s impact on supply chain management, see How Artificial Intelligence Will Transform Tomorrow’s Digital Supply Chain.


Paul Brody

About Paul Brody

Paul Brody is global innovation blockchain leader at EY. Paul is responsible for driving EY’s initiatives and investments in blockchain, playing a dual role as global innovation blockchain leader as well the Americas strategy leader for the technology sector. He has extensive experience in the areas of IoT, supply chain, and operations and business strategy.

People Are The Engine That Drives Finance Transformation: Part 2

Nilly Essaides

Part 2 of a 2-part series. Read Part 1

Part 1 of this series set out the premise that finance executives will realize the benefits of financial transformation only if their teams have the right skills – the overarching takeaway from The Hackett Group’s recent Best Practices Conference. In Part 2, we’ll look at how a few companies are getting ahead of the curve.

How are companies preparing?

Asking questions is no longer enough. Digital transformation is here, and the challenges are not two to three years away. They are 12-18 months ahead. Some companies are getting in front of this by taking steps to get their people and organizations ready. Here’s how:

They develop comprehensive talent-development programs. We’re working with several leading companies that are in the latest stages of creating comprehensive finance talent-development frameworks that begin with onboarding and continue through training, development, and retention. They are tailored to every employee in the finance hierarchy. They include training in technical, technological, and soft skills. They bring together finance and business leaders so that finance staff can get to know their “customers” right away, and they incorporate learning success in the performance evaluation process.

One large technology company has begun to hire computer science graduates into the internship program and train them in finance while training existing professionals in analytics skills using internal and external sources. The program also teaches soft skills and business partnering acumen.

Another European company launched an intense six-month training initiative last year that focuses on developing finance business collaboration capabilities. The company anticipates a big share of finance’s transactional roles to be replaced by machines. It established a business partnering “college” that provides a five-day “crash” course, followed by a 100-day practicum. The first 500 graduates delivered $300 million in additional value by improving business performance during their first 100 days.

They build digital centers of excellence. Companies are increasingly centralizing their digital talent and execution capabilities in hubs that bring like-minded experts together and allow them to design, build, pilot, and run new technologies. These Centers of Excellence, or COEs, eliminate the need for hiring multiple specialists within business units or functions and allow for quick ramp-up and faster time to benefit.

They typically comprise a small group of data specialists and IT-savvy finance and other executives who understand the business requirements and can bridge the language and knowledge gap between. The COE builds the business case, designs the first robots or AI applications, pilots them on a small scale or in a lab environment, tests, rolls them out, and runs the new systems.

This smart automation COE or digital operations provides leadership with knowledge of best practices, support, training, deployment, and operations of new digital solutions across the enterprise and/or within a function or business unit. Through knowledge-sharing and access to common tools and methodologies, it promotes higher delivery standards, communization of practices and lower cost, and faster time to benefit. Plus, perhaps most critically, the COE builds the governance and delivery model for quick scale-up throughout the enterprise.

One technology company leveraged its robotics process automation (RPA) COE to deploy 256 robots throughout its controller organization. The company managed to bring cash application to zero errors and is now moving into cognitive computing and AI. Another has a COE for master data management across the enterprise and an RPA COE, which will soon deploy robots to manage the master data–management change review and approval process.

They empower their employees to own their careers. Companies with a future-trending talent strategy are taking advantage of the incoming millennial workforce view of “work” and leveraging it across the company. They are encouraging everyone on their teams to take charge of their own career development. Finance professionals must realize that career advancement and development won’t just “happen” to them.

While smart companies will offer career development programs, it will be up to finance professionals to pursue opportunities, advocate for rotations inside and outside of finance, seek lateral moves, request to be on new projects to build their personal portfolio, and demand time with their leaders. In the words of Heidi Stock, VP of Talent Management at Bosch: “We must create a framework and culture which engages associates to shape their own individual development according to their aptitudes and interests.”

Conclusion

Digital transformation is behind the top three factors that are driving the change in the talent profile for finance in the next two to three years, according to The Hackett Group’s research (see chart below). It will decrease the emphasis on repetitive work; it will increase the emphasis on value-add work; and it will force an adjustment to technologies like robotics, AI, and the cloud. 

Finance professionals are only beginning to ask the questions about what it all means to their skill development and career prospects. They’re not going to be replaced by machines. But many of their traditional activities will be. Hence, it’s incumbent upon finance workers to take charge of their own careers, take advantage of the new programs their companies offer, learn to speak the language of technology, and hone their uniquely human skills of translation, partnering, communication, and negotiation.

Read People Are The Engine That Drives Finance Transformation: Part 1.


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).

Black Box Or White Box? Machine Learning For Finance And Risk Processes

Birgit Starmanns

Consider two different approaches to logic and system-driven decisions, commonly known as a white box and a black box approach.

With a white box approach, a term that comes from software testing, the logic and steps are known and can be traced. The logic may be mapped in flowcharts, rules, or code; most importantly, the logic and the steps of a program are very clear.

Enter the black box approach. In this case, the inner workings are not known, for example, to a software tester who sees only the inputs and the outputs. In terms of automation, this approach is also relevant when a process is too complicated to allow rules to be defined. Instead, the internal mechanism is hidden; and the subject here is that an artificial intelligence system makes specific inferences.

By extension, the argument can be made that this is similar to the way that humans learn. We make decisions every day, in general without consulting a list of rules one by one; we have learned through experience. And additional experiences influence the decisions that we make in the future.

Now let’s apply this to business systems, specifically finance systems. First, let’s look at different types of automation. As finance organizations undergo digital transformation, the need to become more efficient through automation is key, to allow finance departments to reduce errors, and to improve their speed of processing and the financial close. Such efficiencies also allow finance teams to transform their own organizations, to focus on more strategic tasks instead of handling a myriad of exceptions on a manual basis.

Types of automation

Let’s first take a look at the different types of automation, and why machine learning is different from other types of logic.

  • Rules engine. In this scenario, business users define specific rules. Sometimes these are set up in configuration; sometimes they need to be coded by an IT department. These rules are then executed on a periodic basis, most often during a period-end close. However, rules engines often become less effective over time, because they are rarely revisited. A company may expand into a new customer base for which different rules are relevant. If the rules are not adjusted – and they rarely are – finance departments must manually deal with more and more exceptions, especially as transaction volumes grow.
  • Robotic process automation. Robotics is essentially automating a manual task in a consistent way, similar to writing a script. Examples of robotic processes could be loading data into a system, where the same fields are populated, often from an uploaded file. It can also be thought of as writing a macro in Excel to execute a certain set of tasks – tasks that never change, such as manipulating data or generating a graph.
  • Machine learning. Machine learning can identify patterns in knowledge-intensive processes, without explicitly defining the patterns by rules or macros. The machine learning engine learns from historical transactions during an initial training period. It then continues to learn as finance teams make decisions based on exceptions; think of this as continuing education. Therefore, as an organization defines new business models, and additional exceptions are generated, the actions taken by finance teams on those exceptions allow the machine learning engine to incorporate these decisions into its learning. Since machine learning is based on an algorithm, it does not actually generate rules, but continues to learn – yes, a bit of a black box approach, and again, similar to the way that humans learn.

Finance applications that leverage machine learning

With the effectiveness of machine learning as part of SAP Leonardo, finance and risk applications are already leveraging machine learning in several scenarios, and the number continues to grow. These include solutions supporting:

  • Cash application. A cloud solution that learns from historical transactions of applying customer payments to invoices for open accounts receivable items. Based on the preferred tolerance level, cash can be applied automatically, leaving finance teams free to deal only with the most complex exceptions.
  • Intelligent goods receipt/invoice receipt reconciliation. A cloud application that learns from historical data and decisions of the finance team in handling exceptions to propose decisions on clearing differences.
  • Business integrity screening. A solution that employs a hybrid set of rules plus predictive analysis. Predictive analysis leverages machine learning to scale across thousands of predictive models to find patterns related to fraud, compliance failures, and other exceptions, thus reducing related financial losses.

Learn more

If you are attending SAPPHIRE NOW and ASUG Annual Conference, visit these sessions for additional information and interactions:

For additional information, you can also visit these sites:

And read this blog: The Evolution of Modern Receivables Management with Machine Learning

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


Birgit Starmanns

About Birgit Starmanns

Birgit Starmanns is a senior director in the Global Center of Excellence for Finance and Risk at SAP. She is focused on the go-to-market for new solutions, and the business benefits they can bring to organizations, such as cloud solutions for finance and applications based on SAP Leonardo technologies such as machine learning. Birgit has over 27 years of experience across solution marketing, solution management, and strategic customer communities. Prior to SAP, she was a principal in management consulting organizations, including Price Waterhouse and several boutique firms. Birgit holds a BA and MBA from the College of William and Mary. She is the author of many articles for the Financials Expert, the coauthor of the SAP Press book Accelerated Financial Closing with SAP, and the SAP Labs guidebook Product Costing Scenarios Made Easy. In addition, she is a regular presenter at various SAP events.

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.