Digital Identity – All About That Choice

Brian Lee-Archer

“All About That Bass” is the debut single by American singer and songwriter Meghan Trainor. It was released by Epic Records on June 30, 2014. One interpretation of the song title and lyrics is a callout to embrace inner beauty and to promote a positive body image and self-acceptance. To put it simply, people should be free to choose how they look and others should respect that choice.

People make life choices and there are risks involved – eat this, don’t eat that, avoid this, do more of that. It is all part of the rich tapestry of life. Public policymakers have an obligation to inform people of potential risks and to reflect attitudes of the broader community through legislation to prevent or sanction behaviours that can cause harm.

In a free society, however, people have the opportunity to exercise a wide range of life choices where society is prepared to accept some risk. Therefore, people have the opportunity to make decisions that may inadvertently lead to negative outcomes. In the public policy discourse, these consequences are weighed up against the overall positive outcomes achieved through allowing people to have choice.

It’s a pity we can’t adopt the same attitude towards digital identity; too often it gets caught up in an all-or-nothing debate. One of the benefits of the digital revolution is the capability to move away from one-size-fits-all business models. Services, including government services, can now be tailored based on the needs, wants, and capabilities of the person who will consume those services. Instead of the service provider prescribing the mode of delivery, the consumer can exercise choice. But in exercising choice, there is a different risk profile for each option. People need to be informed of the risks and their choices should then be respected.

My colleague, Kathleen O’Brien, global industry principal for public sector at SAP Hybris, wrote in a recent op-ed published in the Australian:

As we continue our journey into the data-driven and digital world in which we live, I encourage Australians to not be unnerved by the Government’s efforts to adopt a digital-first approach. There is much to gain from the changes, and embracing these types of digital platforms is essential in positioning Australia as a leader in the global economy, which is fast moving to digital services.

The concern is that as governments take steps to provide much-needed digital identity infrastructure, there will be a chorus of opposition rolling out the traditional arguments of privacy, data protection, trust in government, Big Brother,etc. These are risks which the public needs to be informed about, but they should not be used as a one-size-fits-all barrier to a digital identity system for those people who want to voluntarily opt-in and exercise choice.

There are daily reports and warnings of cybercrime, data breaches, and hacking – yet the public’s appetite to engage in digital commerce and services using digital credentials to identify themselves, such as fingerprints for their smartphones and tablets, continues to grow. It is clear a sizable proportion of the population is ready to access digital services through a digital identity. True, their appreciation of the risks involved may not all be the same and in some cases may be naïve, but this is no different to how people assess risk for a range of lifestyle factors. Should the opponents of a digital identity system be allowed to deny the benefits of same for those who want it and who are prepared to accept the risks involved?

The world has moved on since Orwell’s 1984. A key feature of the digital revolution is its empowering nature for individuals where choice is king and one-size-fits-all service delivery models can be sent to the museum. It is a valid choice to stay out of the digital space and live in a world of paper documents where your personal information is kept well out of harm’s way. Policymakers and the community need to respect the choices people make and legislate, where appropriate, to provide protections that enable, not restrict, people’s choices.

Policymakers have shown they can be adept at setting laws and policies that enable choice while allowing people to bear a risk burden that comes with the choices they make – the aim is to do the same with a digital identity. It’s all about that choice.

As for the koala picture— what is its connection to digital identity? Koalas are the only other animal that, like humans, have individual fingerprints.  Their fingerprints, although distinguishable, appear similar to humans.  So you might need to be careful about lending a koala your smartphone.

To find out more about the SAP Institute for Digital Government visit www.sap.com/sidg, follow us on Twitter @sapsidg and email us at digitalgovernment@sap.com. 


Brian Lee-Archer

About Brian Lee-Archer

Brian Lee-Archer is director of the SAP Institute for Digital Government Global (SIDG). Launched in 2015, SIDG is a global think tank that aims to create value for government by leveraging digital capability to meet the needs of citizens and consumers of government services. In collaboration with government agencies, universities and partner organizations, SIDG facilitates innovation through digital technology for deeper policy insight and improved service delivery.

Blockchain To Blockchains: Broad Adoption Enters The Realm Of The Possible, Part 4

Darshini Dalal

Eric Piscini is co-author of this blog.

Part 4 in the “Deloitte Blockchain Adoption” series

Blockchain technology and its derivatives are continuing to mature, but a number of enabling conditions need to be addressed for its mainstream potential to be realized around the world. Deloitte leaders across 10 global regions see varying levels of certainty around the anticipated impact that the technology could have on financial services, manufacturing, supply chain, government, and other applications. While there are pockets of innovation in places such as Asia Pacific, Northern Europe, and Africa, many countries in Europe and Latin America are taking it slow, awaiting more standardization and regulation.

The general expected timeframe for adoption is two to five years, with some notable exceptions. Most regions have seen an uptick in proof-of-concept (PoC) and pilot activity, mostly by financial institutions working with blockchain startups. A few countries in Africa and Northern Europe are exploring national digital currencies and blockchain-based online payment platforms. In Asia Pacific, several countries are setting up blockchains to facilitate cross-border payments.

The Middle East, while bullish on blockchain’s potential—Dubai has announced its intention to be the first blockchain-powered government by 2020, for example—finds itself in the very early phases of adoption; widespread adoption is expected to take up to five years in the region.

In most regions, the main barrier to adoption is public skepticism as well as concerns about regulation. However, as consortiums, governments, and organizations continue to develop use cases for smart contracts, and the public becomes more educated on potential benefits, viable blockchain applications should continue to evolve around the world.

Where do you start?

Though some pioneering organizations may be preparing to take their blockchain use cases and PoCs into production, no doubt many are less far down the adoption path. To begin exploring blockchain’s commercialization potential in your organization, consider taking the following foundational steps:

  • Determine if your company actually needs what blockchain offers. There is a common misconception in the marketplace that blockchain can solve any number of organizational challenges. In reality, it can be a powerful tool for only certain use cases. As you chart a path toward commercialization, it’s important to understand the extent to which blockchain can support your strategic goals and drive real value.
  • Put your money on a winning horse. Examine the blockchain uses cases you currently have in development. Chances are there are one or two designed to satisfy your curiosity and sense of adventure. Deep-six those. On the path to blockchain commercialization, focusing on use cases that have disruptive potential or those aligned tightly with strategic objectives can help build support among stakeholders and partners and demonstrate real commercialization potential.
  • Identify your minimum viable ecosystem. Who are the market players and business partners you need to make your commercialization strategy work? Some will be essential to the product development lifecycle; others will play critical roles in the transition from experimentation to commercialization. Together, these individuals comprise your minimum viable ecosystem.
  • Become a stickler for consortium rules. Blockchain ecosystems typically involve multiple parties in an industry working together in a consortium to support and leverage a blockchain platform. To work effectively, consortia need all participants to have clearly defined roles and responsibilities. Without detailed operating and governance models that address liability, participant responsibilities, and the process for joining and leaving the consortium, it can become more difficult—if not impossible—to make subsequent group decisions about technology, strategy, and ongoing operations.
  • Start thinking about talent—now. To maximize returns on blockchain investments, organizations will likely need qualified, experienced IT talent who can manage blockchain functionality, implement updates, and support participants. Yet as interest in blockchain grows, organizations looking to implement blockchain solutions may find it increasingly challenging to recruit qualified IT professionals. In this tight labor market, some CIOs are relying on technology partners and third-party vendors that have a working knowledge of their clients’ internal ecosystems to manage blockchain platforms. While external support may help meet immediate talent needs and contribute to long-term blockchain success, internal blockchain talent—individuals who accrue valuable system knowledge over time and remain with an organization after external talent has moved on to the next project—can be critical for maintaining continuity and sustainability. CIOs should consider training and developing internal talent while, at the same time, leveraging external talent on an as-needed basis.

Bottom line

With the initial hype surrounding blockchain beginning to wane, more companies are developing solid use cases and exploring opportunities for blockchain commercialization. Indeed, a few early adopters are even pushing PoCs into full production. Though a lack of standardization in technology and skills may present short-term challenges, expect broader adoption of blockchain to advance steadily in the coming years as companies push beyond these obstacles and work toward integrating and coordinating multiple blockchains within a single value chain.

Contact Darshini Dalal at ddalal@deloitte.com

www.deloitte.com/SAP

SAP@deloitte.com

@DeloitteSAP

This article originally appeared on Deloitte Insights and is republished by permission.


Darshini Dalal

About Darshini Dalal

Darshini Dalal, a technology strategist with Deloitte Consulting LLP, has deep implementation experience with complex large-scale technology transformations. She leads Deloitte's U.S. Blockchain Lab, and focuses on creating immersive experiences to help clients understand not only the applications but also implications of blockchain technology across a variety of business issues that plague today’s transaction fabrics. Darshini helps clients define their vision statement and translate this vision to reality by designing the next generation of systems and platforms.

Three Ways Digital Transformation Is Disrupting The Metals Industry

Jennifer Scholze

The metals industry is at a crossroads. It faces decreasing global demand, trade flow disruptions, widening workforce skill gaps, and declining resource quality. These challenges have hurt profits and reduced capital investments. The metals industry is ripe for change – and digital transformation is leading the way.

Stefan Koch, global lead for metals in the mill products industry business unit at SAP, recently spoke about the future of the metals industry on the S.M.A.C. Talk Technology Podcast. Koch addressed the three major ways digitization will change the industry. Machine learning will simplify production processes and streamline operations. Virtual reality (VR) will enable virtual plant operations, creating new business models. Blockchain will enable verified material tracking for purchases like green (recycled) steel. Together, these technologies can disrupt everything from extraction to production to sales.

1. Machine learning simplifies production processes, predicts quality outcomes

“Smart machines” are not a new addition to the metals industry. The industry already relies on sensor data to monitor machine performance and maximize uptime. For most companies, however, that’s the current extent of this data utility.

“It’s still very often that you see this island of information,” says Stefan Koch on the S.M.A.C. Talk Technology Podcast. “Somebody thinks of production. Another one thinks of, “Oh yeah, that’s my customers, that’s my sales.” In the future, everything will need to go together and work together in an integrated way.”

Machine learning will allow companies to do more with their data, optimizing everything from materials sourcing to process adjustments. For example, a company could link systems across multiple operations and operators. This company could then use machine learning to either eliminate or automate redundant processes like invoicing.

Koch predicts that machine learning will also enable more advanced metal production capabilities that are cost-effective and high-value for the end customer. Presently, identical production processes may still yield slightly different finished products. These differences are due to naturally occurring material variances. Machine learning will allow companies to “look into the future” and predict quality outcomes down to the slightest variation. Producers could then pre-assign products to specific customers, delivering greater value and increasing customer satisfaction.

2. Virtual reality enables remote plant operations and value chain control

Will metal companies of the future still own physical deposits? Perhaps not, says Koch. On the S.M.A.C. Talk Technology Podcast, Koch notes that some metal companies are already moving away from asset ownership. These companies are “contracting production, resources, logistics, and materials” in a bid to control the value chain.

Consider, for example, a company that shares tasks with suppliers in other countries. This company could use virtual reality contacts to enable repair and control. The company could also use virtual reality to exchange or integrate data, boosting collaboration across the value chain.

Koch predicts that virtual reality will play an important role in streamlining remote plant operation. “These are concepts we see already picking up.”

3. Blockchain guarantees supply chain validity and authenticity

A blockchain is a tamper-proof distributed ledger that maintains a historical record of all data. Since this record is independent of a central authority, it is inherently resilient. Algorithms enable continuous verification and validity calibration. Data can be signed, timestamped, and immutably recorded in the blockchain. Blockchain can then provide essential transaction validation and purity verification, guaranteeing authenticity.

Koch predicts the metal industry will use blockchain to “provide faster and more rapid ways to authenticate materials.” In the recycling industry, for example, not all parties involved communicate with one another every day. The lack of a closed loop supply chain creates authentication challenges. In fact, Koch characterizes the current metal recycling supply chain as “a pretty random list of partners who interact on a long timeframe.” Blockchain solves this challenge by providing an immutable authenticity guarantee at each step.

Why the future of metals depends on digital transformation

Digitization is more than using predictive maintenance to maximize machine uptime. It’s about disrupting outdated processes and creating new business models.

The World Economic Forum predicts that, by 2025, digital transformation will create more than $425 billion of value for the mining and metals industry. Companies that embrace digital transformation will be best positioned to capitalize on this value creation.

To learn more about how digital transformation is disrupting the metals industry, listen to the S.M.A.C. Talk Technology Podcast with Stefan Koch. Learn how to bring new technologies and services together to power digital transformation by downloading The IoT Imperative for Energy and Natural Resource Companies.


Jennifer Scholze

About Jennifer Scholze

Jennifer Scholze is the Global Lead for Industry Marketing for the Mill Products and Mining Industries at SAP. She has over 20 years of technology marketing, communications and venture capital experience and lives in the Boston area with her husband and two children.

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.