New Technologies And Opportunities For Professional Services CFOs

Marcus Fischer

The financial structure of a professional services company is organized from the top down, making the CFO an essential piece of the transformation puzzle. Thanks to digital transformation, the very job of the CFO is constantly evolving. Disruptive technology and shifting business strategies require the CFO to stay informed and agile because selecting the right technology at the right time enables greater agility, simplified finance processes, and adoption of new business models. This can make the difference between staying competitive or getting left behind. According to an article in MIT Sloan Management Review:

“Companies with 50% or more of their revenues from digital ecosystems achieve 32% higher revenue growth and 27% higher profit margins.” *

The financial challenges the professional services industry faces are unique: intangible assets such as people and intellectual property, profit margins that need to be tracked carefully, the necessity of visibility into utilization rates and projects, continuous reduction of fixed overheads, and compliance with audit and regulatory standards. The tools it needs to become more flexible are changing rapidly and include cloud platforms, blockchain, Big Data, and predictive analytics. These technologies can significantly transform financial processes – and subsequently the work of the CFO.

Challenges to modern CFOs

First, let’s take a deeper look at why digital transformation is disrupting the traditional professional services business models. The traditional model of billing consultants on a time and materials basis is being challenged by customers who expect engagements to be tied to specific business outcomes and by leaner new entrants to market with digital business models. Digitalization has enabled digital service delivery, which drives costs down and enables fees to be charged on a usage basis or subscription basis.

These developments aren’t the only disruptor; shifting economic centers and digitally enabled new entrants to the market are fueling disruption as well. And in the information age, clients have higher expectations and instant access to expert knowledge online, while firms face more and tougher competition – spurring the need to differentiate themselves more clearly and provide higher value-added services.

The automation provided by digital technologies poses a particular threat to the professional services industry. Accounting professionals, for example, can be replaced to a degree with machine learning and algorithms that automate accounting processes and technology, allowing clients to take over certain accounting services such as filing taxes.

Despite the impact that digital transformation might have on some professional services sectors, it presents a major opportunity for growth and innovation for the industry as a whole. In a recent study by SAP and the Oxford Economic Unit, 96% of industry leaders see digital transformation as a core business goal, compared to 61% of all other companies. Similarly, 94% of leaders are investing in next-generation technologies such as Big Data, the Internet of Things, and machine learning, while only 60% of all other companies are making such investments. The trend is clear: to invest in digital change is to be a leader in your industry.

For CFOs of professional services firms, digital innovation can have a significant impact on the bottom line. The same study from SAP and the Oxford Economic Unit found that 85% of leaders say that transformation efforts have increased their market share, and that they expect 23% higher revenue growth in the next two years. A full 80% of leaders say transformation has increased their profitability.

The tools CFOs need to create more responsive organizations

What are these digital technologies that leaders are investing in, and how can they help professional services firms remain competitive?

Digital core

With intelligent enterprise resource planning (ERP) software, CFOs can create a digital core to simplify finance processes. A digital core can help reinvent business models and drive new revenues by instantly connecting people, devices, and business networks. For example, an intelligent ERP can help streamline accounts payable, invoice management, cash and liquidity management, and financial planning and analysis. You can eliminate IT complexity and create a flow of real-time information with a digital core that helps you run a live, digital business.

Predictive analytics

To become a digital leader, it’s important to look to the future, not the past. Predictive analytics allow CFOs to do just that, forecasting results and revenue predictions based on customer-experience profiles and current demand, instead of comparing to previous years as most companies still do today.

With high-performance analytics, it is possible for a CFO to review, in a single dashboard, revenue and margin by country and period, utilization rates per country, revenue and margin by practice by period, headcount by practice, and utilization by practice. Armed with this detailed financial information, the CFO can make a greater contribution to strategic decisions.

Big Data and cloud platforms

Instead of using the traditional consulting business model for audits, CFOs can also rely on Big Data. The amount of data at the CFO’s fingertips is exponentially greater than in years past, but many companies don’t know how to exploit it. By using a cloud platform, CFOs gain easier access to Big Data for audits and can better analyze it for business insights.

Blockchain

Blockchain can be used to improve information traceability, enabling transparency while improving auditability and regulatory compliance. Blockchain technology allows companies to share important or confidential documents securely – without an intermediary. The information can be shared quickly, in a format that cannot be changed. This method has the potential to help ensure the privacy and security of shared information, which is crucial for professional services companies and their clients.

New customer experiences are constrained by the ability of companies to support microtransaction-based revenue models tied to consumption. Blockchain provides a secure approach to enabling this automation.

Only the firms that are embracing technology are expected to survive and thrive in the years to come. If you want to stay ahead of the competition and continue to move forward, the time to look at Big Data, predictive analytics, blockchain, and artificial intelligence for your financial processes is now.

For more insight on digital leaders, check out the SAP Center for Business Insight report, conducted in collaboration with Oxford Economics, “SAP Digital Transformation Executive Study: 4 Ways Leaders Set Themselves Apart.”

*Source: Peter Weill and Stephanie L. Woerner, “Thriving in An Increasingly Digital Ecosystem,” MIT Sloan Management Review, Magazine Summer 2015.

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


Cognitive Technologies Help Media Companies Build Consumer Loyalty

Catherine Lynch

Media companies need to provide unique, personalized content, driven by deep insights into individual consumer preferences, due to the growing popularity of over-the-top (OTT) streaming services. In the past, media companies were not in direct contact with consumers and interacted in a mass marketing fashion. Now the business model is changing to direct to consumer, and media companies need to adapt to survive and thrive.

Consumers are willing to pay for the right content

In the music world, interactive personalized streaming of music (Spotify, Apple Music, Deezer…) is overtaking physical downloads of music from a revenue perspective, and it is even rumored that Apple will stop downloads from iTunes next year. In 2017 streaming accounted for almost two-thirds of music industry revenue. By the end of this year, over half (57%) of Spotify’s 157 million worldwide active users will be paying for subscriptions.

Over 30% of U.S. households now subscribe to more than one OTT service, according to Parks Associates. The OTT video service industry is expected to reach $30 billion by 2020.

Using analytics and identity management to suggest relevant content to consumers

To understand what a viewer will like in six months, media companies must manage the complexity of multiple touchpoints, both physical and digital. It is also essential to build consumer trust and loyalty if you are seeking personal information from a viewer to drive that personalized experience. Algorithms underpinned by cognitive technologies help determine which content might interest a subscriber. Identity management software enables the buildup of a profile of preferences and leads to greater personalization and consumer loyalty.

Media companies can also analyze social activity information about a viewer to further increase levels of personalization, and it makes sense to provide that viewer with a personalized subscription offer and “up-sell” based on that person’s video consumption and social media activity.

Machine learning and blockchain help with personalized ads and content monetization

Software such as Pippa developed a technology that allows podcasters to insert personalized ads to a podcast. It is planning to use AI to perform deep audio search and personalize ads based on a podcast’s content. With a new avenue for monetization of podcasts, this technology could boost podcasting and make it much more profitable. Jaak uses blockchain technology to identify the usage and rights to song streams. It enables apps and platforms to identify who is streaming a song and when identifying the multiple rights holders and assigning corresponding payments.

By 2020, Gartner predicts that artificial intelligence (AI) bots, rather than humans, will manage 85 percent of customer interactions. There will be more than 82 million U.S. millennial digital video customers. As media companies grapple with the challenge of getting personalized content to the consumer at the right time, companies that proactively invest in advanced analytics, machine learning, and blockchain will gain a critical first-mover advantage.

To learn more, read our Reimagining Media in the Digital Age blog series.

Are you attending SAPPHIRE? If so, join us at the SAP Industries Experience Area during the event and check out the Media sessions on the agenda builder.


Catherine Lynch

About Catherine Lynch

Catherine Lynch is a Senior Director of Industry Cloud Marketing at SAP. She is a content marketing specialist with a particular focus on the professional services and media industries globally. Catherine has a wide international experience of working with enterprise application vendors in global roles, creating thought leadership and is a social media practitioner.

A Look Beyond The Basics Of Cloud Database Services: What’s Next? Part 2

Ken Tsai

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

When Amazon Web Services (AWS) began, it started by providing massive computing resources from an infrastructure-as-a-service (IaaS) standpoint. As other vendors followed, the first approach (and easiest thing to do) was to take existing software and put it into the cloud. But very quickly, database management system (DBMS) providers realized this was an opportunity to rethink the architecture of their products. Database systems can run faster, easier, and cheaper if you re-architect the software for cloud operation – database-as-a-service (DBaaS) – versus on-premises.

DBaaS opportunities and emerging trends

It is obvious that DBaaS adoption until now has come only from migrating an existing DBMS workload. Yes, DB in the cloud with portabilities for popular open source or best-of-breed options, such MySQL, Cassandra, and Mongo, have been no-brainers for both users and public cloud providers. I call this the GEN-1 cloud DB. I eagerly anticipate a much faster convergence of the multitude of data processing techniques within a re-factored DBaaS, such as a DBaaS that also has query and process data stored in the cloud object store.

I look forward to the development of an improved unified data computing framework that expands directly from DBaaS to query data in shared storage, such as cloud object store. This is already happening in some DB-as-a-service offers, which I refer to as GEN-2 cloud DB. This generation promises to strike the right balance between value, performance, and ease of access.

The promise of hybrid cloud environments and the apps we have yet to invent

One of the hypotheses I’ve been testing is the possibility of a new class of application scenarios that can naturally take advantage of the distributed nature of data and workload between on-premises and cloud. I see ample evidence in both enterprise and consumer companies dealing with distributed data everywhere.

I see first-hand examples of many of these application scenarios, ranging from marketing automation, service and support, customer service and sales, or even in a business-to-business or a business-to-network collaboration.

For example, Google Photos was originally built to automatically sync our photos to the cloud, but it rapidly evolved to use machine learning and AI to search, detect, and display a photo when you search for a keyword. It also creates a montage of photos, with background music to share as a video or a memory/timeline. This is all possible based on the understanding of where the data is and what it must be used for.

Opportunities in security, data privacy, and data anonymization

Security offers another area of opportunity for DBaaS. Increased data privacy regulations in the European Union are bringing even more pressure to bear on companies that handle consumer data. The EU’s General Data Protection Regulation (GDPR), which goes into effect May 25th, 2018, guarantees new levels of data privacy for customers by requiring companies who utilize any type of customer information to inform (explain why, where, and what their information is used for) and receive explicit consent.

This is good news for consumers, but it poses some challenges for companies who want to gain business insights from data. Innovative companies are responding by heavily investing in new technology called data anonymization. This technology enables businesses to completely anonymize the data itself so that you can’t tell where or whom the data is attributed to.

Eventually, I see that the rise of data privacy and security regulation will become global. (For further information, here’s a Webinar that dives deeper into security, data privacy, and anonymization.)

Cloud-based databases have a role to play in digital transformation

For many companies, when digital transformation becomes the goal of the C-suite, adoption of DBaaS naturally follows. Digital transformation in its simplest terms is about the digitization of assets, relationships, and engagements in the context of bringing together customers, partners, and employees. Decisions – about them and the company – must be highly data driven. To this end, databases become the critical component of facilitating the successful digital transformation of a company.

I’d like to hear what you think. What do you see as the challenges for DBaaS? What are you most excited about? Leave a comment or connect with me on LinkedIn and Twitter.

Learn more

To learn how your organization can benefit from a hosted data management platform in multi-clouds and cloud/on-premises deployments, visit sap.com/HANA.

If you’re missing the live launch at SAPPHIRE NOW, register for our webcast June 21, 11 a.m. EDT/5 p.m. CET to get the latest information.

This article originally appeared on Hackernoon.com and is republished by permission. Special thanks to David Fletcher and CloudTweaks.com for the use of their cartoons. You can follow them on Twitter at @CloudTweaks


Ken Tsai

About Ken Tsai

Ken Tsai is the global VP and head of database and data management at SAP, and leads the global product marketing efforts for SAP’s flagship SAP HANA platform and the portfolio of SAP data management solutions. Ken has more than 20 years of experiences in the IT industry, responsible for application development, services, presales, business development, and marketing. Ken is a graduate of the University of California, Berkeley.

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.