Digital Transformation: Disrupting With Diversified Business Models

Paul Lewis

As a previous CFO I work with used to say frequently, “It’s all about cash flow.” Good ideas and good businesses are about making money and spending just enough to grow that money incrementally and predictably over time. “The faster we achieve positive cash flow on any particular project, the more investment money will be available to innovate again,” was a mantra I heard time and time again.

Getting to financial steady-state for new organizations and/or maintaining incremental positive growth for mainstay companies is becoming more difficult, however, because:

  • Competition from Internet/mobile-born startups are quick to innovate and can change priorities on a dime
  • Changing consumption demands from a changing consumer market will make or break product success far faster than ever
  • Explosion of massive automation techniques, like robotics and artificial intelligence, is reducing the human effect of customer service
  • Globalization and ubiquitous information sharing are creating real-time service comparison and global rating systems
  • Startup adjacent markets like bitcoin are competing with centuries-old financial markets

These “disrupters” could be summarized into one simple word: choice. Your clients want choice in products, service, payment method, company, length of engagement, etc. They are perfectly happy to replace you if they are not satisfied.

Consumers are also choosing to break up long-term and broad business relationships to create several short-term, diverse relationships. Instead of being loyal to a single bank consuming all the retail, investment, and insurance offerings available, they would rather spread their wealth across many institutions, and they will purposely and quickly move to another institution if you’re not keeping up with your side of the bargain on customer service.

Collectively, these digital disrupters are chipping away potential growth, especially if executives are relying on the traditional tools in their tool belt: Introducing new products at the same rate and incrementally improving customer service, prices, sales, and promotions.

The best way to compete with disruption? DISRUPTION!

Competing against these disrupters requires a new disruptive business strategy – digital transformation – which is largely divided into these three categories:

  • Operations and processes: Take a ground-up re-evaluation of the services you deliver to dramatically change the time to market delivery of your products (from months to hours)
  • Customer experience: Purposely identify and understand new customer behaviors and buying expectations with a consumer mindset of replaceability
  • New business models: Shift from “sell product” to “sell service” to “sell usage” to “sell outcome” to “sell network”

I’ve already written about operations and processes and customer experiences in the last couple of months, so let’s dig a little deeper into new business models.

A quick reminder in the last few minutes: The customer expectation is choice, as evidenced by competitive pressures from digital disrupters.

Many of the digital disrupters, including your digital competition, likely have a significantly different business model. We could go in depth in terms of the various characteristics of your business model, including value proposition, customer segments, partner relationships, key assets and activities, etc., which would certainly show major differences:

  • Major hotel chains have trillions of dollars worth of property, while online room rental capabilities have none
  • Big-box retailers cater to a diverse set of customer demographics, while drone-based delivery retailers focus on urbanites
  • Large manufacturers require hundreds of partners to deliver an array of complex machines, while a niche manufacturer only needs a 3D printer and time on its hands
  • Major technology companies rely on a solid brand for continued patronage, but new entrants need some samples that fit into the trunks of their car

We could go in depth on each of these characteristics, and they do need to be addressed by the executives, but let’s focus even more on the financial models of your offerings relative to customer choice and cash flow.

For the most part, your financial model (how you earn revenue and how you spend it) largely fits into this generic description:

  • Sell product or service; make money – spend money to make product or deliver service – invest profit to make new product or service
    • It’s tried and true, and you can create and deliver a variety of products and services that fit this model. The more profit made, the faster debt is paid, the happier investors become.

But what happens when your customers are looking for choice and find the exact same product or service available from your competition in dramatically different financial models, including ones that suit their particular financial needs much better? Let’s explore those other models:

  • Sell product – make money THEN sell service – make money
    • Not a huge difference from the generic model, but it does create potential for new and recurring revenue. Adding the ability to sell add-on post-sale services not only creates new revenue, but also a level of “stickiness” with the customer due to the ongoing interaction. Instead of buying once and hoping they come back for a newer model later, the continued interaction keeps the brand front and center. The negative of course, is that a poor or declining customer experience will have a dramatically negative effect. It’s almost impossible to bring back a customer with a poor experience.
  • Sell product AS a service – make money over time
    • This model is the big shift from CAPEX to OPEX for all participants. For a customer, it’s replacing the financial burden of an upfront cash outlay with ongoing expenses over a period of time (a contractual term or when they stop the service). For the company, it means changing the spending model by taking on the upfront risk of product or service creation and availability, with the potential return of more profit per product over time. This model is preferable for customers looking to manage a predictable cash flow.
  • Sell product AS a service – make money based on USAGE
    • While still an OPEX model, the difference in that the burden of profitability is entirely on the shoulders of the company to create enough customers with enough usage over time to compensate for the upfront initial investment in the product creation and expenses over its lifespan. The potential return, however, is a far higher potential of profit if usage becomes popular. This model has created many cash cows. For customers, the expense is directly controllable and they can spend as little or as much as they need at their discretion.
  • Sell product AS a service – make money based on OUTCOME
    • As an extension to the usage model, the outcome model helps balance the risk between the seller and the consumer for the cost of the product. The burden of the product investment is still with the company, and the usage over time will still dictate the amount of potential profit, but that risk is now reduced with each customer interaction by jointly taking on the risk for the ongoing or end price. This is the model of “everybody roll up your sleeves” to create an average transaction price that’s lower for the consumer.
  • Sell platform services – make money from all participants:
    • This is a dramatic shift from creating and selling products to creating a network of buyers and sellers for a particular set of products or services. From the consumer perspective, and even your brand recognition as a whole, you may be seen as a provider, but this model is only about making offerings available from a variety of different sellers and earning revenue transactionally as part of the buying experience. The burden of product investment remains with the sellers. The burden of creating a marketplace (both the platform and relationships with all parties) becomes exclusively yours. The time and investment required to create these platforms will be a significant burden, and the potential of failure is significantly high. However, once the network is thriving, net new revenue can be earned by creating new and innovative value for each of the participants in the network and creating logarithmic profits by the simple organic growth of the network alone. The value for the customer, of course, is creating the ultimate venue for choice.

Just to be clear: I am not advocating a shift or a move to a new business or financial model for your existing offerings. And even if you strategically decide a new model would be valuable, I am not suggesting the various models described represent maturity or evolution. My recommendation is to evaluate your current growth against your competitors’ and your customers’ desires in order to create diversity in your business financial models to offer choice to your various customer segments. Ultimately, it’s choice that will be the winning digital transformation business strategy.

Everyone has a perspective and a point of view. Spend time reading, forming an opinion, and talking about it. Being right isn’t important. If you are never wrong, you aren’t trying hard enough.

See how IT can help organizations shift to real-time operations. Read the EIU report.

This blog originally appeared on Hitachi Data Systems Community.


Paul Lewis

About Paul Lewis

Paul Lewis is the chief technology officer in Hitachi Vantara for the Americas, responsible for the leading technology trend mastery and evangelism, client executive advocacy, and external delivery of the Hitachi vision and strategy especially related to digital transformation and social innovation. Additionally, Paul contributes to field enablement of data intelligence and analytics; interprets and translates complex technology trends including cloud, mobility, governance, and information management; and represents the Americas region in the Global Technology Office, the Hitachi LTD R&D division. In his role of trusted advisor to the CIO community, Paul’s explicit goal is to ensure that clients’ problems are solved and opportunities realized. Paul can be found at his blog, on Twitter, and on LinkedIn.

Visibility 3.0: Transforming From Basic Information Sharing To Supply Chain Alignment

Sudy Bharadwaj

In the late mid-to-late ’90s, the early days of collaboration brought visibility to organizations from their supply base. This level of visibility was usually in the form of basic information, such as quantity of components needed (high tech), amount of ingredients needed (consumer goods), feedstock required (chemicals), amount of active pharmaceutical ingredients needed (life sciences). Moving from fax machines to email was the first generation of supplier visibility. Remember “e-business”? The advent of email provided transactional efficiency and some level of process automation.

e-business circa 2000

Figure 1: E-business circa 2000. Business and supplier with (a) simple lines of communication, (b) disparate business processes, and (c) limited information exchanged.

What’s next?

A McKinsey study reveals organizations that collaborate on innovation with suppliers achieve higher growth than organizations that fail to do so. Innovation with suppliers can take many forms, including migrating from the buyer’s process efficiency to overall supply chain alignment – process efficiency between buyers and their supply base. If the figure above represents basic visibility, then the figure below represents increasing levels of supply base alignment between buyer/supplier (left side), or supply chain alignment among members of a supply chain (right side). Rather than a distinct line between buyer and supplier, there is a union between the buyer and supply base.

supply chain alignment

Figure 2: Supply chain alignment is a union of business processes and information within an ecosystem.

The metaphor? Reduce the lines and integrate processes using richer data. Examples of richer data include the sharing of capacities, manufacturing information, sales data, and quality data. Naturally, some data is confidential and cannot be shared, but more sharing results in greater efficiency, typically resulting in lower costs, faster response to customers, less waste, and a variety of other improved performance metrics.

In an earlier blog post, I discussed How Manufacturing Organizations Can Regain Control Of Their Supply Chain; the concepts reviewed in this post can be applied to supply chain alignment. Examples of transforming from simple communication to more of a union in the supply chain vary by industry:

  • Semiconductor: The chip in your smartphone is usually made by contract manufacturing organizations where quality and yield information are shared among members of the ecosystem in near real-time in order to make decisions about yield or quality in response to supply and demand.
  • Life sciences: Most pharmaceutical ingredients are developed for use in multiple countries, but associated documentation and other regulatory requirements vary across jurisdictions. Aligning the production of documentation with that of ingredients can reduce approval time, thus reducing time to market. Much the same holds true for medical device supply chains.
  • Automotive: Products recalls are a natural way of life in this industry. The ability to quickly trace components, sub-components, and sub-sub-components all the way down to commodities such as polycarbonate, aluminum, and other basic materials can help to assess which end products (automobiles) require a recall and whether a specific manufacturing line needs to switch one or more suppliers.

Summary

Digital transformations require consideration of the supply base and discussions about aligning supply chains by sharing more information than ever. Think about removing lines and providing more of a union of business to improve profitability, flexibility, and agility – for all members of the ecosystem.

To learn more about how digital transformation can impact your supply chain operation, link to our value calculator and analyze the impact and generate your own customized reports.


Sudy Bharadwaj

About Sudy Bharadwaj

Sudy Bharadwaj has over 25 years experience in helping businesses transform operations in a variety of areas and industries. Sudy's current focus for SAP is helping organizations transform direct spend leveraging supply chain and sourcing competencies.

Three Reasons Discrete Manufacturers Must Integrate Digital And Physical Products

Miranda LaBate

Part 9 of the “Intelligent ERP-Driven Industries” series

Discrete manufacturers in automotive, aerospace and defense, high tech, and industrial machinery and components are facing unprecedented pressures on their ability to innovate, engage with customers and consumers, and maximize return on their assets. By 2018, nearly one-third of discrete manufacturing leaders will be disrupted by competitors that are digitally enabled, reports IDC. In the age of digital disruption and transformation, discrete manufacturers must rethink traditional business models to capitalize on new, digital opportunities. One such opportunity is the sale of digital products.

Digital products offer many benefits over physical products, including frictionless buying, immediate delivery, and no shipping or supply chain management costs. But digital products can be difficult to sell on their own. To address this challenge, companies are pairing digital products with physical ones. For discrete manufacturers, this pairing offers new business models and revenue-stream opportunities.

Valuing digital products: Using physical products to drive digital sales

What is the value of a digital product? Consumers in the B2C world have historically been slow to jump at the purchase of digital products. As Fast Company reports, it takes a companion physical product to give the digital product value. For example, consider the case of Apple’s iPod and digital music downloads. In the age of Napster and free MP3s, digital music downloads were a slow seller. This changed after Apple introduced its iPod in 2001, creating a new physical product to house these digital downloads. More than 5 billion songs were sold through Apple’s iTunes store by 2008.

Learning from Apple, discrete manufacturers can adopt a similar approach by integrating their physical and digital offerings. Digital offerings, such as remote upgrade service and preventive maintenance contracts, are a natural add-on to physical products. IDC estimates that by 2018, 60% of large manufacturers will bring in new revenue from information-based products and services with embedded intelligence driving the highest profitability levels.

Three applications for digital-physical product integration

For discrete manufacturers, integrating digital and physical products offers three key benefits:

  1. Increased aftermarket value. Selling remote monitoring and digital services is perhaps the most obvious application for digital and physical product integration. Offering upgrades, continuous service, and preventive maintenance via remote monitoring is an important new revenue stream for discrete manufacturers. For example, remote monitoring can dramatically extend the shelf life of industrial machinery used in the food and beverage industries, high-tech manufacturing, and automotive manufacturing. Typically, an industrial machine has a shelf life of 20+ years. But the rapid pace of technological change means machines constantly need to be retrofitted. Condition-monitoring sensors combined with the Internet of Things (IoT), cloud technology, and analytics would enable discrete manufacturers to offer ongoing digital service plans.
  1. Data monetization. IDC estimates that less than 10% of data is effectively used. Discrete manufacturers must treat data as a digital asset and use this data to improve user experiences, provide insight, influence decisions, and set directions. In the automotive space, discrete manufacturers can leverage usage and engagement information to effectively send content, such as software upgrades and infotainment. Like the Apple iPod/digital download model, auto manufacturers could use the physical product (the car entertainment system) to sell the digital product (the infotainment) to drivers. Automobile manufacturers can use analytic data to better understand driving patterns and preferences, location usage, and demographics. Analyzing this data will allow manufacturers to better target their digital infotainment offerings.
  1. Faster design-to-market cycles. Embedding sensors in industrial machines will generate a wealth of digital performance data that is useful not only for predictive maintenance but also for streamlining future production. Industrial machines are incredibly complex. Ideally, these machines are built following a model-based systems engineering approach that allows designs to be reused for a variety of customers. Integrating sensors into these machines will produce a stream of data that discrete manufacturers can use for future production guidelines. This includes using the data to configure new customer orders. This approach accelerates design-to-market cycles and increases customer satisfaction.

For discrete manufacturers to capitalize on new business opportunities, they need a strategic partner to support digital and physical product integration. Manufacturers need a platform that enables the seamless integration of industrial IoT with advanced analytics process to support product development.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading “Accelerating Digital Transformation in Industrial Machinery and Components.”


Miranda LaBate

About Miranda LaBate

iranda LaBate is an aspiring marketer with an affinity for technology, blogging, and social media. The 2018 graduate of Drexel University supports the Automotive Business Unit in creating excitement and awareness around disruptive technologies molding the future of the automotive industry. Connect with Miranda on Twitter or LinkedIn.

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.