From The Business Of Language To The Language Of Business: The Future Of Translation Worldwide

VR Ferose , Barney Pell and Lorien Pratt

Today, with even the smallest enterprise potentially serving a global client base, the need to communicate across languages and cultures is growing rapidly. However, cross-context communication is hard and costly. Unless great care is taken, many things can be lost in translation due to translation errors and/or differing interpretations of even correctly translated communications.

The costs of translation failures are often more than just financial. Miscommunication can lead to loss of reputation, legal exposure, physical harm, or even industrial disasters. For this reason, clear, accurate and effective communication – between cultures, languages, disciplines, and industries – is an increasing priority.

In response, many companies spend significant resources to ensure communication within their networks of agents, partners, customers, and government agencies. This need to accurately share information between and among diverse trading partners has evolved into the business function called localization.

Localization is defined as technologies and processes that adapt products and services for use in and by specific countries, regions, or groups. It is often a highly complex and expensive process that includes translating text and audio material, modifying documents and software to reflect localized conventions (such as how decimal points and dates are represented), and continually analyzing and incorporating into products and services the regulatory, compliance, and tax requirements of specific countries, states, and cities.

This article focuses on one specific function within the broader localization picture: translation. Across businesses worldwide, there is an increasing demand for translation, driven by:

  • an increase in demand for non-English languages,
  • increase in products and services from non-English countries reaching foreign markets,
  • an in vertical-specific translation use cases, and
  • a reduction of translation, driven by improvements in AI technology and the rise of cloud-based translation platforms. These lower costs support a growing “long tail” of businesses that can profitably offer services in multiple languages.

This article discusses this new future, in which there is an increased demand and opportunity for translation. We also cover nuances that your organization needs to keep in mind to succeed in this changing market.

Translation services at a turning point

Annual enterprise spending on translation services is expected to grow to US$45 billion by 2020, primarily driven by increasing globalization and an increasing amount of text being generated worldwide.

This growth is also being stimulated by new technology. Many organizations are using artificial intelligence (AI) in the form of machine translation (MT) to reduce the costs of translation. AI-enabled automated translation platforms like Google Translate, Microsoft Translator, and the recently released Amazon Translate have in the last 24 months taken a great leap forward in accuracy. This is for two reasons: one, they build on recent breakthrough improvements in neural machine translation (NMT) algorithms, and, two, they have access to a much larger amount of language data from search engines, social networks, and e-commerce sites.

For less-demanding consumer (B2c) use cases, such as translating a web site for a casual browser, the accuracy of these fully-automated AI-based systems has recently become “good enough” for a large number of use cases. Typically these translations are offered for free and supported by ads, so the users are happy with whatever quality they can get, and the consequences of errors are low.

In contrast, the accuracy of these existing systems is not adequate for many business use cases, such as creating a user interface in a new language, translating a tax document, or creating a user manual for a product in a new language. Yet AI is also having a big impact here, where human-in-the-loop uses cases allow the AI system to do an initial translation that is then refined by a human expert. Although this isn’t driving translation pricing all the way to zero, this technology is, nonetheless, having a profound impact on the translation marketplace, which is transforming in shape as a result of these forces.

AI and the end of translation jobs?

The recent acceleration in machine translation sophistication and reliability leads some observers to speculate that machines will essentially remove the need for expensive human translation even in the enterprise market, eliminating tens of thousands of jobs in product and service localization, publishing, marketing, and myriad other fields, even as the demand for translation explodes.

However, this is a false extrapolation of current success. Although the hype around recent improvements is largely justified, the idea that machines will destroy language services as an industry and drastically reduce the need for translation and globalization teams is not.  There are a number of reasons:

  1. As described above, the bar for successful language translation in the enterprise is substantially higher than for consumer applications.
  1. Even within the enterprise, the bar is rising for these reasons:
    • More languages and dialects must be handled.
    • More specialty vertical markets, such as the law and healthcare, must be served.
    • There is an increase in the need for more specialty horizontal document types, such as for documents describing decisions, requirements, and systems.
    • Translating the functional aspects of a product (e.g. menus and documentation) is a specialized practice and one for which consumer approaches to translation do not readily apply.
  1. Current language translation technologies will not improve at the current pace unabated. The biggest recent advances have come from leveraging massive corpora of already-translated materials to learn translation models that can translate similar content in the future. Many enterprise cases are much more specific in terms of context and discipline, and also have lower volumes of already-translated data for these narrower contexts. These are technical challenges that AI algorithms are only today beginning to address, and new technology transfer ―if not also new R&D―are required to reach the next level in driving business value.
  1. The number of languages that can be profitably translated is increasing with the new lower-cost, AI-supported approach, as we describe in more detail below. Hence, even as the costs for translating higher-priority languages might come down, the volume of emerging-priority languages continues to rise. These less-translated languages have less training data, making the automation problem harder, as noted above.

Translating the long tail

The number of languages and language pairs now handled by the most advanced translation platforms represents only a small fraction of the languages spoken across even the developed world. But translating content into languages beyond the 40 or so supported by the largest language service providers (LSP) and by enterprise software vendors has, to date, been difficult or impossible to cost-justify: for most companies, the cost and time required to add just one new language to a product has been measured in the millions of dollars and years of time.

Those barriers are about to be shattered by the combination of scale efficiencies enabled by cloud-based platforms and translator productivity improvements enabled by machine translation.

While some of these long-tail language markets are growing quickly, few will ever represent enough revenue to justify the cost under the existing on-premises deployment, non-AI technology model. However, with platform economics and AI-enabled efficiencies dropping the cost, translation providers will be able to recoup the financial and time investment required to add new languages while still keeping translation prices affordable for a much larger set of customers. The economics will also allow providers to vastly increase the volume of translation they can handle, helping to maintain revenue and margins even as prices drop.

The mix of languages that need to be translated is shifting. Today, while English is the top language used on the internet, less than a third of an estimated 4 billion Internet users are English speakers.

Top languages used on the Internet

Going forward, the number of new language opportunities is substantial and represents a new market for many businesses. According to Common Sense Advisory (CSA), enterprises will need to translate content into a steadily increasing number of “niche” languages in order to reach small but fast-growing economies. Where approximately 14 languages are sufficient to reach about 75% of global Internet users today, reaching the next 20% requires adding about 40 more. By 2027, the firm estimates that enterprises will need to translate into more than 60 languages in order to reach 96% of the online population.

languages spoken on the internet

More data on this point: while English remains the primary language of international business and the Internet, a commonly cited forecast that appeared as early as 2005 projected that the next billion Internet users would not be native English speakers. Visual Capitalist traces the entire status of language usage today. World language authority Ethnologue estimates that English is a second language for about 60% of all English speakers. If you have ever learned a foreign language, you will appreciate how much easier it is to understand your native language. That translates into more effective communication and higher business value for whoever does the translation.

While no one expects every linguistic group will be covered any time soon, new patterns of trade will soon drive requirements for some unexpected languages and language pairs. China’s Belt and Road initiative to build a new Silk Road – roads, rail, bridges, sailing routes, pipelines, and trade alliances – is expected to connect 70 countries in Asia. Defaulting to English as a common language in place of local languages will become less viable as these markets are opened up.

Also consider that there are more than 20 major languages in India, written in a dozen different scripts, and estimates of over 720 dialects. Twenty-six regional languages in India are spoken by over 1 million people who don’t speak Hindi. And although it may not be obvious, some of these regional languages are more valuable than Hindi in terms of the speakers’ literacy and economic status.

Beyond adding entirely new languages, the move to a platform model will also ultimately make translating within languages – between dialects and specialist jargons – affordable, further extending the long-tail opportunity.

Altogether, this combination of today’s decreasing hegemony of English along with non-Western centric developments in global business means that there is an increasing demand for translation between many new language pairs, and this demand will last for years to come.

High-value and high-volume domain-specific translation

While these long-tail enterprise language services opportunities develop, the ability to translate and interpret domain-specific vocabularies that convey highly precise meaning is at least as important as the number of languages translated. For an example, a document may describe a medical patient, an important strategic decision, or the rationale for a new government or management policy. In a medical context, for instance, the word “protocol” has a very specific meaning referring to a set of standard steps used to treat a condition. For example, a particular chemotherapy drug may be associated with a protocol that says it should be administered at a certain dosage every two weeks. In contrast, “protocol” in a telecommunications context has an entirely different meaning referring to how data must be changed as it is exchanged between two telecommunications companies like Verizon and Orange.

In medicine, as more medical documents are digitalized, there will be an increasing global demand for fast but accurate translation for doctor-patient communication. The disparity between the availability of specialists in developed countries and in the developing world (including in refugee camps) represents one important gap for which automated translation systems can be particularly helpful.

In domain-specific contexts like these, AI systems require domain-specific language examples for training. However there is a power-versus-generality tradeoff here: It is a time-consuming process to gather medical, insurance, and other specific language information in 100 languages at once. For this reason, new AI technologies like transfer learning are promising because they allow one system to bootstrap another that is targeted for a related situation.

From vertical to horizontal domains

It is less widely recognized that every industry and many industry sectors use language in ways that, while not as specialized as in highly technical fields, differ in important ways from common usage.

Companies both within and between industries and/or segments may share those semi-specialized vocabularies. For instance, many industries share the same project management vocabulary, with well-understood meanings for words like “resource” (which usually means a person working on a task) and “dependency” (which specifies which tasks must be done before others) independent of whether the company offers insurance or healthcare.

Enterprise software vendors, especially those furthest along in the transition to delivering solutions via cloud-based platforms rather than via on-premises implementations, have access to the best and most extensive source of this semi-specialized training data – their own products and the unending stream of transactions that move across their business systems.

As with machine translation, the resulting systems will augment human expertise, not replace it. Companies will refine the resulting “horizontal domain” translation tools by identifying subtle differences in otherwise common business language usage across industries, sectors, and regions. The resulting mass-customization capabilities will allow cost-effective product development that can be leveraged across multiple companies and industries.

This can get particularly complicated when vertical- and horizontal-specific semantics overlap. For instance, a word like “deliverable” may only be completely disambiguable when both definitions are known. This need for sophisticated understanding will challenge humans and advanced AI systems for years to come.

Translating a threat into an opportunity

As if the changes in the industry wrought by AI weren’t enough, in addition, the rise of cloud-based machine translation platforms also promises to further drive down the cost of enterprise translation. Platforms consolidate functionality needed by multiple ecosystem partners in a system that is analogous to Amazon’s providing marketing functionality for its network of product suppliers.

While the two forces of AI and cloud-based platforms might appear on the surface to create an existential threat for language services providers (LSPs) and for human translators, the truth is that they will actually create a massive opportunity for those with the foresight and agility to pivot quickly into the new reality.

Lower costs will increase the number of enterprises that can consider sophisticated translation services they haven’t previously been able to justify, driving a sharp increase in demand. Rather than eliminating the need for human translators with specific language, cultural, and vertical domain expertise, cloud-based, AI-powered platforms will instead enable massive improvements in human translators’ capacity (languages, verticals, and horizontals, as above), efficiency, and accuracy.

Providers prepared to transition away from high-cost, hard-to-scale translation models and take advantage of enterprise-focused translation platforms will be able to handle more volume, continue to offer the value-added expertise that differentiates the enterprise and consumer translation markets, and more easily and quickly add new languages.

From an employment point of view, although there will be a massive shift in the work performed by a typical translator, for the reasons given above, we do not foresee a substantial decrease in the need for their services.

Conclusion

This article has explored a massive shift in the translation industry, primarily driven by machine translation (MT) technology and the shift from a premises-based to a platform model. It has examined some commonly held assumptions about how MT developments are shaping the opportunities and challenges facing language services providers and their enterprise software vendor partners, customers, and competitors.

As we have discussed, rapid machine translation improvements and the development of platforms like Amazon Translate, Google Translate, and Microsoft Translator have lowered to near-zero the cost of translation output that is good enough for many consumer and simple business-to-consumer applications. For this reason, many industry players and observers worry that those same trends will reduce the value of enterprise language services providers, independent translators, and corporate localization teams; decrease margins for translation services to unsustainable levels; and eliminate jobs.

In contrast, the continued need for domain expertise and very high accuracy, combined with machine translation and the transition to a platform-based model, actually holds great promise.

Even small enterprises now have global reach previously reserved for only the largest brands. Many must execute global marketing campaigns in multiple languages. This has been prohibitively expensive to date. But, much like Amazon enables a “long tail” of small sellers, emerging cloud translation platforms will consolidate many functions, allowing translators to remain profitable, even when they serve only niche markets.

AI tools will augment rather than replace humans at the high end of the enterprise market, increasing providers’ ability to handle vastly increased volume while simultaneously meeting the stringent requirements of highly specialized translation in healthcare, law, engineering, and other technical verticals. Growth will remain healthy in that segment of the market and prices will drop more slowly than for less-specialized translation.

Enhanced by the increase in the digitalization of business documents worldwide, these trends will drive new demand from enterprises previously unable to justify the cost of enterprise-quality translation and related services and will open up a long tail of opportunities to provide native-language services targeted to small but rapidly growing emerging markets.

Despite its growth, artificial intelligence won’t eliminate the importance of Human Skills for the Digital Future.


VR Ferose

About VR Ferose

V R Ferose is Senior Vice President and Head of Globalization Services at SAP. Based in Palo Alto, Ferose is responsible for the adoption of SAP products worldwide through the delivery of solutions targeted at individual local markets. By providing functional localization, translation, product compliance and product support across several countries, Ferose’s team enables SAP’s global footprint.

Barney Pell

About Barney Pell

Barney Pell, Ph.D. is a leading entrepreneur, investor and advisor in AI and deep technology companies. He was Founder and CEO of Powerset, a pioneering startup in natural-language search that was acquired by Microsoft, where he then served as Strategist of Bing, lead for Bing’s local and mobile search team, leader of Microsoft’s long-range plan for semantics and knowledge, and initiated the projects that became Satori, Microsoft's knowledge graph, and Cortana, Microsoft’s conversational assistant. He has worked in R&D at NASA, SRI, Stanford and at Cambridge University, where his Ph.D. research pioneered the field of general game playing in AI.

Lorien Pratt

About Lorien Pratt

Lorien Pratt, Ph.D., Chief Scientist at Quantellia: Pratt has been delivering AI solutions for her clients for over 30 years. These include the Human Genome Project, the Colorado Bureau of Investigation, the US Department of Energy, and the Administrative Office of the US Courts. She is a machine learning pioneer, having led the teams that invented Inductive Transfer and Decision Intelligence (DI).

Sarah Jessica Parker: How To Start A Business

Jane Lu

On a recent SHE Innovates podcast, Sarah Jessica Parker talks about how she stepped into the role of designer and creative director of shoe brand the SJP Collection after years of playing Carrie Bradshaw on the show Sex and the City. She joins Michelle King, a leader in UN women’s gender innovation work, to share insights gained from knowing her customers and why getting started is the hardest part of building a business.

The actress’s first retail experience was a partnership with a Long Island, N.Y., company that produced sportswear sold at low prices. The company expanded quickly, but a large part of its business was in malls. “The business model didn’t work in ways that were meaningful and they ultimately folded,” she says. Two years later, everyone was doing low-price, ready-to-wear sports clothing, and she believes the company was ahead of its time. Although it was a challenging experience, she values what she learned.

She recognized there was money to be made in the shoe business, but many of Parker’s potential partners wanted to manufacture large-quantity, low-priced shoes in China. Instead, Parker wanted to produce high-quality shoes in a factory that values social compliance. She started the SJP Collection after contacting George Malkemus III, president of shoe company Manolo Blahnik. In 2014, the SJP Collection was born. Each pair of shoes is handcrafted by a third-generation Tuscan shoemaker in Italy and each design represents significant memories in Parker’s life.

Advice on asking for help

Parker says it’s hard for her to give advice to other aspiring entrepreneurs, because she has access to powerful people, which isn’t the case for everyone. She suggests realizing that “people do want to help, people want to be mentors.” She says it may feel weak to ask but, if you can summon the courage, there’s almost always a way to get contact information for people, and it’s impressive when people reach out. The most helpful thing for starting a business is being around people who have experience. “The more that I can get close to them and listen…the better equipped I am to ask for money or start a conversation…it’s that initial hurdle…that’s always the hardest part,” she says.

Parker finds that confidence comes from knowing you have something to offer: “When people believe in something…and they’ve informed themselves as much as they can, that says a lot to other people.” She says women may not have the confidence to go for what they want because they are caught up in not having concrete skills on their CV – but everybody starts somewhere. Business sprouts all the time from people who haven’t yet worked in those industries. Many women are struggling financially and can’t sit around and wait for an income; Parker hopes to encourage them to start a business.

Lessons on running a successful business

The actress designs four collections a year with Malkemus. Parker is inspired by women on the street, 1960s and ’70’s Italian magazines, and shoes she’s worn in the past. Designs are based on what is selling and what they believe in.

They’re a small company, so they re-invest their profits back into the business for growth. Shoes that they believe in but don’t do well are reintroduced into the market with changes. They created two “evergreens” – popular styles that can never be marked down – to encourage partners to buy into collections without having to worry about what the company does with unsold inventory.

Parker says, “I don’t think anyone can be lazy today…the customer is really informed…you have to know what [they’re] thinking.” If you’re starting a business, Parker recommends being in the store and ensuring customer service is your top priority. You should answer every question on social media and resolve issues – this means a lot to a customer.

Listen to Sarah Jessica Parker’s interview on the SHE Innovates podcast.

SHE Innovates is a podcast that shares the stories, challenges, and triumphs of women across innovation, technology and entrepreneurship. Listen to all our podcasts on PodBean.


Jane Lu

About Jane Lu

Jane is a writer and marketing intern at SAP. She is pursuing a Bachelor of Arts degree majoring in English at the University of Waterloo. While Jane is currently studying in Waterloo, she is originally from Toronto.

The “Emotional” Enterprise

Maricel Cabahug

In my previous blog, Soft Skills in a Software-Driven World, I talked about the important role that people and their soft skills will play in the intelligent enterprise. Here I’d like to continue that discussion with my view on how natural language processing (NLP), virtual reality (VR) and augmented reality (AR) will make the experience of the intelligent enterprise more emotional for us as well.

Our expectations for intelligent systems to understand us, help us, and connect with us on an emotional level will increase exponentially in the coming years. We will be conversing and interacting more and more with machines, expecting them to sound and react in a way that is convincingly human. We already see these technologies developing rapidly for the world of commerce, and consumer trends have driven and will continue to drive our expectations of software in the workplace. NLP, AR, and VR will make business tasks more personal, conversational, visual and visceral – thus more emotional. Once people get a taste for a richer and more compelling user experience for recreational purposes, the demand will increase for business tasks.

The rise of affective computing

Abstractions of today’s digital age such as the keyboard and computer mouse are neither natural nor particularly intuitive. Like reading and writing, they are learned rather than innate. These kinds of “meta” interactions will increasingly take a back seat, making room for more natural kinds of dealings with machines that rely on some of our earliest learned abilities and capabilities: seeing, hearing, and speaking. These innate abilities are connected at a very deep level with emotion. And it is emotion that is credited with being the magic key that unlocks learning, engagement, and memory.

The importance of emotion has not gone unnoticed by technology or industry, and it has given rise to a branch of computer science known as “affective computing,” pioneered by the MIT Media Lab in the mid-1990s. The goals of this new science reach in both directions: striving to make computers understand human emotion better as well as giving computers more emotional intelligence.

Business software makers have also taken notice, and are currently investing heavily in researching and crafting the personality attributes of digital assistants for the enterprise. There is also significant investment in how, using machine learning, digital assistants should adapt to user’s individual preferences.

Just as all politics is local, all emotional connection is personal. This is why personalization is so important. If you have ever talked to Alexa or any other digital assistant, you know that it is a very different experience from typing a search query into a search engine. The reason is that troupes of smart people are adding personality to today’s digital assistants. In the area of enterprise software, conversing with a digital assistant promises much more positive emotional potential than using a traditional GUI-based ERP system.

What’s next?

While we are making great strides in conversational user experience for business users (and of course, we are still in the early stages), we must continue to think ahead. The “next-gen” frontier will be immersive experience (IX), the term that bundles virtual, augmented, and mixed reality. Being transported visually and acoustically in time and space gets under your skin and goes directly to primitive centers of the brain. That level of tangibility surpasses anything we might experience today with a traditional GUI or even with simple video. Again, once people become accustomed to this in their private lives, they will demand more of it in their work environment.

My prediction is that work is about to get much more human and much more rewarding. As technology leaders, let’s embrace the future disruption and help everyone to succeed in the coming intelligent and emotional enterprise.

This article originally appeared on the SAP User Experience Community.


Maricel Cabahug

About Maricel Cabahug

As Chief Design Officer, Maricel is responsible for SAP’s overall design strategy and product design. At the heart of everything she is does is her goal to improve people's lives by making work delightful. She and her organization are passionate about co-innovation with customers to realize greater business value through technology that works for people. Maricel graduated with a Bachelor of Science in Mathematics and Computer Science from the Ateneo De Manila University in Manila (Philippines). She has an MBA from Lake Forest Graduate School of Management (Illinois, USA), where she graduated with honors. She also completed the program for high performers at the Harvard Business School.

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.