Perception vs. Reality: Immersive Technologies

Christopher Koch and Kai Goerlich

01Perception:
Immersive experiences are scripted productions.

Reality:

Credit: The Void VR

Early versions of immersive technologies, which include augmented reality (AR) and virtual reality (VR), resemble their video game forebears in that they are essentially journeys of discovery through different stages of preprogrammed experiences. We can scale virtual cliffs and mountains while riding a roller coaster or stumble over park benches in pursuit of Pokémon Go characters. However, as immersive technologies become imbued with machine learning and AI, digital experiences will become increasingly multisensory, making them more convincingly “real.” For example, Fast Company reports that surgeons can now practice a procedure using VR with a stylus that simulates the feel of operating on an open knee joint. The AR and VR of the future will gather information from the surrounding physical environment and instantly pass it back to an AI for analysis in order to derive unique, in-the-moment responses to our actions.

 

02Perception:
You need bulky equipment.

Reality:

Credit: Eter

We won’t be wearing those silly goggles forever. As the sensors that pick up data from our movements and speech become smaller, they will be easier to embed in everything. Imagine being in a factory in which every object has a visual overlay that lets you drill into information about that object, handle a digital version of it, or control it remotely. Today, firefighters can wear a smart helmet from Qwake Tech that combines AR technology with a thermal imaging camera. The device outlines the edges of objects (such as doors and stairs) and highlights sources of high heat, enabling firefighters to move through buildings more quickly. Companies including BMW are experimenting with advanced gesture recognition technology that would enable users to control devices without having to touch them. You might soon be able to launch a video chat by waving your hand.

 

03Perception:
A physical presence is required.

Reality:

Credit: vTime

For now. But before long, you’ll be able to create a VR avatar that looks like you, that sounds like you, and that can meet with your colleagues’ VR avatars in a realistic virtual space. The technology will likely require a brain-computer interface such as a headset or a brain-implanted chip. Neurable has a prototype software platform to power headset sensors that let users maneuver in VR video games using only their thoughts. Given sufficient computing power and a smart enough AI, you may one day be able to program your VR avatar to participate in a virtual meeting, tour the digital twin of a factory, or attend a keynote speech as your proxy and (theoretically) do a good enough job that your colleagues would never guess it wasn’t actually you. That will raise questions about how to tell if an avatar is being controlled live by a human or operated by a bot—and whether to require the differences be obvious. D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu

Monetizing And Optimizing Content Distribution With Machine Learning And Blockchain

Catherine Lynch

A generation ago, media conglomerates tightly controlled content production and distribution, deciding when, where, and how content was consumed. That’s all changed. Gone are the days of linear television channels and a single-television household. Today’s consumers decide when and where to consume content across multiple platforms.

With the average attention span of an adult hovering at eight seconds, down from 15 seconds in 2000, the media industry is fighting for increasingly smaller slivers of consumer attention. Media companies need a solution for monetizing content and delivering the right content to the right consumer at the right moment. Advanced analytics, machine learning, and blockchain are three disruptive technologies that can solve the twin problems of volume overload and content monetization.

How advanced analytics and machine learning solve the “paradox of choice”

For media companies, consumers drive demand. It’s all about what they want, when they want it, and which device they want it on.

“We went from a very analog-driven, subscriber numbers rated world to a world where it’s about engagement, and about data, and about consuming the content when you want,” says Richard Whittington, senior vice president, Media Industry Cloud Solutions, in the S.M.A.C. Talk Technology Podcast.

Of course, if consumers can’t find they content they want, they can’t consume it. In a world with nearly infinite choices, consumers are increasingly paralyzed by the “paradox of choice.” This theory states that there is a tipping point for choice, a point where more choices cease to provide an advantage and instead become a hindrance. It’s akin to the feeling of mindlessly scrolling Netflix looking for something to watch, but not finding anything. One-third of consumers say that they frequently cannot find anything to watch, according to a Cord Cutting Survey conducted for Rovi.

Media companies are experimenting with new machine learning algorithms to better understand consumer behavior, preference, and social cues. With machine learning is it easier to utilize metadata through intuitive, creative applications, rather than simply recommending a movie based on genre or actor preference. For example, machine learning enables language processing for a deeper understanding of content based on mood, emotion or intensity. Coupled with social signals, such as a conversation on Facebook around a new movie, machine-learning powered content recommendations could boost viewer engagement, satisfaction, and loyalty. Relevancy and timing are paramount: media companies that can provide consumers with a perfectly curated shortlist may outperform the companies offering an endless list of options that miss the mark.

New monetization pathways with blockchain and machine learning

Since consumers moved away from physical products like CDs or DVDs, media companies have struggled to monetize their content. According to Whittington, blockchain offers a new path forward, addressing problems associated with rights management, payment, and distribution.

“Blockchain gives media companies the ability to track content and create events when content is consumed,” says Whittington in the S.M.A.C. Talk Technology Podcast. “For example, if I send you a football match to view, this will trigger an event that indicates that you consumed the match. Money is then paid to whoever owns the rights to that match, rather than having to go through the traditional controlled, linear model. Blockchain has the ability to turn the whole business model upside down.”

In addition to using blockchain to monetize content distribution and consumption, Whittington says machine learning may also play an important role in content monetization.

“We’ve always heard about product placement in shows,” says Whittington in the S.M.A.C. Talk Technology Podcast. “But [product placement] has never been able to be measured to such an extent with heat mapping and knowing exactly what people looked at, and did they notice it, and how long did they look at it for, and what is the value of those impressions compared to other media avenues that they might have put those dollars into. I love the example, for instance, of how can you use machine learning to say, hey, on this episode of ‘Modern Family’ we had this many times that we showed XYZ’s product and we’re going to charge you for this. If you don’t see any value in this company A, company B might. We can actually create competition there.”

Next steps: Gaining the first-mover advantage

The media industry has undergone a transformation from a distribution model to a direct-to-consumer model and continues to evolve at a rapid pace. By 2020, Gartner predicts that artificial intelligence (AI) bots, rather than humans, will manage 85 percent of customer interactions. There will be over 82 million US millennial digital video customers. As media companies grapple with the challenge of getting the right content to the right consumer at the right time, companies that proactively invest in advanced analytics, machine learning and blockchain will gain a critical first-mover advantage. These companies will be best positioned to turn data into insights, monetizing the delivery of the right piece of content at the right moment to the right consumer.

To learn more about how digital transformation is disrupting content distribution and monetization in the media industry, listen to the S.M.A.C. Talk Technology Podcast with Richard Whittington.

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


Catherine Lynch

About Catherine Lynch

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

Three Ways To Implement AI For Business

Timo Elliott

Rivers of digital ink have been spilled on the new opportunities of artificial intelligence and machine learning. A lot of the coverage has been thought-provoking pieces on the long-term possibilities for “cognitive computing,” which allows computers to reason and simulate human thought processes.

In the meantime, robust machine learning algorithms are proving their worth in three ways that can easily be implemented in your business today.

Process automation

As a pattern-recognition engine, machine learning enables more automation in existing business processes.

  • Machine learning requires a great deal of high-quality data. In most organizations, this is found in existing business applications such as finance, logistics, and sales. The data in these systems has already been collected, cleansed, and stored over a long period of time, so there’s plenty of data available to create meaningful, useful predictive models.
  • Machine learning works best where there’s a tightly defined decision to be made, thousands of times a day, using a small number of variables, and where errors are clear and can be quickly corrected to further improve the algorithm. For example, “which of these bank payments correspond to this invoice?” is much easier to implement than “how can we improve long-term lung cancer survival rates?”
  • Machine learning is easiest to implement when the decision can be seamlessly automated as part of an existing business process, rather than requiring new processes or cultural changes.

Some examples of automatable processes:

  • Extracting relevant payment or order data from unstructured invoices, forms, emails (such as product names, amount, currency, payee, address, etc.)
  • Classifying transactions for tax compliance
  • Predicting when contracts based on usage will need to be renewed
  • Predicting and acting on stock-in-transit delays
  • Calculating the optimal length of time between physical inventories to ensure that it’s in line with automated systems
  • Routing customer service requests to the most appropriate teams

These “boring” uses of machine learning are by far the biggest real opportunity for business value today. McKinsey calculates that around 43% of financial processes can be automated using AI, and Gartner believes that by reducing the need for action and choices, AI will save half a billion people two hours a day this year alone. There is vast potential if you combine the power of machine learning with sensors, IoT, and other technologies.

More intuitive interfaces

Recent advances in machine learning have dramatically improved the ability of computers to decipher and understand human speech, writing, and commands.

New service chatbots can make it easier for customers to find information and do simple transactions via voice or chat interfaces. Machine learning algorithms can scan large amounts of product and technical documentation and automatically create answers to frequently asked questions. Initial deployments show that using chatbots to answer basic questions results in quicker customer conversations, increased customer satisfaction, and much lower costs.

Inside organizations, new enterprise digital assistants can assist you throughout your working day, especially in the context of core business processes such as procurement, HR, and budgeting. Instead of clicking around in a complex interface, you could tell the digital assistant, “I’d like to book a week’s vacation next week” or ask, “what’s the current actual vs. budget for my department?”

In a work environment, digital assistants have access to a vast amount of context that can be used to simplify or even anticipate the exchanges. The system knows how many vacation days you have remaining, which budget you are part of, and so on. Using machine learning, the system could even start identifying and alerting you to unusual circumstances without having to hunt down the information: “Based on your current reservations and forecasted trips, you will exceed your travel budget by 30% this quarter – would you like to review your trips or alert finance?”

Reveal and optimize processes in ways that previously weren’t possible

Machine learning can help efficiently process data that was too complex or expensive to analyze before. This gives insights into processes that can be optimized in new ways.

Examples include:

  • Predictive maintenance. Using detailed sensor data and algorithms to deter the first real signs of problems in parts or machinery. This can save huge sums of money compared to changing them on a regular schedule regardless of whether or not they are worn or – worse – waiting until the parts have failed and stopped production. These technologies are even being used for expensive human assets such as professional athletes. By keeping track of the detailed vital signs of players during training, coaches can ensure they are performing optimally while minimizing injuries that would keep them on the sidelines.
  • Image analysis and tracking. There has been a particularly sharp rise in the ability of new “deep learning” algorithms to interpret and understand complex images. This has opened up a wide range of business opportunities. For example, an oil company can scan barrels to ensure they are correctly and clearly labeled, or sponsors of sporting events can get detailed analytics of how often their logo appears during video coverage of a sporting event, for how long, and where on the screen. This helps them optimize coverage and determine if they are getting a good return on their sponsorship investments. For companies that have complex catalogs of many different products and variations, these algorithms can be used to quickly identify the right product from a photo, whether it’s a company selling office supplies, tires, or crystal jewelry.
  • Text analysis and classification. Machine learning can be used to extract text and images from electronic documents, then classify that information so it can be analyzed more easily than ever before. Uses include analyzing insurance claim texts for possible fraud, sentiment analysis for customer retention, classification of drug interactions based on research documents and much, much more.

Find out how businesses are benefiting from using machine learning.


Timo Elliott

About Timo Elliott

Timo Elliott is an Innovation Evangelist for SAP and a passionate advocate of innovation, digital business, analytics, and artificial intelligence. He was the eighth employee of BusinessObjects and for the last 25 years he has worked closely with SAP customers around the world on new technology directions and their impact on real-world organizations. His articles have appeared in publications such as Harvard Business Review, Forbes, ZDNet, The Guardian, and Digitalist Magazine. He has worked in the UK, Hong Kong, New Zealand, and Silicon Valley, and currently lives in Paris, France. He has a degree in Econometrics and a patent in mobile analytics. 

Hack the CIO

By Thomas Saueressig, Timo Elliott, Sam Yen, and Bennett Voyles

For nerds, the weeks right before finals are a Cinderella moment. Suddenly they’re stars. Pocket protectors are fashionable; people find their jokes a whole lot funnier; Dungeons & Dragons sounds cool.

Many CIOs are enjoying this kind of moment now, as companies everywhere face the business equivalent of a final exam for a vital class they have managed to mostly avoid so far: digital transformation.

But as always, there is a limit to nerdy magic. No matter how helpful CIOs try to be, their classmates still won’t pass if they don’t learn the material. With IT increasingly central to every business—from the customer experience to the offering to the business model itself—we all need to start thinking like CIOs.

Pass the digital transformation exam, and you probably have a bright future ahead. A recent SAP-Oxford Economics study of 3,100 organizations in a variety of industries across 17 countries found that the companies that have taken the lead in digital transformation earn higher profits and revenues and have more competitive differentiation than their peers. They also expect 23% more revenue growth from their digital initiatives over the next two years—an estimate 2.5 to 4 times larger than the average company’s.

But the market is grading on a steep curve: this same SAP-Oxford study found that only 3% have completed some degree of digital transformation across their organization. Other surveys also suggest that most companies won’t be graduating anytime soon: in one recent survey of 450 heads of digital transformation for enterprises in the United States, United Kingdom, France, and Germany by technology company Couchbase, 90% agreed that most digital projects fail to meet expectations and deliver only incremental improvements. Worse: over half (54%) believe that organizations that don’t succeed with their transformation project will fail or be absorbed by a savvier competitor within four years.

Companies that are making the grade understand that unlike earlier technical advances, digital transformation doesn’t just support the business, it’s the future of the business. That’s why 60% of digital leading companies have entrusted the leadership of their transformation to their CIO, and that’s why experts say businesspeople must do more than have a vague understanding of the technology. They must also master a way of thinking and looking at business challenges that is unfamiliar to most people outside the IT department.

In other words, if you don’t think like a CIO yet, now is a very good time to learn.

However, given that you probably don’t have a spare 15 years to learn what your CIO knows, we asked the experts what makes CIO thinking distinctive. Here are the top eight mind hacks.

1. Think in Systems

A lot of businesspeople are used to seeing their organization as a series of loosely joined silos. But in the world of digital business, everything is part of a larger system.

CIOs have known for a long time that smart processes win. Whether they were installing enterprise resource planning systems or working with the business to imagine the customer’s journey, they always had to think in holistic ways that crossed traditional departmental, functional, and operational boundaries.

Unlike other business leaders, CIOs spend their careers looking across systems. Why did our supply chain go down? How can we support this new business initiative beyond a single department or function? Now supported by end-to-end process methodologies such as design thinking, good CIOs have developed a way of looking at the company that can lead to radical simplifications that can reduce cost and improve performance at the same time.

They are also used to thinking beyond temporal boundaries. “This idea that the power of technology doubles every two years means that as you’re planning ahead you can’t think in terms of a linear process, you have to think in terms of huge jumps,” says Jay Ferro, CIO of TransPerfect, a New York–based global translation firm.

No wonder the SAP-Oxford transformation study found that one of the values transformational leaders shared was a tendency to look beyond silos and view the digital transformation as a company-wide initiative.

This will come in handy because in digital transformation, not only do business processes evolve but the company’s entire value proposition changes, says Jeanne Ross, principal research scientist at the Center for Information Systems Research at the Massachusetts Institute of Technology (MIT). “It either already has or it’s going to, because digital technologies make things possible that weren’t possible before,” she explains.

2. Work in Diverse Teams

When it comes to large projects, CIOs have always needed input from a diverse collection of businesspeople to be successful. The best have developed ways to convince and cajole reluctant participants to come to the table. They seek out technology enthusiasts in the business and those who are respected by their peers to help build passion and commitment among the halfhearted.

Digital transformation amps up the urgency for building diverse teams even further. “A small, focused group simply won’t have the same breadth of perspective as a team that includes a salesperson and a service person and a development person, as well as an IT person,” says Ross.

At Lenovo, the global technology giant, many of these cross-functional teams become so used to working together that it’s hard to tell where each member originally belonged: “You can’t tell who is business or IT; you can’t tell who is product, IT, or design,” says the company’s CIO, Arthur Hu.

One interesting corollary of this trend toward broader teamwork is that talent is a priority among digital leaders: they spend more on training their employees and partners than ordinary companies, as well as on hiring the people they need, according to the SAP-Oxford Economics survey. They’re also already being rewarded for their faith in their teams: 71% of leaders say that their successful digital transformation has made it easier for them to attract and retain talent, and 64% say that their employees are now more engaged than they were before the transformation.

3. Become a Consultant

Good CIOs have long needed to be internal consultants to the business. Ever since technology moved out of the glasshouse and onto employees’ desks, CIOs have not only needed a deep understanding of the goals of a given project but also to make sure that the project didn’t stray from those goals, even after the businesspeople who had ordered the project went back to their day jobs. “Businesspeople didn’t really need to get into the details of what IT was really doing,” recalls Ferro. “They just had a set of demands and said, ‘Hey, IT, go do that.’”

Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants.

But that was then. Now software has become so integral to the business that nobody can afford to walk away. Businesspeople must join the ranks of the IT consultants. “If you’re building a house, you don’t just disappear for six months and come back and go, ‘Oh, it looks pretty good,’” says Ferro. “You’re on that work site constantly and all of a sudden you’re looking at something, going, ‘Well, that looked really good on the blueprint, not sure it makes sense in reality. Let’s move that over six feet.’ Or, ‘I don’t know if I like that anymore.’ It’s really not much different in application development or for IT or technical projects, where on paper it looked really good and three weeks in, in that second sprint, you’re going, ‘Oh, now that I look at it, that’s really stupid.’”

4. Learn Horizontal Leadership

CIOs have always needed the ability to educate and influence other leaders that they don’t directly control. For major IT projects to be successful, they need other leaders to contribute budget, time, and resources from multiple areas of the business.

It’s a kind of horizontal leadership that will become critical for businesspeople to acquire in digital transformation. “The leadership role becomes one much more of coaching others across the organization—encouraging people to be creative, making sure everybody knows how to use data well,” Ross says.

In this team-based environment, having all the answers becomes less important. “It used to be that the best business executives and leaders had the best answers. Today that is no longer the case,” observes Gary Cokins, a technology consultant who focuses on analytics-based performance management. “Increasingly, it’s the executives and leaders who ask the best questions. There is too much volatility and uncertainty for them to rely on their intuition or past experiences.”

Many experts expect this trend to continue as the confluence of automation and data keeps chipping away at the organizational pyramid. “Hierarchical, command-and-control leadership will become obsolete,” says Edward Hess, professor of business administration and Batten executive-in-residence at the Darden School of Business at the University of Virginia. “Flatter, distributive leadership via teams will become the dominant structure.”

5. Understand Process Design

When business processes were simpler, IT could analyze the process and improve it without input from the business. But today many processes are triggered on the fly by the customer, making a seamless customer experience more difficult to build without the benefit of a larger, multifunctional team. In a highly digitalized organization like Amazon, which releases thousands of new software programs each year, IT can no longer do it all.

While businesspeople aren’t expected to start coding, their involvement in process design is crucial. One of the techniques that many organizations have adopted to help IT and businesspeople visualize business processes together is design thinking (for more on design thinking techniques, see “A Cult of Creation“).

Customers aren’t the only ones who benefit from better processes. Among the 100 companies the SAP-Oxford Economics researchers have identified as digital leaders, two-thirds say that they are making their employees’ lives easier by eliminating process roadblocks that interfere with their ability to do their jobs. Ninety percent of leaders surveyed expect to see value from these projects in the next two years alone.

6. Learn to Keep Learning

The ability to learn and keep learning has been a part of IT from the start. Since the first mainframes in the 1950s, technologists have understood that they need to keep reinventing themselves and their skills to adapt to the changes around them.

Now that’s starting to become part of other job descriptions too. Many companies are investing in teaching their employees new digital skills. One South American auto products company, for example, has created a custom-education institute that trained 20,000 employees and partner-employees in 2016. In addition to training current staff, many leading digital companies are also hiring new employees and creating new roles, such as a chief robotics officer, to support their digital transformation efforts.

Nicolas van Zeebroeck, professor of information systems and digital business innovation at the Solvay Brussels School of Economics and Management at the Free University of Brussels, says that he expects the ability to learn quickly will remain crucial. “If I had to think of one critical skill,” he explains, “I would have to say it’s the ability to learn and keep learning—the ability to challenge the status quo and question what you take for granted.”

7. Fail Smarter

Traditionally, CIOs tended to be good at thinking through tests that would allow the company to experiment with new technology without risking the entire network.

This is another unfamiliar skill that smart managers are trying to pick up. “There’s a lot of trial and error in the best companies right now,” notes MIT’s Ross. But there’s a catch, she adds. “Most companies aren’t designed for trial and error—they’re trying to avoid an error,” she says.

To learn how to do it better, take your lead from IT, where many people have already learned to work in small, innovative teams that use agile development principles, advises Ross.

For example, business managers must learn how to think in terms of a minimum viable product: build a simple version of what you have in mind, test it, and if it works start building. You don’t build the whole thing at once anymore.… It’s really important to build things incrementally,” Ross says.

Flexibility and the ability to capitalize on accidental discoveries during experimentation are more important than having a concrete project plan, says Ross. At Spotify, the music service, and CarMax, the used-car retailer, change is driven not from the center but from small teams that have developed something new. “The thing you have to get comfortable with is not having the formalized plan that we would have traditionally relied on, because as soon as you insist on that, you limit your ability to keep learning,” Ross warns.

8. Understand the True Cost—and Speed—of Data

Gut instincts have never had much to do with being a CIO; now they should have less to do with being an ordinary manager as well, as data becomes more important.

As part of that calculation, businesspeople must have the ability to analyze the value of the data that they seek. “You’ll need to apply a pinch of knowledge salt to your data,” advises Solvay’s van Zeebroeck. “What really matters is the ability not just to tap into data but to see what is behind the data. Is it a fair representation? Is it impartial?”

Increasingly, businesspeople will need to do their analysis in real time, just as CIOs have always had to manage live systems and processes. Moving toward real-time reports and away from paper-based decisions increases accuracy and effectiveness—and leaves less time for long meetings and PowerPoint presentations (let us all rejoice).

Not Every CIO Is Ready

Of course, not all CIOs are ready for these changes. Just as high school has a lot of false positives—genius nerds who turn out to be merely nearsighted—so there are many CIOs who aren’t good role models for transformation.

Success as a CIO these days requires more than delivering near-perfect uptime, says Lenovo’s Hu. You need to be able to understand the business as well. Some CIOs simply don’t have all the business skills that are needed to succeed in the transformation. Others lack the internal clout: a 2016 KPMG study found that only 34% of CIOs report directly to the CEO.

This lack of a strategic perspective is holding back digital transformation at many organizations. They approach digital transformation as a cool, one-off project: we’re going to put this new mobile app in place and we’re done. But that’s not a systematic approach; it’s an island of innovation that doesn’t join up with the other islands of innovation. In the longer term, this kind of development creates more problems than it fixes.

Such organizations are not building in the capacity for change; they’re trying to get away with just doing it once rather than thinking about how they’re going to use digitalization as a means to constantly experiment and become a better company over the long term.

As a result, in some companies, the most interesting tech developments are happening despite IT, not because of it. “There’s an alarming digital divide within many companies. Marketers are developing nimble software to give customers an engaging, personalized experience, while IT departments remain focused on the legacy infrastructure. The front and back ends aren’t working together, resulting in appealing web sites and apps that don’t quite deliver,” writes George Colony, founder, chairman, and CEO of Forrester Research, in the MIT Sloan Management Review.

Thanks to cloud computing and easier development tools, many departments are developing on their own, without IT’s support. These days, anybody with a credit card can do it.

Traditionally, IT departments looked askance at these kinds of do-it-yourself shadow IT programs, but that’s changing. Ferro, for one, says that it’s better to look at those teams not as rogue groups but as people who are trying to help. “It’s less about ‘Hey, something’s escaped,’ and more about ‘No, we just actually grew our capacity and grew our ability to innovate,’” he explains.

“I don’t like the term ‘shadow IT,’” agrees Lenovo’s Hu. “I think it’s an artifact of a very traditional CIO team. If you think of it as shadow IT, you’re out of step with reality,” he says.

The reality today is that a company needs both a strong IT department and strong digital capacities outside its IT department. If the relationship is good, the CIO and IT become valuable allies in helping businesspeople add digital capabilities without disrupting or duplicating existing IT infrastructure.

If a company already has strong digital capacities, it should be able to move forward quickly, according to Ross. But many companies are still playing catch-up and aren’t even ready to begin transforming, as the SAP-Oxford Economics survey shows.

For enterprises where business and IT are unable to get their collective act together, Ross predicts that the next few years will be rough. “I think these companies ought to panic,” she says. D!


About the Authors

Thomas Saueressig is Chief Information Officer at SAP.

Timo Elliott is an Innovation Evangelist at SAP.

Sam Yen is Chief Design Officer at SAP and Managing Director of SAP Labs.

Bennett Voyles is a Berlin-based business writer.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Survey: Four Ways Machine Learning Will Disrupt Your Business

Dan Wellers and Dirk Jendroska

We are entering the era of the machine learning enterprise, in which this subset of artificial intelligence (AI) capabilities will revolutionize operating models, shake up staffing methods, upend business models, and potentially alter the nature of competition itself. The adoption of machine learning capabilities will be limited only by an organization’s ability to change – but not every company will be willing or able to make such a radical shift.

Very soon, the difference between the haves and the have-nots of machine learning will become clear. “The disruption over the next three to five years will be massive,” says Cliff Justice, principal in KPMG’s Innovation and Enterprise Solutions team. Companies hanging onto their legacy processes will struggle to compete with machine learning enterprises able to compete with a fraction of the resources and entirely new value propositions.

For those seeking to be on the right side of the disruption, a new survey, conducted by SAP and the Economist Intelligence Unit (EIU), offers a closer look at organizations we’ve identified as the Fast Learners of machine learning: those that are already seeing benefits from their implementations.

Machine learning is unlike traditional programmed software. Machine learning software actually gets better – autonomously and continuously – at executing tasks and business processes. This creates opportunities for deeper insight, non-linear growth, and levels of innovation previously unseen.

Given that, it’s not surprising that machine learning has evolved from hype to have-to-have for the enterprise in seemingly record time. According to the SAP/EIU survey, more than two-thirds of respondents (68%) are already experimenting with it. What’s more, many of these organizations are seeing significantly improved performance across the breadth of their operations as a result, and some are aiming to remake their businesses on the back of these singular, new capabilities.

So, what makes machine learning so disruptive? Based on our analysis of the survey data and our own research, we see four primary reasons:

1. It’s probabilistic, not programmed

Machine learning uses sophisticated algorithms to enable computers to “learn” from large amounts of data and take action based on data analysis rather than being explicitly programmed to do something. Put simply, the machine can learn from experience; coded software does not. “It operates more like a human does in terms of how it formulates its conclusions,” says Justice.

That means that machine learning will provide more than just a one-time improvement in process and productivity; those improvements will continue over time, remaking business processes and potentially creating new business models along the way.

2. It creates exponential efficiency

When companies integrate machine learning into business processes, they not only increase efficiency, they are able to scale up without a corresponding increase in overhead. If you get 5,000 loan applications one month and 20,000 the next month, it’s not a problem, says Sudir Jha, head of product management and strategy for Infosys; the machines can handle it.

3. It frees up capital – financial and human

Because machine learning can be used to automate any repetitive task, it enables companies to redeploy resources to areas that make the organization more competitive, says Justice. It also frees up the employees within an organization to perform higher-value, more rewarding work. That leads to reduced turnover and higher employee satisfaction. And studies show that happier employees lead to higher customer satisfaction and better business results.

4. It creates new opportunities

AI and machine learning can offer richer insight, deeper knowledge, and predictions that would not be possible otherwise. Machine learning can enable not only new processes, but entirely new business models or value propositions for customers – “opportunities that would not be possible with just human intelligence,” says Justice. “AI impacts the business model in a much more disruptive way than cloud or any other disruption we’ve seen in our lifetimes.”

Machine learning systems alone, however, will not transform the enterprise. The singular opportunities enabled by these capabilities will only occur for companies that dedicate themselves to making machine learning part of a larger digital transformation strategy. The results of the SAP/EIU survey explain the makeup of the evolving machine learning enterprise. We’ve identified key traits important to the success of these machine-learning leaders that can serve as a template for others as well as an overview of the outcomes they’re already seeing from their efforts.

Learn more and download the full study here.  

 


About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Dirk Jendroska

About Dirk Jendroska

Dr. Dirk Jendroska is Head of Strategy and Operations Machine Learning at SAP. He supports the vision of SAP Leonardo Machine Learning to enable the intelligent enterprise by making enterprise applications intelligent. He leads a team working on machine learning strategy, marketing and communications.