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The Super Materials Revolution

Dan Wellers

Thousands of years ago, humans discovered they could heat rocks to get metal, and it defined an epoch. Later, we refined iron into steel, and it changed the course of civilization. More recently, we turned petroleum into plastic, with all that implies. Whenever we create new materials that push the limits of what’s possible, we send the world down an entirely new path.

Today, we’re on the verge of a revolution in materials science that will transform the world yet again. Scientists have developed tools that make it possible to design, build, and shape new “super materials” that will eclipse what we once believed were physical limits, create previously unimaginable opportunities, and expand the capabilities of what we already think of as exponential technologies in ways limited only by our imaginations.

Super strength in a pencil

The materials of the future are already being made in the present. One astonishing example is graphene, derived from the same graphite that’s in the pencil on your desk. A sheet just one atom thick, graphene is essentially two-dimensional. It weighs next to nothing, yet is up to 300 times stronger than steel. It conducts electricity more efficiently and faster than any other material. It dissipates heat faster than any other known material. It’s the only substance on earth that is completely impermeable by gas.

Excitement about graphene’s potential was high from the first, and it’s not ebbing. At least 13 conferences focusing on graphene, 2D substances, and nanotechnology are scheduled for 2016. The European Commission has created Graphene Flagship, Europe’s largest-ever research initiative, to bring graphene into the mainstream by 2026. And researchers have already developed an array of fascinating uses for graphene: new types of sensors, high-performance transistors, composites that are both super-light and super-strong, even a graphene-based gel for spinal cord injuries that can help nerve cells communicate by conducting electricity between them.

In 2015, IBM achieved a breakthrough in carbon nanotubes — graphene rolled into a tubular shape — that opens the door to faster transistors that will pack exponentially more computing power onto a single silicon chip. In fact, taken to its logical conclusion, the ability to shrink transistors to nanoscale could lead to processors that combine vast power and tiny size in a way that could be called “smart dust” (good news for those of us who don’t prioritize good housekeeping).

But that’s not all we’ll be doing with graphene. Here are just a few examples of what researchers say this single super material is likely to bring us in the not-too-distant future:

Transparent future mobile phone in hands. Concept.
  • batteries that last twice as long as they do now and could offer electric cars a 500-mile range on a single charge.
  • solar cells that are up to 1,000 times more efficient
  • clothing that conducts electricity and has wireless connectivity
  • bendable, highly conductive display screens
  • water desalinization using 15 to 50 percent less energy
  • coatings that can be applied to almost any surface that needs protection from water and air
  • meteor-resistant spacecraft and lightweight bulletproof armor, both enabled by graphene’s ability to dissipate energy from incoming projectiles

Marveling at the possibilities

Amazingly, graphene barely scratches the surface. Consider these advanced materials, all of them currently in development, and let yourself marvel at how we might put them to work:

Nanomaterials artificially engineered at molecular scale are giving rise to cotton-blend fabric that kills bacteria or conducts electricity, a coating that makes objects so frictionless they give no tactile feedback, and ceramics that bounce back from extreme pressure.

Recyclable carbon fiber composites that can be turned back into liquid form and remolded will replace the current versions that can only go into landfills when they’re broken.

Ultra-thin silicon circuits will lead to high-performance medical instruments that can be not just worn, but implanted or swallowed.

Flexible solar cells will replace large, unwieldy solar panels with thin film that can go almost anywhere and be incorporated into almost anything, from windows to tents to clothing.

Rechargeable metal-air batteries that can store electricity in grid-scale amounts will bring plentiful low-cost, reliable energy to places that currently have unreliable or no access to the traditional power grid.

Biomaterials will allow us to build robotic structures out of engineered materials that mimic organic ones. Soft materials that can be activated by an electric field will give us a whole new take on the human/machine interface. The next generation of prosthetics, for example, will be more comfortable, more functional, and harder to distinguish from living flesh.

Metamaterials, synthetic composites designed at the inter-atomic level, will have properties not found in nature. Those of you who love Star Trek and/or Harry Potter will be thrilled at this example: Scientists have already created a thin skin of metamaterial that makes whatever it covers undetectable. That’s right—an actual invisibility cloak. (Unfortunately, non-Romulans and Muggles will probably have to wait quite a while for the retail version.)

Designing the future, one molecule at a time

More mind-boggling developments in material science are on their way. The Materials Genome Initiative (MGI) is a multi-agency U.S. government project designed to help American institutions discover, develop, and deploy advanced materials at lower cost and bring them to market in less time. One central part of the initiative is a database attempting to map the hundreds of millions of different combinations of elements on the periodic table so that scientists can use artificial intelligence to predict what properties those combinations will have. As the database grows, scientists can draw on that data to determine how best to combine elements to create new super materials that have specific desired properties.

Of course, no technological advance is without its challenges, and the rise of the super materials is no exception. One technical hurdle that’s already pressing is the need to find ways to integrate graphene into a high-tech world in which industry and academia have already invested trillions of dollars in silicon. That sum is impossible to walk away from, so unless (until?) graphene supplants silicon entirely, factories, production lines, and research centers will have to be retooled so that both materials can co-exist in the same projects.

That said, advanced materials are a fundamental building block for change, so keep your eye on them as they develop. As super materials become exponentially easier to produce, we’ll start to see them in common use — imagine 3D printers that can create new objects with high-performance computing and battery power literally baked in. As they become more common, expect to see them weaving exponential technologies tightly into the fabric of daily life, both literally and figuratively, and bringing us ever-closer to a world of ambient intelligence. And as these foundation-shaking new materials become ubiquitous, it’s likely that they’ll make today’s technological marvels seem like a preschooler’s playthings.

Download the executive brief Super Materials: Building the Impossible

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To learn more about how exponential technology will affect business and life, see Digital Futures in the Digitalist Magazine.

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About Dan Wellers

Dan Wellers is the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.

Securing Your Digital Future: Cyber Trust As Competitive Advantage

Justin Somaini and Dan Wellers

The accepted wisdom in the cybersecurity field today is that there are two types of companies in the world: those that know they’ve been hacked, and those that don’t.

No enterprise is immune from cyber threats, and the list of big, scary data breaches continues to grow. The vast majority of companies in Europe (92 percent) have been hacked in the last five years, according to a recent survey by specialty insurer Lloyd’s of London. The average total cost of a breach is $4 million, according to a 2016 study by the Ponemon Institute.

Yet, categorized as risk to avoid rather than opportunity to pursue, cybersecurity has never been a terribly sexy topic in the C-suite. It’s an added expense—and one that slows down efforts to leap ahead technologically. The significant attention it receives tends to be of the negative variety when things go horribly wrong. Even as companies have embarked on their digital transformation efforts, security has remained an afterthought—tacked on after a big new investment in advanced analytics, cognitive systems, or Internet of Things (IoT) technology. Very soon, however, that reactive approach will seem antiquated.

A coming mind shift

Spending on IT security has been increasing in the last two years, even as overall technology budgets have been decreasing, according to 2016 report by the SANS Institute. But it’s not just a lift in spending that’s called for, but also shift in thinking.

In today’s age of rapidly developing transformational technologies, keeping on top of emerging security and privacy threats is more challenging—and more critical—than ever before. As companies collaborate with a wider network of partners and meet new demands for 24/7 operations and greater transparency with customers, cyber security risks multiply. The scope, scale, and impact of cyber attacks will grow in concert with increasing digitization:

  • 4.2 billion records were exposed in more than 4,000 known data breaches in 2016, according to Risk Based Security.
  • Cyber insurance premiums could increase tenfold to $20 billion annually by 2025, according to Marsh & McLellan.
  • The cost of data breaches will reach $2.1 trillion globally by 2019—nearly four times the estimated cost of breaches in 2015, according to Juniper Research.
  • Cyber attacks could cost the world up to $90 trillion in net economic benefit by 2030 if cyber security doesn’t keep pace with growing interconnectedness, according to a study published by the Atlantic Council and the Zurich Insurance Group
  • Cyber risk is expanding beyond the virtual world to the physical one. Hackers used highly destructive malware to bring down three Ukranian power distribution companies in 2016, for example, cutting power to 80,000 people.
  • The expanding universe of Internet of Things devices is particularly vulnerable to exploitation as companies may not update them after installation and many devices are not able to receive security update patches, according to AIG. In fact, an IoT hack took down Amazon, Twitter, Netflix, and other major sites in October 2016.
  • Connected devices pose particular concern in healthcare, an industry that already faces 340 percent more cyberattacks than the average industry and that fails to monitor 75 percent of hospital network traffic, according to a report from Raytheon and WebSense Security Labs.
  • Cyberattacks are one of the top ten global risks of highest concern for the next decade, right alongside such threats as water and food crises, natural catastrophes, social instability, and national governance failures, according to the World Economic Forum.

Just a third of companies today are sufficiently prepared to prevent a worst-case attack, according to Oliver Wyman and only a quarter currently treat cyber risk as a significant corporate risk. But as cyber risk expands and the attacks result not only in financial and reputational damage but also in physical destruction, danger, or loss of life, trust will become a competitive advantage. Therefore, those companies and organizations that want to dominate their markets will approach security as a strategic investment, proactively embedding cybersecurity strategy into business strategy.

As companies continue their digital transformations, they need to adopt more flexible and ubiquitous cyber defense measures to meet the more extreme threats they will face. Failing to do so risks unanticipated costs, operational shutdowns, reputational damage, and legal consequences.

A zero-trust approach

Unfortunately, there is no off-the-shelf solution to manage the entirety of a company’s cyber risk. As companies continue to introduce more digital innovations, they must continuously adopt and adapt cyber security measures commensurate with the growing threats they’ll face.

In a global economy, security can only be as good as the regulations, compliance, and enforcement in the countries where an organization operates—and those vary wildly around the world. What’s more, even when a company’s leaders take a more proactive approach to investing in cyber security protection and response, its partners and suppliers may not. Nearly 80 percent of companies fail to assess their customers and suppliers for cyber risk, according to a survey by Marsh & McLellan. And hackers certainly will be proactive about finding the weakest link in a value chain. Meanwhile, as enterprises adopt a growing legion of internet-connected devices and sensors, cyber security risk will be distributed even more widely.

Organizations must evolve from the attitude that perimeter security, achievable by firewalls or anti-virus protection, is enough. As interconnectivity and interdependency increases so too will the adoption of zero-trust networks. The zero-trust approach questions the assumption that a company can be made safe and sound within the confines of its own “secure” corporate network. Instead, a zero-trust approach places controls around data assets themselves and creates increased visibility into how they are used across a digital business ecosystem.

A new approach for a networked world

But, as SAP CEO Bill McDermott wrote to customers in 2016, “Information security is a journey without a destination. The security threat in the enterprise is relentless and multiplying, and the attackers are getting more sophisticated.” A zero-trust network is not enough. When the question is not if, but when, a significant breach will occur, how a company manages this inevitability becomes critical.

The key is to develop a robust approach to measuring, controlling, and responding to cyber risk. We recommend a three-pronged strategy to manage the threats in the expanding enterprise ecosystem:

  1. Prevent. This aspect of cyber security strategy remains as important as ever, and companies must evolve their preventative strategies, from their security policies and educational approaches to the actual access controls they put in place.
  1. Detect. In an evolving cyber threat environment, there is no foolproof prevention approach. Selecting and deploying appropriate intrusion detection systems for the timely detection and notification of compromises is critical.
  1. React. Detection is useless without a response. Companies that approach cyber security as a competitive advantage will put incident response plans in place in much the same way they would plan for recovery from a natural disaster.

Building trust, not walls

The Great Wall of China may have succeeded as an exercise in power or a feat of construction. But as a security strategy, it was a failure. Similarly a cyber security strategy focused on building strong enough borders around the company will fail. It’s impossible to keep all the bad guys out.

As more of a company’s data and its business processes become distributed, it’s cyber security strategy must become much more far-reaching. The good news is that even as digital technologies increase cyber security risk, they can also help mitigate it. Many cloud providers for example, are taking a more robust approach to security strategy that their customers might. New technologies like machine learning and big data analytics can strengthen security protections. Of course, the hackers can—and will—take advantage of these powerful technological advancements as well. Cyber risk experts will tell you the dark web is teeming with attack tools that enable hackers to take advantage of outdated security approaches and corporate vulnerabilities. They’ve been quick to take advantage of new automation tools in order to carry out more sophisticated and layered attacks on corporate and state assets.

Companies who embrace trust and security as competitive advantages will build security into their digital ecosystems at each layer:

  • Secure Products: Incorporating security into all applications, ensuring the protection of content and transactions.
  • Secure Operations: Investing in hardened systems, security patch management, security monitoring, end-to-end incident handling, and a comprehensive cloud operations security framework.
  • Secure Company: Creating a security-educated and aware workforce, end-to-end physical security of assets, and a comprehensive business continuity framework.

Forward-looking companies will follow these principles not only within their own organizations but expect them from their network of partners, supplier, and customers. The hackers of today and the future aren’t working alone and neither should the companies they’re targeting.

The risk of full-blown cyber catastrophes is real. The WEF has warned that large-scale cyber attacks could cause significant economic damage, geopolitical tensions, or widespread loss of trust in the Internet.

A report from the Atlantic Council and Zurich Insurance Group found as soon as 2018, there could be damage from massive cyber attacks equivalent to 1.5 percent of global GDP that is “certain to drastically increase risks and drag down net profits for companies that are most exposed to cyber-attacks..” The worst case scenario could result in a state of perpetual cyber crime and cyber warfare, increasingly vulnerable critical infrastructure, and losses of $90 trillion globally, according to the report.

A collaborative network approach will be critical to combatting such a persistent global threat with implications not just for corporate and personal data, but strategy, supply chains, products, and physical operations. Trust will be the most important currency in the digital future—one that companies will have to earn and work diligently to keep.

Read the executive brief The Future of Cybersecurity: Trust as Competitive Advantage.


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Justin Somaini

About Justin Somaini

Justin Somaini heads the Global Security unit at SAP. With more than 17 years of information security experience, he is responsible for SAP’s overall security strategy, ensuring that SAP and our customers have a consistent and convenient security experience. In his role Justin develops, implements, and manages SAP’s overall policies, standards, and guidelines as well as ongoing SAP security initiatives to meet the emerging international IT and cyber security environments and data protection and privacy laws worldwide. Before joining SAP in 2015, Justin was Chief Trust Officer at Box, the world's leading enterprise software platform for content collaboration. Prior to Box, Justin held the role of Chief Information Security Officer (CISO) at Yahoo!, driving security planning and operations for the company. Prior to Yahoo!, he was CISO of Symantec. Justin holds a Bachelor's of Science degree in Management Information Systems from Drexel University, Philadelphia.

About Dan Wellers

Dan Wellers is the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.

Machine Learning: The Real Business Intelligence

Kai Görlich

Business intelligence (BI) tools first appeared on the enterprise technology scene several decades ago, at birth clumsy and difficult to use but ultimately improving the flow of data through organizations from their operational systems to decision support. Data warehousing cut the time it took to access data, but even at their full maturity, BI systems could do little more than produce data and reports in a traditional organized way. The rules-driven software wasn’t actually providing intelligence at all.

But with the advancement of artificial intelligence and—more importantly—machine learning, true business intelligence is actually on its way to the enterprise. Such self-learning software will run on servers, be built into bots, drive decision-making systems, be embedded into cars or aircraft, and become the beating heart of mobile devices.

Increased data-processing power, the availability of big data, the Internet of Things, and improvements in algorithms are converging to power this actual business intelligence. To be clear, this will be an evolution rather than a revolution. There are a number of factors that could limit the progress of machine learning and its integration into business, from quality of data and human programming to cultural resistance. However, the question is when, not if, the BI tools of today become a quaint relic of earlier times and real business intelligence emerges.

Beyond sci-fi AI

Artificial intelligence (AI), a term dating back to the 1960s, is tossed about quite a bit these days. It’s an umbrella descriptor that refers to computers capable of doing things that a human typically would. It’s often inaccurately used interchangeably with machine learning. Machine learning, however, is a specific subset of AI that uses statistical methods to improve the performance of a system over time. Any programmer can write code to develop a program that more or less acts like a human. But it’s not machine learning unless the systems is learning to how to behave based on data. Machine learning comes in several flavors, sometimes referred to as supervised learning (the algorithm is trained using examples where the input data and the correct output are known), unsupervised learning (the algorithm must discover patterns in the data on its own), and reinforced learning (the algorithm is rewarded for penalized for the actions it takes based on trial and error). In each case, the machine is able to learn from data—structured and increasingly unstructured in the future —without explicitly being programmed to do so, absorbing new behaviors and functions over time.

Gartner recently placed machine learning at the height of “inflated expectations” in its report, noting that this emerging capability is two to five years from mainstream adoption. But those immersed in machine learning development are grounded in reality. And the reality is that they are making significant strides. Machine learning mimics human learning; it takes time.

The big advantage machines have over us is that they can handle massive amounts of data, take advantage of ever-faster processing power, and run (and thereby) improve 24 hours a day. Over just the last four years, the error rate in machine learning-driven image recognition, for example, has fallen dramatically to near zero—practically to human performance levels.

Still, every instance of machine learning is different. Just as, for us, learning to play piano is different from learning how to crawl, each instance of machine learning is different. It may take longer for a computer to learn to analyze text than it takes it to recognize the meaning of a furrowed brow.

Machine learning for the rest of us

Digital giants are leading the way in machine learning development. Google has more than 1,000 machine learning projects underway, including its Google Brain project. IBM continues to make headlines with Watson. Microsoft uses neural networks to powers its search rankings, photo search, and translation systems while Facebook translates 2 billion user posts in more than 40 languages each day in the same manner. In the last year alone, venture capital firms have poured approximately $5 billion into machine intelligence startups.

At this early stage, there are no concrete baselines for machine learning adoption rates in the rest of industry. Consumer adoption of machine-learning technologies has taken off with the success of Amazon’s Echo and Apple’s Siri. It’s an important component in fraud detection and surveillance, image and voice recognition, and product recommendations. But, as a recent report from 451 Research pointed out, but enterprise adoption is less pervasive. To broaden the enterprise use of machine learning, some of the biggest tech players in the field, such as Google, Microsoft, Intel, and Facebook make their older machine learning systems and designs available to the open source community.

Machine learning could bring significant value to the business: improving the core functionality of existing software and analytics, uncovering previously inaccessible insights hidden in large data sets unstructured data formats, and taking over tasks like image recognition, text analysis, and repetitive knowledge work. The potential use cases are seemingly endless, from supply chain and risk detection to logistics and technical support to behavioral analysis and customer support.

Limiting factors

Machine learning is not a silver bullet and there are a number of issues that companies must address. Because it is based on algorithms that learn from data rather than relying on rules-based programming, effective machine learning is dependent on relevant and reliable data—and lots of it. Business leaders must take a hard look at available data (the quality of it, the gaps in it, the silos around it) to extract the value of self-learning capabilities.

What’s more, machine learning is ultimately guided by human decision making. Humans will decide what problems the technology will be used to solve. Humans will develop the algorithms to employ. And humans don’t necessarily operate on logic.

Perhaps most importantly, the adoption of machine learning is going to be determined more by organizational and cultural forces than by technical factors. Humans are yet not machine ready. Machine learning will need to be designed with the man-machine interaction in mind. Fear, uncertainty, and doubt about how these self-learning systems will impact our roles and our livelihoods must be addressed, and significant investment must be made in change management as business processes and models are reworked to integrate self-learning systems.

The rise of the machines in business—and beyond

Business leaders have been talking about the importance of context-sensitive systems to the enterprise for several years. Machine learning could finally bring that concept to life—from smart software to smart vehicles to intelligent machines and robots to machine learning-enabled digital assistants and to smart grids that can learn to understand their environment and adapt on their own.

Smart machines will become an integral part of business—and daily life—creating insight from data in ways that humans on their own never could. That will lead to new levels of automation, cost savings, and process change. Gartner predicts that in 2018, 45 percent of the fastest-growing companies will have fewer employees than instances of smart machines and customer-facing digital assistants will recognize individuals by face and voice across channels and partners. Self-learning algorithms will introduce unprecedented levels of efficiency in business systems taking over highly repetitive work. On a personal level, smart assistant technology could turn our mobile devices—already capable of voice response, into interactive learning assistants tasked with helping us navigate our daily lives. Machine learning could uncover new efficiencies in our complex and overstressed infrastructure systems including energy, logistics, healthcare, IT, and even education.

The value that machine learning can deliver will be dependent on the degree to which these systems can deal with structured and unstructured data (which remains a challenge) as well as the availability of useful data and quality algorithms. Taking over the mundane and repetitive tasks within business systems and for consumers is all but guaranteed. Organizations are starting to collect unstructured und unprocessed data in so-called data lakes. If companies open up more of their self-learning data and designs, that shared insight will result in ever better algorithms and more accurate and effective machine learning capabilities.

If machine learning matures to the point that it can handle unstructured data, organizations openly share data, and algorithms begin to interact with each other more freely, machine learning will be embedded in all systems, devices, machines and software. That will enable highly context-sensitive insight at both the large scale and individual level. We can only guess about the level of automation and support that will result, but the impact on business—and society—will be significant.

However this evolution plays out, it will take time. But business leaders can prepare now for the rise of machine learning, taking a hard look at data structures and availability, freeing up information from siloed systems, identifying the richest areas for machine-fueled insight and improvement, and addressing the cultural and change management challenges that will be required to take advantage of this real business intelligence.

Download the executive brief Rise of the Smart Machines

To learn more about how exponential technology will affect business and life, see Digital Futures in the Digitalist Magazine.

For more on next-generation business intelligence in the enterprise, see An AI Shares My Office.

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Kai Görlich

About Kai Görlich

Kai Goerlich is the Idea Director of Thought Leadership at SAP. His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.

The Future of Cybersecurity: Trust as Competitive Advantage

Justin Somaini and Dan Wellers

 

The cost of data breaches will reach US$2.1 trillion globally by 2019—nearly four times the cost in 2015.

Cyberattacks could cost up to $90 trillion in net global economic benefits by 2030 if cybersecurity doesn’t keep pace with growing threat levels.

Cyber insurance premiums could increase tenfold to $20 billion annually by 2025.

Cyberattacks are one of the top 10 global risks of highest concern for the next decade.


Companies are collaborating with a wider network of partners, embracing distributed systems, and meeting new demands for 24/7 operations.

But the bad guys are sharing intelligence, harnessing emerging technologies, and working round the clock as well—and companies are giving them plenty of weaknesses to exploit.

  • 33% of companies today are prepared to prevent a worst-case attack.
  • 25% treat cyber risk as a significant corporate risk.
  • 80% fail to assess their customers and suppliers for cyber risk.

The ROI of Zero Trust

Perimeter security will not be enough. As interconnectivity increases so will the adoption of zero-trust networks, which place controls around data assets and increases visibility into how they are used across the digital ecosystem.


A Layered Approach

Companies that embrace trust as a competitive advantage will build robust security on three core tenets:

  • Prevention: Evolving defensive strategies from security policies and educational approaches to access controls
  • Detection: Deploying effective systems for the timely detection and notification of intrusions
  • Reaction: Implementing incident response plans similar to those for other disaster recovery scenarios

They’ll build security into their digital ecosystems at three levels:

  1. Secure products. Security in all applications to protect data and transactions
  2. Secure operations. Hardened systems, patch management, security monitoring, end-to-end incident handling, and a comprehensive cloud-operations security framework
  3. Secure companies. A security-aware workforce, end-to-end physical security, and a thorough business continuity framework

Against Digital Armageddon

Experts warn that the worst-case scenario is a state of perpetual cybercrime and cyber warfare, vulnerable critical infrastructure, and trillions of dollars in losses. A collaborative approach will be critical to combatting this persistent global threat with implications not just for corporate and personal data but also strategy, supply chains, products, and physical operations.


Download the executive brief The Future of Cybersecurity: Trust as Competitive Advantage.


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How Digital Transformation Is Rewriting Business Models

Ginger Shimp

Everybody knows someone who has a stack of 3½-inch floppies in a desk drawer “just in case we may need them someday.” While that might be amusing, the truth is that relatively few people are confident that they’re making satisfactory progress on their digital journey. The boundaries between the digital and physical worlds continue to blur — with profound implications for the way we do business. Virtually every industry and every enterprise feels the effects of this ongoing digital transformation, whether from its own initiative or due to pressure from competitors.

What is digital transformation? It’s the wholesale reimagining and reinvention of how businesses operate, enabled by today’s advanced technology. Businesses have always changed with the times, but the confluence of technologies such as mobile, cloud, social, and Big Data analytics has accelerated the pace at which today’s businesses are evolving — and the degree to which they transform the way they innovate, operate, and serve customers.

The process of digital transformation began decades ago. Think back to how word processing fundamentally changed the way we write, or how email transformed the way we communicate. However, the scale of transformation currently underway is drastically more significant, with dramatically higher stakes. For some businesses, digital transformation is a disruptive force that leaves them playing catch-up. For others, it opens to door to unparalleled opportunities.

Upending traditional business models

To understand how the businesses that embrace digital transformation can ultimately benefit, it helps to look at the changes in business models currently in process.

Some of the more prominent examples include:

  • A focus on outcome-based models — Open the door to business value to customers as determined by the outcome or impact on the customer’s business.
  • Expansion into new industries and markets — Extend the business’ reach virtually anywhere — beyond strictly defined customer demographics, physical locations, and traditional market segments.
  • Pervasive digitization of products and services — Accelerate the way products and services are conceived, designed, and delivered with no barriers between customers and the businesses that serve them.
  • Ecosystem competition — Create a more compelling value proposition in new markets through connections with other companies to enhance the value available to the customer.
  • Access a shared economy — Realize more value from underutilized sources by extending access to other business entities and customers — with the ability to access the resources of others.
  • Realize value from digital platforms — Monetize the inherent, previously untapped value of customer relationships to improve customer experiences, collaborate more effectively with partners, and drive ongoing innovation in products and services,

In other words, the time-tested assumptions about how to identify customers, develop and market products and services, and manage organizations may no longer apply. Every aspect of business operations — from forecasting demand to sourcing materials to recruiting and training staff to balancing the books — is subject to this wave of reinvention.

The question is not if, but when

These new models aren’t predictions of what could happen. They’re already realities for innovative, fast-moving companies across the globe. In this environment, playing the role of late adopter can put a business at a serious disadvantage. Ready or not, digital transformation is coming — and it’s coming fast.

Is your company ready for this sea of change in business models? At SAP, we’ve helped thousands of organizations embrace digital transformation — and turn the threat of disruption into new opportunities for innovation and growth. We’d relish the opportunity to do the same for you. Our Digital Readiness Assessment can help you see where you are in the journey and map out the next steps you’ll need to take.

Up next I’ll discuss the impact of digital transformation on processes and work. Until then, you can read more on how digital transformation is impacting your industry.

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About Ginger Shimp

With more than 20 years’ experience in marketing, Ginger Shimp has been with SAP since 2004. She has won numerous awards and honors at SAP, including being designated “Top Talent” for two consecutive years. Not only is she a Professional Certified Marketer with the American Marketing Association, but she's also earned her Connoisseur's Certificate in California Reds from the Chicago Wine School. She holds a bachelor's degree in journalism from the University of San Francisco, and an MBA in marketing and managerial economics from the Kellogg Graduate School of Management at Northwestern University. Personally, Ginger is the proud mother of a precocious son and happy wife of one of YouTube's 10 EDU Gurus, Ed Shimp.