Machine Learning: The Real Business Intelligence

Kai Goerlich

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 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

Innovate Your Business Model With Conversational AI: Part 3

Ivo van Barneveld

Part 3 in the 3-part “Driving Innovation with Conversational AI” series

In the first two blogs in this series, we looked at the impact of injecting conversational AI into the value proposition as digital value drivers. Now, let’s explore how conversational AI can change the way you interact with your customers.

Unlike humans, chatbots operate 24/7, so your customers can engage with you any time they want. What’s more, as chatbots can pull information from many different sources and systems much faster than humans can, their answers and recommendations are more accurate and personalized. Many customers dislike calling a customer service agent: choosing the right option from a menu, waiting for the next available agent, explaining the problem or question, waiting for the agent to come back with an answer – that’s no longer acceptable in 2018! Instead, customers prefer to interact with brands as they do with friends: through conversations, getting a personalized experience, being able to continue on a previous thread. Chatbots offer just that.

Covering both high- and low-touch interactions

So let’s have a look again at the business model canvas, and this time inject conversational AI into customer relationships. The relationship could be very personal and “high-touch,” for example, when dedicated account managers build close relationships with their customers. Or it could be automated and “low-touch,” for example, through self-service tools with no personal interaction whatsoever. The paradox of conversational AI is that it covers both sides of this scale! It offers a personalized, contextual, 24/7 interaction, while being fully automated at the same time.

Chatbots can be used throughout the customer journey:

  • Evaluation: answering general questions about the product or service
  • Purchase: proposing the right product or service, suggesting related products or services (upsell), handling the transaction
  • Delivery: providing information about order status
  • After sales: providing tips and tricks, handling customer incidents

Thousands of brands already use chatbots in one or more of these phases. The Wall Street Journal offers a chatbot to deliver the latest breaking news, live stock market data, and other financial information. Expedia offers a bot for travelers to quickly see hotel options and move forward with a booking. And Tommy Hilfiger has a bot helping fashionistas choose clothes that match their style.

The conversational nature of chatbots make them very suitable for communication channels customers love to use: Facebook Messenger, WhatsApp, WeChat and Slack, and so on. Rather than trying to pull customers to your Web site or mobile application, with a chatbot, you can follow customers where they spend most of their time when engaging with others: messaging applications – and lower the barrier for engaging them with your brand. KLM President & CEO Pieter Elbers couldn’t have said it better when announcing KLM’s business account on WhatsApp: “We want to be where our customers are and, given the 1 billion users, you have to be on WhatsApp. With an account verified by WhatsApp, we offer our customers worldwide a reliable way to receive their flight information and ask questions 24/7.”

Expanding the exposure of your brand

The popularity of messaging applications is huge: combined, they have more than 3 billion users globally. That’s a reach you can’t get anywhere else! Offering your chatbot in these channels will give your brand exposure to new potential customers. In turn, new customers will lead to incremental revenues. An increase in customer satisfaction is another positive effect.

So we see how introducing conversational AI in the customer relationship propagates to the customer channel, customer segment, and revenue components in the business model canvas. But there is another effect: chatbots are often associated with reducing operating costs. Benchmark figures for call center pricing show that the average cost per minute for inbound customer calls ranges between $0.35 and $0.90. The cost of an API call to conversational cloud services such as Language Understand (LUIS) on Microsoft’s Azure platform, or Conversation on IBM Watson, is less than $0.01. Or, as stated in an SAP solution brief, the cost of resolving a ticket is $0.10 per ticket using chatbots versus $2.50 per ticket using a human agent. This means that you can service the same number of customers with fewer personnel.

Freeing up people for developing customer intimacy

And while this is great if your focus is on the bottom line, you could also use the freed-up resources for innovation. For example, you could create a new value proposition by focusing on customer intimacy. While chatbots perform high-volume but low-value tasks like providing order status, resetting passwords and so on, your customer service personnel will have more time to focus on high-value activities. These might include building personal relationships with customers (think private banking), handling complex issues and brand advocacy (writing blogs and how-to’s). These activities will positively change your value proposition, and with that, you can unearth new customer segments in the market!

Don’t wait, start now

There are of course many other examples of how conversational AI can impact your business model, including those focused on internal (employee) use cases:

    • improve employee productivity by achieving faster task completion
    • reduce time spent on administrative tasks
    • eliminate the need for end-user training for enterprise applications
    • access self-service tools
    • support decision-making
    • write meeting minutes
    • book travel
    • order products

These are ideal areas for gaining experience with conversational AI. All major software suppliers for systems of record offer a digital assistant to improve the user experience of your employees. SAP CoPilot is already available for SAP S/4HANA Cloud, and with planned support for natural language interaction for SAP S/4HANA on-premise systems in early 2018.

AI has reached a stage where chatbots can have a meaningful, engaging, and gratifying conversation with end users. The technology is available, the chatbot ecosystem is fairly robust, and users embrace it. So don’t wait, and start creating your first chatbot!

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Ivo van Barneveld

About Ivo van Barneveld

Ivo van Barneveld is a passionate evangelist of innovations in user experience, mobile, and Internet of Things. His work focuses on the intersection of technology and business. He is currently a member of the UX Customer Office team in SAP Global Design, with the remit to drive adoption of SAP’s award-winning user experience, SAP Fiori. Previously, he worked at SAP as a lead consultant, supporting customers with planning and executing digital transformation strategies. Prior to joining SAP in 2012, he held several business development, account manager, and partner manager roles at Nokia and Layar, among others. Ivo holds a Master’s degree in Applied Physics from the Delft University of Technology, and is based in the Netherlands.

Underfit Vs. Overfit: Why Your Machine Learning Model May Be Wrong

Paul Kurchina

Just shy of 60 years old, machine learning has never looked so good. Exponential data growth, advanced algorithms, and powerful computer processing are enabling the technology to fulfill its ultimate destiny: identifying profitable opportunities and avoiding unknown risks by evaluating massive volumes of complex data and delivering accurate results in real time.

However, during the Americas’ SAP Users’ Group (ASUG) Webcast “Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes,” Darwin Deano, principal and chief SAP Leonardo officer, and Denise McGuigan, senior manager and Deloitte reimagine platform leader (both from Deloitte Consulting LLP), forewarned that machine learning is only as good as the algorithm. And the algorithm is only as good as the data.

Deano advised, “Data evolves over time. Even though ERP systems provide a strong foundation for identifying opportunities and delivering on the promise of machine learning, it does not factor in information outside the core structure, nor does it move with information as it changes.”

Adding to Deano’s observation, McGuigan noted the importance of understanding data well. “Businesses must know all of the variables and data sets that drive certain decisions. Doing so will reduce the risk of bringing information into the analysis that will only cause noise or false positives within machine learning results,” she said.

Machine learning success depends on finding the right data “fit”

Although it’s tempting to jump into machine learning by automating heavily used transactions, McGuigan warned that this view misses the cognitive advantages of machine learning. “Companies have a considerable opportunity to operate with tremendous efficiency and speed,” she said. “They should also consider enabling processes and tasks that free up resources, time, and talent for entering new markets; offering breakthrough products and services; and innovating industry-disruptive business models.”

To successfully execute such an advanced form of machine learning, organizations must ensure that the right data is being applied to the machine learning model. Understanding how each data category impacts the training data helps businesses fine-tune the model to increase prediction accuracy and efficient automation. However, as McGuigan suggested, one of the most common causes of underperforming or inaccurate models can be attributed to an imbalance of data used, commonly referred as biased invariance.

One form of disparity is experienced when the model underfits the training data when assumptions are oversimplified to the point where either the wrong information or too little insight is applied. This condition leads to the inability to capture the relationship between the programmed input examples and the targeted outcomes.

On the flip side, a model can overfit training data when too much information is used and there is too much complexity. Even though it performs well with training data, the model cannot accurately evaluate data to deliver the expected outcome. The model only memorizes data, instead of learning from it to generalize how unseen examples should be treated.

It’s also important to remember that this exercise is an iterative process of trial and error. The model may be calibrated well enough at one moment to deliver expected outcomes consistently and predictively; however, as Deano suggested, “what may be overfitting today may not be the same situation six months from now as data evolves.”

For more insights into putting machine intelligence to work for your organization, watch the replay of the Americas’ SAP Users’ Group (ASUG) Webcast “Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes,” featuring Darwin Deano, principal and chief SAP Leonardo officer for Deloitte Consulting LLP, and Denise McGuigan, senior manager and Deloitte reimagine platform leader for Lights Out Finance at Deloitte Consulting LLP

Comments

Paul Kurchina

About Paul Kurchina

Paul Kurchina is a community builder and evangelist with the Americas’ SAP Users Group (ASUG), responsible for developing a change management program for ASUG members.

Why Strategic Plans Need Multiple Futures

By Dan Wellers, Kai Goerlich, and Stephanie Overby , Kai Goerlich and Stephanie Overby

When members of Lowe’s Innovation Labs first began talking with the home improvement retailer’s senior executives about how disruptive technologies would affect the future, the presentations were well received but nothing stuck.

“We’d give a really great presentation and everyone would say, ‘Great job,’ but nothing would really happen,” says Amanda Manna, head of narratives and partnerships for the lab.

The team realized that it needed to ditch the PowerPoints and try something radical. The team’s leader, Kyle Nel, is a behavioral scientist by training. He knows people are wired to receive new information best through stories. Sharing far-future concepts through narrative, he surmised, could unlock hidden potential to drive meaningful change.

So Nel hired science fiction writers to pen the future in comic book format, with characters and a narrative arc revealed pane by pane.

The first storyline, written several years before Oculus Rift became a household name, told the tale of a couple envisioning their kitchen renovation using virtual reality headsets. The comic might have been fun and fanciful, but its intent was deadly serious. It was a vision of a future in which Lowe’s might solve one of its long-standing struggles: the approximately US$70 billion left on the table when people are unable to start a home improvement project because they can’t envision what it will look like.

When the lab presented leaders with the first comic, “it was like a light bulb went on,” says Manna. “Not only did they immediately understand the value of the concept, they were convinced that if we didn’t build it, someone else would.”

Today, Lowe’s customers in select stores can use the HoloRoom How To virtual reality tool to learn basic DIY skills in an interactive and immersive environment.

Other comics followed and were greeted with similar enthusiasm—and investment, where possible. One tells the story of robots that help customers navigate stores. That comic spawned the LoweBot, which roamed the aisles of several Lowe’s stores during a pilot program in California and is being evaluated to determine next steps.

And the comic about tools that can be 3D-printed in space? Last year, Lowe’s partnered with Made in Space, which specializes in making 3D printers that can operate in zero gravity, to install the first commercial 3D printer in the International Space Station, where it was used to make tools and parts for astronauts.

The comics are the result of sending writers out on an open-ended assignment, armed with trends, market research, and other input, to envision what home improvement planning might look like in the future or what the experience of shopping will be in 10 years. The writers come back with several potential story ideas in a given area and work collaboratively with lab team members to refine it over time.

The process of working with writers and business partners to develop the comics helps the future strategy team at Lowe’s, working under chief development officer Richard D. Maltsbarger, to inhabit that future. They can imagine how it might play out, what obstacles might surface, and what steps the company would need to take to bring that future to life.

Once the final vision hits the page, the lab team can clearly envision how to work backward to enable the innovation. Importantly, the narrative is shared not only within the company but also out in the world. It serves as a kind of “bat signal” to potential technology partners with capabilities that might be required to make it happen, says Manna. “It’s all part of our strategy for staking a claim in the future.”

Planning must become completely oriented toward—and sourced from—the future.

Companies like Lowe’s are realizing that standard ways of planning for the future won’t get them where they need to go. The problem with traditional strategic planning is that the approach, which dates back to the 1950s and has remained largely unchanged since then, is based on the company’s existing mission, resources, core competencies, and competitors.

Yet the future rarely looks like the past. What’s more, digital technology is now driving change at exponential rates. Companies must be able to analyze and assess the potential impacts of the many variables at play, determine the possible futures they want to pursue, and develop the agility to pivot as conditions change along the way.

This is why planning must become completely oriented toward—and sourced from—the future, rather than from the past or the present. “Every winning strategy is based on a compelling insight, but most strategic planning originates in today’s marketplace, which means the resulting plans are constrained to incremental innovation,” says Bob Johansen, distinguished fellow at the Institute for the Future. “Most corporate strategists and CEOs are just inching their way to the future.” (Read more from Bob Johansen in the Thinkers story, “Fear Factor.”)

Inching forward won’t cut it anymore. Half of the S&P 500 organizations will be replaced over the next decade, according to research company Innosight. The reason? They can’t see the portfolio of possible futures, they can’t act on them, or both. Indeed, when SAP conducts future planning workshops with clients, we find that they usually struggle to look beyond current models and assumptions and lack clear ideas about how to work toward radically different futures.

Companies that want to increase their chances of long-term survival are incorporating three steps: envisioning, planning for, and executing on possible futures. And doing so all while the actual future is unfolding in expected and unexpected ways.

Those that pull it off are rewarded. A 2017 benchmarking report from the Strategic Foresight Research Network (SFRN) revealed that vigilant companies (those with the most mature processes for identifying, interpreting, and responding to factors that induce change) achieved 200% greater market capitalization growth and 33% higher profitability than the average, while the least mature companies experienced negative market-cap growth and had 44% lower profitability.

Looking Outside the Margins

“Most organizations lack sufficient capacity to detect, interpret, and act on the critically important but weak and ambiguous signals of fresh threats or new opportunities that emerge on the periphery of their usual business environment,” write George S. Day and Paul J. H. Schoemaker in their book Peripheral Vision.

But that’s exactly where effective future planning begins: examining what is happening outside the margins of day-to-day business as usual in order to peer into the future.

Business leaders who take this approach understand that despite the uncertainties of the future there are drivers of change that can be identified and studied and actions that can be taken to better prepare for—and influence—how events unfold.

That starts with developing foresight, typically a decade out. Ten years, most future planners agree, is the sweet spot. “It is far enough out that it gives you a bit more latitude to come up with a broader way to the future, allowing for disruption and innovation,” says Brian David Johnson, former chief futurist for Intel and current futurist in residence at Arizona State University’s Center for Science and the Imagination. “But you can still see the light from it.”

The process involves gathering information about the factors and forces—technological, business, sociological, and industry or ecosystem trends—that are effecting change to envision a range of potential impacts.

Seeing New Worlds

Intel, for example, looks beyond its own industry boundaries to envision possible future developments in adjacent businesses in the larger ecosystem it operates in. In 2008, the Intel Labs team, led by anthropologist Genevieve Bell, determined that the introduction of flexible glass displays would open up a whole new category of foldable consumer electronic devices.

To take advantage of that advance, Intel would need to be able to make silicon small enough to fit into some imagined device of the future. By the time glass manufacturer Corning unveiled its ultra-slim, flexible glass surface for mobile devices, laptops, televisions, and other displays of the future in 2012, Intel had already created design prototypes and kicked its development into higher gear. “Because we had done the future casting, we were already imagining how people might use flexible glass to create consumer devices,” says Johnson.

Because future planning relies so heavily on the quality of the input it receives, bringing in experts can elevate the practice. They can come from inside an organization, but the most influential insight may come from the outside and span a wide range of disciplines, says Steve Brown, a futurist, consultant, and CEO of BaldFuturist.com who worked for Intel Labs from 2007 to 2016.

Companies may look to sociologists or behaviorists who have insight into the needs and wants of people and how that influences their actions. Some organizations bring in an applied futurist, skilled at scanning many different forces and factors likely to coalesce in important ways (see Do You Need a Futurist?).

Do You Need a Futurist?

Most organizations need an outsider to help envision their future. Futurists are good at looking beyond the big picture to the biggest picture.

Business leaders who want to be better prepared for an uncertain and disruptive future will build future planning as a strategic capability into their organizations and create an organizational culture that embraces the approach. But working with credible futurists, at least in the beginning, can jump-start the process.

“The present can be so noisy and business leaders are so close to it that it’s helpful to provide a fresh outside-in point of view,” says veteran futurist Bob Johansen.

To put it simply, futurists like Johansen are good at connecting dots—lots of them. They look beyond the boundaries of a single company or even an industry, incorporating into their work social science, technical research, cultural movements, economic data, trends, and the input of other experts.

They can also factor in the cultural history of the specific company with whom they’re working, says Brian David Johnson, futurist in residence at Arizona State University’s Center for Science and the Imagination. “These large corporations have processes and procedures in place—typically for good reasons,” Johnson explains. “But all of those reasons have everything to do with the past and nothing to do with the future. Looking at that is important so you can understand the inertia that you need to overcome.”

One thing the best futurists will say they can’t do: predict the future. That’s not the point. “The future punishes certainty,” Johansen says, “but it rewards clarity.” The methods futurists employ are designed to trigger discussions and considerations of possibilities corporate leaders might not otherwise consider.

You don’t even necessarily have to buy into all the foresight that results, says Johansen. Many leaders don’t. “Every forecast is debatable,” Johansen says. “Foresight is a way to provoke insight, even if you don’t believe it. The value is in letting yourself be provoked.”

External expert input serves several purposes. It brings everyone up to a common level of knowledge. It can stimulate and shift the thinking of participants by introducing them to new information or ideas. And it can challenge the status quo by illustrating how people and organizations in different sectors are harnessing emerging trends.

The goal is not to come up with one definitive future but multiple possibilities—positive and negative—along with a list of the likely obstacles or accelerants that could surface on the road ahead. The result: increased clarity—rather than certainty—in the face of the unknown that enables business decision makers to execute and refine business plans and strategy over time.

Plotting the Steps Along the Way

Coming up with potential trends is an important first step in futuring, but even more critical is figuring out what steps need to be taken along the way: eight years from now, four years from now, two years from now, and now. Considerations include technologies to develop, infrastructure to deploy, talent to hire, partnerships to forge, and acquisitions to make. Without this vital step, says Brown, everybody goes back to their day jobs and the new thinking generated by future planning is wasted. To work, the future steps must be tangible, concrete, and actionable.

Organizations must build a roadmap for the desired future state that anticipates both developments and detours, complete with signals that will let them know if they’re headed in the right direction. Brown works with corporate leaders to set indicator flags to look out for on the way to the anticipated future. “If we see these flagged events occurring in the ecosystem, they help to confirm the strength of our hypothesis that a particular imagined future is likely to occur,” he explains.

For example, one of Brown’s clients envisioned two potential futures: one in which gestural interfaces took hold and another in which voice control dominated. The team set a flag to look out for early examples of the interfaces that emerged in areas such as home appliances and automobiles. “Once you saw not just Amazon Echo but also Google Home and other copycat speakers, it would increase your confidence that you were moving more towards a voice-first era rather than a gesture-first era,” Brown says. “It doesn’t mean that gesture won’t happen, but it’s less likely to be the predominant modality for communication.”

How to Keep Experiments from Being Stifled

Once organizations have a vision for the future, making it a reality requires testing ideas in the marketplace and then scaling them across the enterprise. “There’s a huge change piece involved,”
says Frank Diana, futurist and global consultant with Tata Consultancy Services, “and that’s the place where most
businesses will fall down.”

Many large firms have forgotten what it’s like to experiment in several new markets on a small scale to determine what will stick and what won’t, says René Rohrbeck, professor of strategy at the Aarhus School of Business and Social Sciences. Companies must be able to fail quickly, bring the lessons learned back in, adapt, and try again.

Lowe’s increases its chances of success by creating master narratives across a number of different areas at once, such as robotics, mixed-reality tools, on-demand manufacturing, sustainability, and startup acceleration. The lab maps components of each by expected timelines: short, medium, and long term. “From there, we’ll try to build as many of them as quickly as we can,” says Manna. “And we’re always looking for that next suite of things that we should be working on.” Along the way certain innovations, like the HoloRoom How-To, become developed enough to integrate into the larger business as part of the core strategy.

One way Lowe’s accelerates the process of deciding what is ready to scale is by being open about its nascent plans with the world. “In the past, Lowe’s would never talk about projects that weren’t at scale,” says Manna. Now the company is sharing its future plans with the media and, as a result, attracting partners that can jump-start their realization.

Seeing a Lowe’s comic about employee exoskeletons, for example, led Virginia Tech engineering professor Alan Asbeck to the retailer. He helped develop a prototype for a three-month pilot with stock employees at a Christiansburg, Virginia, store.

The high-tech suit makes it easier to move heavy objects. Employees trying out the suits are also fitted with an EEG headset that the lab incorporates into all its pilots to gauge unstated, subconscious reactions. That direct feedback on the user experience helps the company refine its innovations over time.

Make the Future Part of the Culture

Regardless of whether all the elements of its master narratives come to pass, Lowe’s has already accomplished something important: It has embedded future thinking into the culture of the company.

Companies like Lowe’s constantly scan the environment for meaningful economic, technology, and cultural changes that could impact its future assessments and plans. “They can regularly draw on future planning to answer challenges,” says Rohrbeck. “This intensive, ongoing, agile strategizing is only possible because they’ve done their homework up front and they keep it updated.”

It’s impossible to predict what’s going to happen in the future, but companies can help to shape it, says Manna of Lowe’s. “It’s really about painting a picture of a preferred future state that we can try to achieve while being flexible and capable of change as we learn things along the way.” D!


About the Authors

Dan Wellers is Global Lead, Digital Futures, at SAP.

Kai Goerlich is Chief Futurist at SAP’s Innovation Center Network.

Stephanie Overby is a Boston-based business and technology journalist.


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

Comments

Dan Wellers

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.

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

About Stephanie Overby

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Human Is The Next Big Thing

Traci Maddox

One of my favorite movies of 2016 was Hidden Figures. The main character, Katherine Johnson, and her team of colleagues had an interesting job title: Computer. Here’s what Katherine said about her job: “On any given day, I analyze the binomial levels of air displacement, friction, and velocity. And compute over 10 thousand calculations by cosine, square root, and lately analytic geometry. By hand.”

That was the 1960s. It was amazing work, but work that took hours to complete – and something an in-memory computer could do in a fraction of a second today.

Just as in-memory computing transformed calculating by hand (and made jobs like Katherine’s much easier), digital technologies are transforming the way we work today – and making our day-to-day activities more efficient.

What’s the real impact of technology in today’s workplace?

We are surrounded by technology, both at home and at work. Machine learning and robotics are making their way into everyday life and are affecting the way we expect to engage with technology at work. That has a big impact on organizations: If a machine can do a job safely and more efficiently, a company, nonprofit, or government – and its employees – will benefit. Digital technologies are becoming increasingly more feasible, affordable, and desirable. The challenge for organizations now is effectively merging human talent and digital business to harness new capabilities.

How will jobs change?

What does this mean for humans in the workplace? In a previous blog, Kerry Brown showed that as enterprises continue to learn, human/machine collaboration increases. People will direct technology and hand over work that can be done more efficiently by machine. Does that mean people will go away? No – but they will need to leverage different skills than they have today.

Although we don’t know exactly how jobs will change, one thing is for sure: Becoming more digitally proficient will help every employee stay relevant (and prepare them to move forward in their careers). Today’s workforce demographic complicates how people embrace technology – with up to five generations in the workforce, there is a wide variety in digital fluency (i.e., the ability to understand which technology is available and what tools will best achieve desired outcomes).

What is digital fluency and how can organizations embrace it?

Digital fluency is the combination of several capabilities related to technology:

  • Foundation skills: The ability to use technology tools that enhance your productivity and effectiveness
  • Information skills: The ability to research and develop your own perspective on topics using technology
  • Collaboration skills: The ability to share knowledge and collaborate with others using technology
  • Transformation skills: The ability to assess your own skills and take action toward building your digital fluency

No matter how proficient you are today, you can continue to build your digital IQ by building new habits and skills. This is something that both the organization and employee will have to own to be successful.

So, what skills are needed?

In a Technical University of Munich study released in July 2017, 64% of respondents said they do not have the skills necessary for digital transformation.

Today's workplace reality

These skills will be applied not only to the jobs of today, but also to the top jobs of the future, which haven’t been imagined yet! A recent article in Fast Company mentions a few, which include Digital Death Manager, Corporate Disorganizer, and 3D Printing Handyman.

And today’s skills will be used differently in 2025, as reported by another Fast Company article:

  • Tech skills, especially analytical skills, will increase in importance. Demand for software developers, market analysts, and computer analysts will increase significantly between now and 2025.
  • Retail and sales skills, or any job related to soft skills that are hard for computers to learn, will continue to grow. Customer service representatives, marketing specialists, and sales reps must continue to collaborate and understand how to use social media effectively to communicate worldwide.
  • Lifelong learning will be necessary to keep up with the changes in technology and adapt to our fast-moving lives. Teachers and trainers will continue to be hot jobs in the future, but the style of teaching will change to adapt to a “sound bite” world.
  • Contract workers who understand how businesses and projects work will thrive in the “gig economy.” Management analysts and auditors will continue to be in high demand.

What’s next?

How do companies address a shortage of digital skills and build digital fluency? Here are some steps you can take to increase your digital fluency – and that of your organization:

  • Assess where you are today. Either personally or organizationally, knowing what skills you have is the first step toward identifying where you need to go.
  • Identify one of each of the skill sets to focus on. What foundational skills do you or your organization need? How can you promote collaboration? What thought leadership can your team share – and how can they connect with the right information to stay relevant?
  • Start practicing! Choose just one thing – and use that technology every day for a month. Use it within your organization so others can practice too.

And up next for this blog series – a look at the workplace of the future!

The computer made its debut in Hidden Figures. Did it replace jobs? Yes, for some of the computer team. But members of that team did not leave quietly and continue manual calculations elsewhere. They learned how to use that new mainframe computer and became programmers. I believe humans will always be the next big thing.

If we want to retain humanity’s value in an increasingly automated world, we need to start recognizing and nurturing Human Skills for the Digital Future.

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Traci Maddox

About Traci Maddox

Traci Maddox is the Director of the North America Customer Transformation Office at SAP, where she is elevating customer success through innovation and digital transformation. Traci is also part of the Digital Workforce Taskforce, a team of SAP leaders whose mission is to help companies succeed by understanding and addressing workforce implications of digital technology.