Will A Robot Take Your Marketing Job?

Tom Pick

Are you panicked yet that artificial intelligence (AI) will soon put you out of work? Could a robot take your marketing job? Some of the brightest minds in Silicon Valley are warning of massive job displacement across the economy in the next decade.

But there remain good reasons not to be terribly alarmed. At least, not for a while.

First, the bad news: according to ThinkGrowth.org, “Between nine percent and 47% of jobs are in danger of being made irrelevant due to technological change (in the next 15 years), with the worst threats falling among the less educated.”

Some panelists at SXSW this spring were even more apocalyptic. Bill Gates said, “AI is the biggest threat to the human race. I can’t believe more people are not worried about this.” Steve Wozniak added, “Fast machines will eventually get rid of slow humans.”

There’s no question the nature of work will continue to change. Automation has been gradually displacing human labor since before the industrial revolution. And AI will expand the range of tasks machines can perform through “smart” automation.

Yet the future for workers may not be so bleak after all, particularly in skilled trades and in creative professions like marketing. Here’s why.

Robots can’t make it alone – even in manufacturing

Robots have been used in manufacturing since 1959. And it’s true, automation in general, and robots in particular, have had a significant impact on factory employment. The number of U.S. workers employed in manufacturing fell 39% from its peak in 1977 to 2012. Five million factory jobs have disappeared since 2000, partly due to trade, but primarily due to automation.

However, those trends don’t quite tell the whole story. The United States has actually added 1 million manufacturing jobs since employment in the sector bottomed out in 2010. And growth is continuing. According to the latest figures from the Bureau of Labor Statistics (BLS):

“In February (2017), employment in manufacturing rose by 28,000. The manufacturing diffusion index increased from 50.0 in January to 65.4, its highest level since November 2014. A value above 50 indicates that more component industries gained jobs than lost them.”

Factories are having trouble finding workers – at least finding those with the right skills. In Minnesota, for example, “nearly 5,000 manufacturing jobs are unfilled – a number that will likely grow as more and more employees move into retirement.” And nationwide, Bloomberg projects, “Over the next decade, 3.4 million manufacturing jobs are expected to become available as baby boomers retire and economic growth spurs work opportunities… but a skills gap could result in 2 million of those jobs staying unfilled.”

How is it possible that employment may grow and factories may face (human) worker shortages even as robotics and AI technologies advance? Simple: Automation increases productivity (which increases societal wealth) and makes the United States more competitive globally. We’ll need more workers and more robots.

Driverless vehicles roll forward slowly

A recent U.S. government report, Artificial Intelligence, Automation, and Economy, predicts driverless automated vehicle (AV) technology may eliminate 2.2 to 3.1 million existing U.S. jobs. But any such job losses that occur won’t happen immediately or abruptly. They will be spread out over time.

Further, the report concedes that certain types of drivers (e.g., long-haul truckers transporting goods) are more likely to be replaced than others (school bus drivers transporting children, for example). The study also notes, “New jobs will also likely be created, both in existing occupations – cheaper transportation costs will lower prices and increase demand for goods and all the related occupations such as service and fulfillment – and in new occupations not currently foreseeable.”

And those projected job losses assume AV technology will become reliable and trusted. Though great progress has been made (driverless vehicles are being tested in several cities beyond San Francisco, Detroit, and Pittsburgh), some of the hardest work remains. As the expression among software developers goes, the first 90% of a project takes 10% of the time; and the last 10% of the project takes the other 90%.

AV technology will need to work nearly flawlessly before adoption becomes widespread. Business Insider has reported that lawyers are “salivating” over self-driving cars because they are “going to get a whole host of new defendants,” with deep pockets, in the event of any crashes.

Development of AV technology that works dependably regardless of weather, daylight, and other conditions remains challenging. As Gary Marcus, a best-selling author, entrepreneur, and professor of psychology at NYU, pointed out in TechCrunch regarding AI, “look, for example, at a driverless car; that’s a form of intelligence, modest intelligence, the average 16-year-old can do it, as long as they’re sober, with a couple of months of training. Yet Google has worked on it for seven years and their car still can only drive –  as far as I can tell, since they don’t publish the data … on sunny days, without too much traffic.”

Still, robots and AI already have displaced some workers and will continue to expand into new jobs, particularly those that deal with things rather than people. It will likely be a long time before robots are trusted to care for children or adults with special needs, but they’ve already been running warehouses for years.

Public policy will need to address those job losses, for example with displacement assistance and retraining programs. But standing in the way of AI and robotic progress would be counterproductive (literally); by increasing productivity, they raise living standards across society. Schemes like a robot tax are a bad idea.

So, robots can weld and they can drive – but can they market?

Technology has eliminated wide swaths of employment in the past, from telephone operators and electric typewriter repairers, to photo technicians and video rental store cashiers. It’s now threatening various types of clerks, professional drivers, even insurance underwriters and appraisers.

But AI is more likely to change how marketers work than to replace them. It will supplement the efforts of human workers rather than take their jobs. Why?

First, consider one type of technology already in wide use: marketing automation software. Despite the label, these applications don’t “automate” marketing; they merely enable marketing professionals to set up sequences of email messages that are then automatically sent out using human-defined sequences and branches.

There are marketing professionals, agencies, and consultants who specialize in optimizing the use of marketing automation systems. In the words of Marketing Week, marketing automation platforms “don’t destroy jobs, they just change what jobs are needed.”

Second, there are several distinctly human characteristics essential to marketing that will likely prove vexing to mimic with silicon.

  • Interpretation: An AI-based tool like PaveAI can evaluate 16 million possible correlations within Google Analytics then produce a report showing the most significant findings. But it still requires a human to interpret the results.

For example, knowing that the highest conversion rate correlates with visitors who land on your homepage on a weekday during business hours is about as unsurprising as any data point could be to a B2B marketer. But discovering the lowest conversion rate is associated with a particular section of your website that visitors often reach through organic search is far more interesting, and actionable.

Sentiment analysis presents another type of problem. Words like bomb, sick, mad, bad, and beast are generally considered negative terms to associate with your brand; yet all have, within recent memory, had a positive connotation in slang. People get that (hopefully). Machines will likely struggle.

  • Creativity: Marketing is an almost uniquely left brain and right brain profession. Data analysis, where AI can help, is of course vital.

But emotion plays a significant role in every considered purchase process, impacting both consumer and B2B buying decisions.

The creative side of marketing appeals to our emotions, and that side requires distinctly human creativity. It’s difficult to imagine, for example, even the most sophisticated AI systems coming up with something like E-TRADE’s invest in vests commercial.

  • Originality:  AI can help marketers optimize current channels, but it won’t develop radically new ideas. For example, AI can help optimize and personalize email content – but AI never would have come up with the idea of using email for marketing in the first place (that was Gary Thuerk of Digital Equipment Corporation).

AI may help with optimizing messaging and timing on social networks. But it couldn’t have spontaneously computed Oreo’s famous dunk-in-the-dark tweet… Or suggested creating a profile for KFC’s famous founder on LinkedIn. And it certainly wouldn’t have invented a sporting event to support brand content marketing, as Red Bull has done with Crashed Ice.

  • Perspective: Not every question, in any realm of life, has a clear-cut answer. Even when looking at the same underlying data, reasonable and intelligent people can disagree, based on their beliefs, assumptions, experiences, and definitions – in short, based on their perspective.

For example, is it possible to accurately measure the ROI of social media marketing efforts? AI could provide an answer – and with the right data sources, even perform the calculations – but it couldn’t provide the perspective on the answer that a human thought leader provides.

In marketing content, it’s often the perspective that’s as interesting as the answer. It’s difficult to imagine an AI system weaving a narrative from a unique or interesting perspective. It’s even harder to imagine AI writing this post.

  • Persuasiveness: Great marketing in any form – text, visual, video – combines logic with emotion to move buyers to act. AI has logic literally at its core, but trying to teach AI to understand human emotions has so far been an enormous challenge.

Robots: The new job creators?

An analysis by The Economist on the impact of robots and AI on employment suggests not only that the fear of massive job losses is likely overblown, but that in some cases automation may actually increase the number of jobs for humans. A study of the American job market from 1982 to 2012 found that:

“Employment grew significantly faster in occupations (for example, graphic design) that made more use of computers, as automation sped up one aspect of a job, enabling workers to do the other parts better. The net effect was that more computer-intensive jobs within an industry displaced less computer-intensive ones. Computers thus reallocate rather than displace jobs, requiring workers to learn new skills. This is true of a wide range of occupations…

“So far, the same seems to be true of fields where AI is being deployed. For example, the introduction of software capable of analyzing large volumes of legal documents might have been expected to reduce the number of legal clerks and paralegals, who act as human search engines during the ‘discovery’ phase of a case; in fact, automation has reduced the cost of discovery and increased demand for it. Judges are more willing to allow discovery now, because it’s cheaper and easier… The number of legal clerks in America increased by 1.1% a year between 2000 and 2013.”

The analysis also reiterates that almost every new wave of technology in the past has raised the specter of mass unemployment, only to end up creating more jobs than were destroyed. The term “technological unemployment” sounds like a concept Gates or Wozniak may have devised. The phrase was in fact coined by economist John Maynard Keynes in the 1930s. The total U.S. labor force more than doubled in the following five decades.

In marketing, AI will take over routine and data analysis-intensive tasks, but also create new opportunities for human employees – for example, in training and teaching AI systems. AI is already being used in areas like personalizing product recommendations and more granularly targeting advertising.

But AI requires human training, testing, and teaching both during the implementation phase and on an ongoing basis. Both human testing and human judgment are needed upfront in terms of preparing AI platforms for the real world and determining when they are ready to go live.

A Harvard Business Review article points out the level at which AI systems are “good enough” varies widely by application; a mistake by Alexa or Siri in understanding speech and ordering the wrong item is annoying. A mistake by a self-driving vehicle may be fatal.

Once live, AI platforms – just like a human graduating from college and entering the workforce – need continued training over time to increase their capabilities and stay current with changing tastes and technology. And that means people, as explained in VentureBeat: “AI’s advancement up the value chain is only possible with the aid of human intelligence.”

Historically, technological advancements have always ended up creating more jobs than they destroyed. Today may prove to be different, but for now, it appears robots are more likely to be workplace assistants rather than job terminators. As a marketer, you probably don’t have to worry about robots or AI taking your job. But you will need to be prepared to work with these technologies to do your job better.

For more on marketing in today’s digital-driven environment, read Primed: Prompting Customers to Buy.

Photo: The Adventurist and MOCIST Flickr via Compfight cc

Comments

Guide To The Machine Learning Galaxy: Meet Tomorrow’s Rising Stars

Jessica Schubert

We’ve just begun to scratch the surface of the impact of machine learning on the enterprise. Organizations are applying machine learning algorithms to business processes to automate manual tasks and identify patterns in transactional data to drive strategic decisions. Many applications are focused on efficiency and automation, but that trend is shifting. More and more businesses are using machine learning to develop disruptive new business models.

What does this mean for your organization? Plenty, according to two experts during a recent Deloitte and ASUG Webinar, Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes. Leading the Webinar from Deloitte Consulting LLP were Darwin Deano, principal and chief officer for SAP Leonardo and Denise McGuigan, senior manager and Deloitte reimagine platform leader for Lights Out Finance. Darwin and Denise explained emerging trends, why enterprise resource planning (ERP) software is a hotbed for machine learning, and the potential impact on the workforce.

Making room for your digital twin

Machine learning unleashes the greatest possibilities for the enterprise by amplifying the best of human capabilities. It’s not about replacing humans; it’s about coexistence. In the future, there will be greater opportunities for people who can work with machines, take the information that’s produced, and do something meaningful with it.

Consider the concept of a digital twin. The digital twin is essentially the replication of a process system. In finance, payables transactions and record-to-report tasks require a copious number of journal entries that take a lot of time to input. Those tasks could all be reduced or even eliminated by a machine learning bot.

As a digital twin takes on transactional processes, individuals who performed those tasks are then able to focus their efforts on activities that make better use of their human skills by driving actionable insights. They would need to work alongside the digital twin and activate the resulting insight and analytics.

Unleashing data-driven ERP power

Before we give too much credit to the enabling power of machine learning, keep in mind that it all starts and ends with data. Machine learning is only as good as the algorithm, the algorithm is only as good as the data, and nobody knows the data about your core business better than the people who understand your ERP. Therefore, your people play key roles in identifying opportunities and driving the value of machine learning in your enterprise.

There are emerging roles across business and IT that will be critical to the success of not only designing and implementing, but operating, sustaining, and continuously improving investments in machine learning. Two of these roles are orchestrators and guardians.

Identifying orchestrators and guardians – the new stars

Before the advent of machine learning, organizations valued individual skills with a lot of emphasis on specialization. With machine learning, that emphasis shifts to the people who can put it all together – the orchestrators. Orchestrators help realize the value of machine learning. For example, a finance manager is a classic orchestrator. Finance managers know how order-to-cash flows into the central finance operation and how each individual department interacts with finance. For any machine learning scenario in finance, this manager would help put it all together.

The guardians monitor the effectiveness of machine learning to validate that your model works and to address any uncertainty about machine-driven actions. They’ll safeguard the audit trail, assess the evolution of data, and determine what adjustments need to be made. A supply chain director is a very good guardian who can filter out extraneous noise and verify the merits of machine learning scenarios. These roles and constructs will be increasingly important going forward.

Learn more to prepare for a disruptive future

By bringing together your people, processes, and technology, you can more effectively put machine learning to work for your organization. To learn more about what it takes to build modern machine-intelligence capabilities with a solid ERP foundation, watch a replay of Guide to the Machine Learning Galaxy: How Your ERP Knowledge Enables Value-Driven Intelligent Processes. You’ll explore use cases, industry-specific challenges, and leading practices, as well as how the combination of SAP Leonardo and SAP S/4HANA provides a synergistic digital innovation capability. For more information, contact @demcguigan, @darwindeano, @DeloitteSAP, or visit www.deloitte.com/sap. For more on this topic, read “Underfit Vs. Overfit: Why Your Machine Learning Model May Be Wrong.”

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | FacebookYouTube

Comments

Jessica Schubert

About Jessica Schubert

Jessica Schubert is the director of Global Partner Marketing, Deloitte Alliance Lead, at SAP. Her specialties include strategic partnerships, business alliances, go-to-market strategy, product marketing, and demand generation.

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!

Comments

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.

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

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

Tags:

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.

Comments

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.