The (R)evolution of PLM, Part 3: Using Digital Twins Throughout The Product Lifecycle

John McNiff

In Part 1 of this series we explored why manufacturers must embrace “live” PLM. In Part 2 we examined the new dimensions of a product-centric enterprise. In Part 3 we look at the role of digital twins.

It’s time to start using digital twins throughout the product lifecycle. In fact, to compete in the digital economy, manufacturers will need to achieve a truly product-centric enterprise in which digital twins guide not only engineering and maintenance, but every business-critical function, from procurement to HR.

Why is this necessary? Because product lifecycles are shrinking. Companies are managing ever-growing streams of data. And customers are demanding product individualization. The only way for manufacturers to respond is to use digital twins to place the product – the highly configurable, endlessly customizable, increasingly connected product – at the center of their operations.

Double the insight

Digital twins are virtual representations of a real-world products or assets. They’re a Top 10 strategic trend for 2017, according to Gartner. And they’re part of a broader digital transformation in which IDC says companies will invest $2.1 trillion a year by 2019.

Digital twins aren’t a new concept, but their application throughout the product lifecycle is. Here are key ways smart manufacturers will leverage digital twins – and achieve a product-centric and model-based enterprise – across operations:

Design and engineering: Traditionally, digital twins have been used by design and engineering to create virtual representations for designing and enhancing products. In this application, the digital twin actually exists before its physical counterpart does, essentially starting out as a vision of what the product should be. But you can also capture data on in-the-field product use and apply that to the digital twin for continuous product improvement.

Maintenance and service: Today, the most common use case for digital twins is maintenance and service. By creating a virtual representation of an asset in the field using lightweight model visualization, and then capturing data from smart sensors embedded in the asset, you can gain a complete picture of real-world performance and operating conditions. You can also simulate that real-world environment for predictive maintenance. Let’s say you manufacture wind turbines. You can capture data on rotor speed, wind speed, operating temperature, ambient temperature, humidity, and so on to understand and predict product performance. By doing so, you can schedule maintenance before a crucial part breaks – optimizing uptime and saving time and cost for a repair.

Quality control: Just as digital twins can help with maintenance and service, they can predictively improve quality during manufacturing. You can also use digital twins to compare quality data across multiple products to better understand global quality issues and quickly visualize issues against the model. And you can apply data collected by maintenance and service to achieve ongoing quality improvements.

Customization: As products become more customizable, digital twins will allow design and engineering to model the various permutations. But digital twins can also incorporate customer demand and usage data to enhance customization options. That sounds obvious, but in the past it was very difficult to incorporate customer input into the manufacturing process. Let’s say you sell high-end custom bikes. You might allow customers to choose different colors, wheels, and other details. By capturing customer preferences in the digital twin, you can get a picture of customer demand. And by capturing customer usage data, you can understand how custom configurations affect product performance. So you can offer the most reliable options or allow customers to configure your products based on performance attributes. You can also visualize lightweight representations of the twin without the burden of heavyweight design systems and parameters.

Finance and procurement: In our custom-configured bike example, different configurations involve different costs. And those different costs involve not only the cost of the various components, but also the cost for assembling the various configurations. By capturing sales data in the digital twin, you can understand which configurations are being ordered and how configuration-specific revenues compare to the cost to build each configuration. What’s more, you can link that data with supplier information. That will help you understand which suppliers contribute to product configurations that perform well in the field. It also can help you identify opportunities to cost-effectively rid yourself of excess supply.

Sales and marketing: The digital twin can also inform sales and marketing. For instance, you can use the digital twin to populate an online product configurator and e-commerce website. That way you can be sure what you’re selling is always tied directly to what you’re engineering in the design studio and what you’re servicing in the field.

Human resources: The digital twin can even extend into HR. For example, you can use the digital twin to understand training and certification needs and be sure the right people are trained on the right product features.

One twin, many views

Digital twins should underlie all manufacturing operations. Ideally you should have a single set of digital twin master data that resides in a central location. That will give you one version of the truth, and with “in-memory” computing-based networks plus a lightweight, change-controlled model capability, you’ll be able to analyze and visualize that data rapidly.

But not all business functions care about the entire data set. You need to deliver the right data to the right people at the right time. Design and engineering requires one set of data, with every specification and tolerance needed to create and continuously improve the product. Sales and marketing requires another set of data, with the features and functions customers can select. And so on.

Ultimately, as the digital product innovation platform extends the dimensions of traditional PLM, at the heart of PLM is an extended version of the digital twin. In future blogs we’ll talk about how you can leverage the latest-generation platform from SAP, based on SAP S/4HANA and SAP’s platform for the Internet of Everything, to achieve a live, visual, and intelligent product-centric enterprise.

Learn how a live supply chain can help your business, visit us at SAP.com.

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John McNiff

About John McNiff

John McNiff is the Vice President of Solution Management for the R&D/Engineering line-of-business business unit at SAP. John has held a number of sales and business development roles at SAP, focused on the manufacturing and engineering topics.

Analytics Leaders In Finance Have Higher Profitability

Tom Groenfeldt

The phrase “Big Data is the new oil,” is looking more than a little shopworn. Most organizations have far more data than they know how to use, write Tom Davenport and Jeanne G. Harris in the recently reissued and updated book Competing on Analytics – the New Science of Winning.

”The data in their systems is like the box of photos you keep in your attic, waiting for the ‘someday’ when you impose meaning on the chaos. IDC estimates that only 0.5% of data is ever analyzed, and we would guess that the amount of data is growing faster than the amount of it that’s analyzed.”

The International Institute for Analytics, co-founded by Davenport, conducted a survey in 2016 of 50 companies across several industries and found that few were analytical competitors. Amazon, no surprise, had the highest score, and financial services firms were the second most analytical industry, but on average banks were not at the analytical competitor level. The lowest ranked industries were healthcare and health insurance.

The top ranked had four key characteristics:

  • They supported a strategic capability
  • Their approach was enterprise-wide
  • The senior management team was committed
  • The company made a significant strategic bet on analytics-based competition

Some of the names in finance will not be a surprise. Progressive Insurance and Capital One have been leaders in using credit scores to improve profits. Progressive was early to understand that people with good credit scores also had better driving records than people with poor credit. But then Progressive, like Capital One, began picking apart those same FICO credit scores looking for potential customers who were better risks than their scores indicated, picking up profitable business that competitors couldn’t recognize.

Central control and standardization

Analytical leaders don’t leave data and analytics to departments; they hold it centrally or impose uniform standards for how it is gathered, stored, and used.

RBC Financial Group decided in the 1970s that customer data would be owned by the enterprise and held centrally. Bank of America decided that interest rate exposure would be managed in a consistent way across the bank.

That emphasis on an enterprise-wide approach is fundamental to competing on analytics because departmental analytics tend to rely on Excel. User-generated spreadsheets often have errors, and by their nature create multiple versions of the truth. When business users meet and bring their own spreadsheets, discussions often get bogged down in whose data is accurate. Analytics programs often have to overcome fiefdoms within the organization, the authors write.

Firms that get it right see significant profits. Kroger uses the customer analytics tool Dunnhumby, which was so useful to the British grocer Tesco that it bought the company. Kroger grew its same-store sales for 52 straight quarters through its customer loyalty program and made millions selling shopping data to food companies. It should be interesting to see what happens as Kroger meets Amazon and Whole Foods.

Capital One saw earnings per share and return on equity grow 20% year on year through its aggressive analytics. The bank runs about 80,000 marketing experiments per year. It increased retention in its savings business by 87% and lowered the cost of acquiring by 83%.

The authors quote Bain, which said that really good analytics companies are twice as likely to be in the top quartile, three times as likely to execute decisions as expected, and five times as likely to make decisions faster.

Commitment to long-term effectiveness

The leaders are not standing still. The CIO of Capital One looks forward to using machine learning to provide more tailored products for customers. Credit Suisse is using Quill from Narrative Science to produce investment research reports on 5,000 companies it covers.

Becoming analytical takes time and sustained commitment. The authors say it can take 18 to 36 months of working with data to get into the practice, and sometimes the process slips. They recount a conversation with a banker whose firm used to be a leader in analytics, but now said the organization was slipping into silos of data. That can lead to unfortunate cases like one where a customer with a $100 million trust account was charged $35 for a bounced check. A manager, who apparently couldn’t see the trust account, said the customer’s savings account wasn’t large enough to justify waiving the fee.

Analytics is easier at digital corporations like eBay or Netflix than at “a legacy corporation where IT appears to have been built in a series of weekend handyman jobs,” they note.

For more on trends shaping the future of finance, read The Digitalist’s Transformation Ahead Series.

This article originally appeared on Financial Technology.

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Tom Groenfeldt

About Tom Groenfeldt

Tom Groenfeldt is a freelance reporter who focuses largely on finance and technology including trading, risk, back-office systems, big data, analytics, retail banking, international banking, and e-commerce. His work appears in several publications, including Forbes.com in the U.S. and Banking Technology in London. In 2015, he was named to the “FinServ 25,” the top 25 top global influencers in banking, by The Financial Brand.

Transformation Ahead, Part 3: 10 Trends Shaping The Future Of Finance

Randy Garrison

Part 3 of the of the 4-part “Transformation Ahead” series

As I wrote in my previous blogs in this series, 10 key trends are significantly changing the future of finance. To ensure that their finance organizations keep pace with the change demanded by the business and catalyzed by disruptive technologies, CFOs must be prepared to address these trends.

Trend 5. Finance will become the analytics hub of the organization.

Successful finance organizations are focusing on enterprise-level analytics and key performance indicators, not just information related to the finance function. To support this shift, the finance organization will need to develop a deep understanding of the business and the ability to translate this knowledge into financial and operational metrics.

Day-to-day analytics should provide self-service for business users. With artificial intelligence and natural language processing making analytics tools more intuitive for users, the finance organization can step away from being analytics experts and instead focus on becoming strategic partners to the business. Finance experts can help the business interpret analytics and discover trends, opportunities, or the root cause of issues. With the use of predictive algorithms, finance organizations can help shape strategy by performing what-if comparisons across multiple scenarios.

Trend 6. Core finance processes will become dynamic, continuous processes.

Too slow to support real-time digital businesses, traditional processes that focus on month-end close and annual budget cycles must be replaced. Instead, accounting is evolving into a continuous activity, performed primarily through automation and business networks. In this environment, closing the books will be just another task – not a cause for stress or overtime.

Other than external reporting for quarterly or annual results, the close will no longer be needed to support financial performance evaluation for the business. Budgeting will also become a dynamic activity, one that supports rapid adjustments to meet changing business requirements. For more on this topic, read the Continuous Accounting Series in the Digitalist.

Trend 7. Finance will be viewed as a business value creator.

As automation and trends such as AI, machine learning, and networking reduce the manual effort required for transactional functions, CFOs will be able to add more top- and bottom-line value by focusing more effort on complex topics such as transfer pricing optimization, proactive working capital management, tax optimization, and proactive currency and commodity hedge management. Detailed, timely insights will equip the finance organization to create data-driven value for the business.

My next blog will conclude with a look at three more significant trends – and my recommendations for how to get ready and take charge. You can read more about finance solutions at www.sap.com/cfo.

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

 

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Randy Garrison

About Randy Garrison

Randy Garrison is vice president, Global Line of Business Finance and Head of Value Advisory at SAP. The LoB Finance organization is responsible for the full suite of SAP solutions for the Office of the CFO.

Randy has held several roles at SAP, most recently in leadership within SAP’s Services business. In these roles, he has led both large and small teams focused on analytics strategy, data strategy, business transformation, Big Data, and so on, focused on the implementation, adoption, and value realization of SAP’s products.

Randy is a Certified Public Accountant, Certified Management Accountant, Chartered Global Management Accountant, and a member of the AICPA and the Institute of Management Accountants.

He is married with five children ranging in age from 32 to 7 years old. Personal interests include golf, hot air ballooning, anything the kids do.

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.

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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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The Human Factor In An AI Future

Dan Wellers and Kai Goerlich

As artificial intelligence becomes more sophisticated and its ability to perform human tasks accelerates exponentially, we’re finally seeing some attempts to wrestle with what that means, not just for business, but for humanity as a whole.

From the first stone ax to the printing press to the latest ERP solution, technology that reduces or even eliminates physical and mental effort is as old as the human race itself. However, that doesn’t make each step forward any less uncomfortable for the people whose work is directly affected – and the rise of AI is qualitatively different from past developments.

Until now, we developed technology to handle specific routine tasks. A human needed to break down complex processes into their component tasks, determine how to automate each of those tasks, and finally create and refine the automation process. AI is different. Because AI can evaluate, select, act, and learn from its actions, it can be independent and self-sustaining.

Some people, like investor/inventor Elon Musk and Alibaba founder and chairman Jack Ma, are focusing intently on how AI will impact the labor market. It’s going to do far more than eliminate repetitive manual jobs like warehouse picking. Any job that involves routine problem-solving within existing structures, processes, and knowledge is ripe for handing over to a machine. Indeed, jobs like customer service, travel planning, medical diagnostics, stock trading, real estate, and even clothing design are already increasingly automated.

As for more complex problem-solving, we used to think it would take computers decades or even centuries to catch up to the nimble human mind, but we underestimated the exponential explosion of deep learning. IBM’s Watson trounced past Jeopardy champions in 2011 – and just last year, Google’s DeepMind AI beat the reigning European champion at Go, a game once thought too complex for even the most sophisticated computer.

Where does AI leave human?

This raises an urgent question for the future: How do human beings maintain our economic value in a world in which AI will keep getting better than us at more and more things?

The concept of the technological singularity – the point at which machines attain superhuman intelligence and permanently outpace the human mind – is based on the idea that human thinking can’t evolve fast enough to keep up with technology. However, the limits of human performance have yet to be found. It’s possible that people are only at risk of lagging behind machines because nothing has forced us to test ourselves at scale.

Other than a handful of notable individual thinkers, scientists, and artists, most of humanity has met survival-level needs through mostly repetitive tasks. Most people don’t have the time or energy for higher-level activities. But as the human race faces the unique challenge of imminent obsolescence, we need to think of those activities not as luxuries, but as necessities. As technology replaces our traditional economic value, the economic system may stop attaching value to us entirely unless we determine the unique value humanity offers – and what we can and must do to cultivate the uniquely human skills that deliver that value.

Honing the human advantage

As a species, humans are driven to push past boundaries, to try new things, to build something worthwhile, and to make a difference. We have strong instincts to explore and enjoy novelty and risk – but according to psychologist Mihaly Csikszentmihalyi, these instincts crumble if we don’t cultivate them.

AI is brilliant at automating routine knowledge work and generating new insights from existing data. What it can’t do is deduce the existence, or even the possibility, of information it isn’t already aware of. It can’t imagine radical new products and business models. Or ask previously unconceptualized questions. Or envision unimagined opportunities and achievements. AI doesn’t even have common sense! As theoretical physicist Michio Kaku says, a robot doesn’t know that water is wet or that strings can pull but not push. Nor can robots engage in what Kaku calls “intellectual capitalism” – activities that involve creativity, imagination, leadership, analysis, humor, and original thought.

At the moment, though, we don’t generally value these so-called “soft skills” enough to prioritize them. We expect people to develop their competency in emotional intelligence, cross-cultural awareness, curiosity, critical thinking, and persistence organically, as if these skills simply emerge on their own given enough time. But there’s nothing soft about these skills, and we can’t afford to leave them to chance.

Lessons in being human

To stay ahead of AI in an increasingly automated world, we need to start cultivating our most human abilities on a societal level – and to do so not just as soon as possible, but as early as possible.

Singularity University chairman Peter Diamandis, for example, advocates revamping the elementary school curriculum to nurture the critical skills of passion, curiosity, imagination, critical thinking, and persistence. He envisions a curriculum that, among other things, teaches kids to communicate, ask questions, solve problems with creativity, empathy, and ethics, and accept failure as an opportunity to try again. These concepts aren’t necessarily new – Waldorf and Montessori schools have been encouraging similar approaches for decades – but increasing automation and digitization make them newly relevant and urgent.

The Mastery Transcript Consortium is approaching the same problem from the opposite side, by starting with outcomes. This organization is pushing to redesign the secondary school transcript to better reflect whether and how high school students are acquiring the necessary combination of creative, critical, and analytical abilities. By measuring student achievement in a more nuanced way than through letter grades and test scores, the consortium’s approach would inherently require schools to reverse-engineer their curricula to emphasize those abilities.

Most critically, this isn’t simply a concern of high-tuition private schools and “good school districts” intended to create tomorrow’s executives and high-level knowledge workers. One critical aspect of the challenge we face is the assumption that the vast majority of people are inevitably destined for lives that don’t require creativity or critical thinking – that either they will somehow be able to thrive anyway or their inability to thrive isn’t a cause for concern. In the era of AI, no one will be able to thrive without these abilities, which means that everyone will need help acquiring them. For humanitarian, political, and economic reasons, we cannot just write off a large percentage of the population as disposable.

In the end, anything an AI does has to fit into a human-centered value system that takes our unique human abilities into account. Why would we want to give up our humanity in favor of letting machines determine whether or not an action or idea is valuable? Instead, while we let artificial intelligence get better at being what it is, we need to get better at being human. That’s how we’ll keep coming up with groundbreaking new ideas like jazz music, graphic novels, self-driving cars, blockchain, machine learning – and AI itself.

Read the executive brief Human Skills for the Digital Future.

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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