Dealing with today’s digital disruption begins by understanding how it differs from past industry changes. After all, stories of the end of our industry as we know it have been a trade press staple for decades. A few key elements distinguish this era of change from the past.
Disruption has accelerated dramatically, and the numbers prove it.
A 2014 study from Constellation Research quantified the accelerating rate of change in the enterprise by examining a simple benchmark — the entry and exit of U.S. corporations in the S&P 500 index.
In 1958, corporations listed in the S&P 500 had an average stay of 61 years. By 1980, numbers from research firm Innosight reveal that the average stay had declined sharply to 25 years. In 2011, the average tenure dropped to 18 years. At the present rate of churn, Innosight’s research estimates three-quarters of today’s S&P 500 will be replaced by 2027.
Digital disruption is the primary catalyst of change.
While the Constellation study is careful to say that companies rise and fall for many reasons, digital disruption is clearly responsible for a large share. Research shows that since 2000, 52 percent of companies in the Fortune 500 have either gone bankrupt, been acquired, or ceased to exist as a result of digital disruption. The collision of the physical and digital worlds has affected every dimension of society, commerce, enterprises, and individuals.
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Digital transformation transcends technology.
Digital transformation is often viewed through a narrow technology lens, as just another mobile project or e-commerce initiative. Fundamentally, though, digital transformation is the result of enterprises seeking to adapt to the storm of new technology affecting markets and customers. Effective internal systems, processes, and value chains will always be essential, but enterprises will increasingly need to harness the skills, capabilities, and passions of the external market. Digital transformation forces wholesale change to the foundations of an enterprise — from its operating model to its infrastructure, what it sells, and to whom and how.
No industry is immune.
Industries dominated by information-rich assets (think publishing and music) were swept up in the early wave of internet innovation. The subsequent mobile revolution created challenges for retailers who found customers flocking to online alternatives. Today, disruptive technology shifts such as cloud, big data, and the Internet of Things will not only upend these industries (again), but will also introduce revolutionary change to even the most staid industries. Specific industries with regulatory barriers or large infrastructure costs will feel greater effects of shifts created by the next generation of IT breakthroughs.
There is significant opportunity.
While the disruption is immense, so is the opportunity. The value of the digital economy continues to grow in size and importance in every company in every industry. Nearly 3 billion consumers, businesses, government agencies, and institutions of every nature interact every day using computers, laptops, tablets, smartphones, and a growing range of mobile devices. The relentless speed of change of customers, markets, and technology has given rise to enormous opportunity.
The digital economy is making significant contributions to global gross domestic product (GDP), outpacing global growth by 400 percent. A 2015 report by Boston Consulting Group (BCG) estimated the digital economy would contribute $4 trillion to the GDP of countries in the G-20 during 2016, and would continue to grow at a rate of 10 percent per year. Growth outside the G-20 is even higher, measured at rates of 15 to 25 percent. These figures compare with a rate of 2.5 percent global economic growth, according to recent estimates by the Conference Board. BCG’s study also credits the digital economy as an increasingly important source of new jobs, as well as an important catalyst for social and political change.
Successful companies have learned that digital disruption is more than a catalyst of unrelenting change. It is also the foundation on which can be built new business strategies that are able to move and evolve at the pace of consumers and markets. These are companies that have embraced transformation as a way of life and are using digital technologies to accelerate their growth.
How can companies thrive on these changes? Get the keys to digital transformation success here.
Erik Brynjolfsson, MIT Sloan School professor, explains how rapid advances in machine learning are presenting new opportunities for businesses. He breaks down how the technology works and what it can and can’t do (yet). He also discusses the potential impact of AI on the economy, how workforces will interact with it in the future, and suggests managers start experimenting now. Brynjolfsson is the co-author, with Andrew McAfee, of the HBR Big Idea article, “The Business of Artificial Intelligence.” They’re also the co-authors of the new book, Machine, Platform, Crowd: Harnessing Our Digital Future.
There are many ways to put data to work, and companies, and especially their leaders, are advised to explore as many of them as they can. Each presents distinct opportunities for profit and competitive advantage, from product improvements to new revenue streams to possible industry game changers. At the same time, each presents challenges that must be experienced to be appreciated.
While big data, analytics, artificial intelligence, and the internet of things garner the lion’s share of media attention, using data to its full potential is much more about management than it is about technology. A team of data scientists may employ a series of clever analyses to yield an important insight, but that insight will die on the vine if others in the organization don’t carry it forward by developing a deeper understanding of the implications, making a critical decision, building it into a product, or leveraging it in interactions with customers. Putting data to work includes the whole sequence, from data to insight to profit.
Sponsored by AccentureAnalytics are critical to companies’ performance.
In working with companies on getting more from their data, I advise managers to explore seven methods to put data to work. I also urge all leaders to initiate department- or business unit–size trials of all these methods, so they can learn how the options work and which would be best for their business.
- Make better decisions. First, use better (more relevant, more accurate) data when making decisions, up and down the organization chart. I’ve not worked with or heard of a company that didn’t freely admit that it needed to make better decisions — and many push hard to improve. But incorporating more and better data into decision making can be difficult. You must learn to understand variation, to combine data from different sources, and to drive decision making to the lowest possible level. By taking the time to learn these skills, though, you can use data to reduce uncertainty, increasing the chances of making sound decisions.
- Innovate products, services, and processes. Use data to uncover hidden insights, and use those insights to create or improve products, services, and processes. For example, at Morgan Stanley, Jeff McMillan and his team aim to improve working relationships with their wealth management clients by analyzing everything from client goals and portfolios to available investment products to email. An algorithm then takes this information and suggests actions, at which point advisors choose the best ones to suggest to their clients. McMillan encourages advisors to “imagine you have a conversation at 6:00 PM every evening with a Harvard MBA with 800 years’ experience. You tell her what you’re thinking about, and she thinks through your clients’ opportunities all night long. In the morning, she presents you with a list of your 10 best actions for the day. Wouldn’t that help you make your clients happier?” Their goal is to develop personalized strategies for each client based on far more data and analytic horsepower than any financial adviser could marshal alone.
- Informationalize products, services and processes. Build more data into what you offer customers, so you make existing products more valuable. Automobile manufacturers have a history of working on this by adding warning lights, GPS, distance-to-empty gas tank notifications, and other features almost seamlessly. I’ve yet to run across a product or process that wouldn’t benefit from more data.
- Improve quality, eliminate costs, and build trust. Proactively address quality by finding and eliminating the root causes of errors. Virtually everything a company does, from delivering products to running the place, uses enormous quantities of data. But bad data makes this work more difficult and increases costs — up to 20% of revenue! You can’t expect someone to factor data they don’t trust into an important decision. Take steps to actively track down data quality issues and eliminate their root causes.
- Provide content. Sell or license new, richer, or more targeted data. All customers depend on content, and thousands of companies, such as Bloomberg and 23andMe, aim to fill the need. Still, most companies don’t think much about selling their data. But doing so can provide great opportunity. For example, car insurance companies discovered a relatively simple piece of data they could sell: the number of new policies written each day. New car sales reflect the health of automobile manufacturers and are of great interest to investors. But manufacturers release sales figures monthly — an eternity for investors. Since each sale requires a new insurance policy, the number of new policies issued each day provides a faster indicator. This becomes a profit stream for the issuers and for Quandl, which aggregates this data across the industry and packages it for investors.
- Infomediate. Connect data providers and those who need the data. Here, the goal is not to provide content but to provide direction toward content. Google is, of course, the best-known example, but Quora, too, helps people find answers when expert help is needed. And there is huge opportunity here for others. In both their personal and professional lives, individuals spend hours each week looking for documents, reports, and other data. Find ways to connect these individuals with others who can provide the answers they’re looking for.
- Exploit asymmetries. An asymmetry arises when one side of a transaction knows something that the other doesn’t. Exploiting this knowledge helps them drive a better deal. Hedge funds and used car dealers use such data to create and leverage asymmetries. More recently, sports venues, airlines, and others have begun using variable pricing to capture maximum revenue from consumers. All companies can examine sales and related data more deeply in search of such opportunities. Conversely, closing asymmetries, as Carfax does for used cars, can also present great opportunities.
Each of these seven options can help your company put data to work, and in many cases a combination of these approaches can create incredible value. For example, Liz Kirscher, head of talent acquisition at Morningstar, and her team are rethinking their hiring process, looking more closely at existing data, incorporating new data, and bringing more discipline throughout. Important prongs of Kirscher’s approach include innovating by using artificial intelligence to better screen resumes; informationalizing by using the Hogan score to better understand “grit” (a predictor of success at Morningstar); and making better decisions, bringing greater transparency to the measurement of hiring success and failure.
As you explore these approaches, you’ll find that some work better or provide greater value than others. You may discover, for instance, that your decision making improves with more data (especially once you’ve fixed some underlying quality issues), but that selling customer data goes against your company values. Or that it’s relatively easy to make incremental improvements to products through informationalization, but you don’t yet have the right talent for larger innovation initiatives. Test options and learn quickly! And as you do, crystallize those ways of putting data to work that create the most value and profit for your company, and implement them in your long-term data strategy.
Many conversations about data and analytics (D&A) start by focusing on technology. Having the right tools is critically important, but too often executives overlook or underestimate the significance of the people and organizational components required to build a successful D&A function.
When that happens, D&A initiatives can falter — not delivering the insights needed to drive the organization forward or inspiring confidence in the actions required to do so. The stakes are high, with International Data Corporation estimating that global business investments in D&A will surpass $200 billion a year by 2020.
A robust, successful D&A function encompasses more than a stack of technologies, or a few people isolated on one floor of the building. D&A should be the pulse of the organization, incorporated into all key decisions across sales, marketing, supply chain, customer experience, and other core functions.
What’s the best way to build effective D&A capabilities? Start by developing a strategy across the entire enterprise that includes a clear understanding of what you hope to accomplish and how success will be measured.
Sponsored by AccentureAnalytics are critical to companies’ performance.
One of the major American sports leagues is a good example of an organization that is making the most of its D&A function, applying it in scheduling to reduce expenses, for example, reducing the need for teams to fly from city to city for games on back-to-back nights. For the 2016–2017 season, thousands of constraints needed to be taken into account related to travel, player fatigue, ticket revenue, arena availability, and three major television networks. With 30 teams and 1,230 games in a regular season stretching from October into April, trillions of scheduling options were possible.
The league used D&A to arrive at a schedule that:
- reduced the number of games teams played on consecutive nights by 8.4%
- reduced instances of teams playing four games in five days by 26%
- reduced instances of teams playing five games in seven days by 19%
- increased the number of consecutive games teams played without traveling by 23%
- allowed each team to appear on one of the league’s premier TV networks at least once, a success that had not been achieved in the league in any prior year
Technology aside, keys to success included a clear strategy for building the new scheduling system and a commitment across the organization to seeing it through with an unwavering eye on improving the experiences for everyone involved — including players, fans, referees, and TV networks.
Companies can follow the league’s lead by first understanding that successful D&A starts at the top. Make sure leadership teams are fully immersed in defining and setting expectations across the entire organization. Avoid allowing strategy setting and decision making to occur in organizational silos, which can produce shadow technologies, competing versions of the truth, and data analysis paralysis. Before starting any new data analysis initiative, ask: Is the goal to help improve business performance? Jumpstart process and cost efficiency? Drive strategy and accelerate change? Increase market share? Innovate more effectively? All of the above?
When answering these questions, it’s important to understand that D&A teams are not data warehouses that perform back-office functions. Your D&A function should be a key contributor to the development and execution of the business strategy by supplying insights into key areas, such as employees and customers, unmet market opportunities, emerging trends in the external environment, and more.
Leadership teams must recognize that being successful will take courage, because once they embark on the journey, the insights from data analytics will often point to the need for decisions that could require a course correction. Leaders need to be honest with themselves about their willingness to incorporate the insights into their decision making, and hold themselves and their teams accountable for doing so.
Cultural resistance can also become a bigger obstacle than anticipated. But it’s underscored by the findings of two recent studies showing that just 51% of C-suite executives fully support their organization’s D&A strategy. Gartner estimates D&A projects falter 60% of the time. Why? We’ve observed that it’s often because they are not supported by the right organizational structure and talent and are not aligned with the business strategy.
Some organizations have D&A capabilities spread across functions, or rely on a few data scientists to provide insights. Some are too reliant on technology tool kits and rigid architectures, and not enough on creating the right environment to effectively leverage people with the right expertise to drive D&A projects forward. These sorts of models usually are not capable of achieving truly transformative D&A.
Consider the case of a large global life sciences company that spent a significant sum of money building an advanced analytics platform without first determining what it was supposed to do. Executives allowed their technology team to acquire a lot of products, but no one understood what the advanced tools were supposed to accomplish or how to use them. Fortunately, executives recognized the problem before it was too late, conducting an enterprise-wide needs assessment and rebuilding the platform in a way that inspired confidence in its ability to drive efficiency and support business transformation.
In another case, a major financial services organization built a robust technology platform based on stakeholder needs. But after building it, executives soon discovered they lacked the organizational structure and people to use the platform successfully. Once they addressed those needs, the company was able to use the great platform to achieve significant savings in operating costs.
According to KPMG’s 2016 CIO Survey, data analytics is the most in-demand technology skill for the second year running, but nearly 40% of IT leaders say they suffer from shortfalls in skills in this critical area. What’s more, less than 25% of organizations feel that their data and analytics maturity has reached a level where it has actually optimized business outcomes, according to International Data Corporation.
Formally structured systems, processes, and people devoted to D&A can be a competitive advantage, but clearly many organizations are missing this big opportunity. In our experience, companies that build a D&A capability meeting their business needs have teams of data and software engineers who are skilled in the use of big data and data scientists who are wholly focused on a D&A initiative.
While structures vary, the team should be seamlessly integrated with the company’s existing providers and consumers of D&A, operating in cohesion with non-D&A colleagues — people who really understand both the business challenges and how the business works — to set and work toward realistic and relevant strategic goals. The teams should also have the complete support of executive leadership, and their goals should be fully aligned with the business strategy.
In an age where data is created on a scale far beyond the human mind’s ability to process it, business leaders need D&A they can trust to inform their most important decisions — not just to reduce costs but also to achieve growth. And the best will use D&A to anticipate what their customers will want or need before they even know they want or need it.
Cat Yu for HBR
In the world of marketing, brand anthropomorphism can be a powerful mechanism for connecting with consumers. It’s the tactic of giving brand symbols people-like characteristics: Think of Tony the Tiger and the Michelin Man. Today some companies are taking brand anthropomorphism to a whole new level with sophisticated AI technologies.
Consider advanced chatbots, like Apple’s Siri, Amazon’s Alexa, and Microsoft’s Cortana. Thanks to the simplicity of their conversational interfaces, it’s quite possible that customers will spend increasingly more time engaged with a company’s AI than with any other interface, including the firm’s own employees. And over time Siri, Alexa, and Cortana, and their individual “personalities,” could become even more famous than their parent companies.
The implications are numerous. As chatbots and other AI technologies increasingly become the face of many brands, those companies will need to employ people with new types of expertise to ensure that the brands continue to reflect the firm’s desired qualities and values. Executives should also be wary of how AI increases the dangers of brand disintermediation. As brands assume more and more AI functionality, businesses must proactively manage any potential ethical and legal concerns.
To study those issues and others, we surveyed how AI is being implemented at more than 1,000 global companies. We found that many of those firms are already using (or have been experimenting with) AI to orchestrate the brand experience across a number of business processes. These include customer service (39% of companies), marketing and sales (35%), and even the managing of noncustomer external relationships (28%) where brand power is key, such as in attracting top talent into the organization’s recruiting pipeline. Studying those deployments led to several insights around three new types of decisions executives face at the intersection of technology, personality, and strategy.
The first is that chatbots are just one type of AI technology being used to establish or reinforce company brands. In fact, there’s a spectrum of intelligent personalities and “form factors” (such as screens, voices, physical “boxes” like Amazon Echo, text, and so on) that companies are using to deliver a brand experience. Cognitive agents like IPsoft’s Amelia are incarnated as virtual people on a user’s computer screen, and future advances may deploy hologram technology to make those agents even more lifelike. In Hong Kong Hanson Robotics is developing robots with human features. Those robots, which can see and respond to facial expressions and are equipped with natural language processing, could become the literal front-office brand ambassadors for companies.
Whatever the form factor, companies must skillfully manage any future shifts in customer interactions. Remember that each interaction provides an opportunity for a customer to judge the AI system and therefore the brand and company’s performance. In the same way that people can be delighted or angered by an interaction with a customer service representative, they can also form a lasting impression of a chatbot, physical robot, or other AI system. What’s more, the interactions with AI can be more far-reaching than any one-off conversation with a salesperson or customer service rep: A single bot incarnated on myriad devices, for example, can theoretically interact with tens of thousands of people at once. Because of that, good and bad impressions may have long-term, global reach.
How to Properly Rear Your Brand Ambassador
Executives need to make judicious decisions about their use of an anthropomorphic brand ambassador — its name, voice, personality, and so forth. IBM’s Watson converses in a male voice; Cortana and Alexa use female ones. Siri and the nameless AI of Google Home can use either. And what qualities will best represent the values of the organization? The personalities of all these assistants seem helpful, like a nerdy friend, ready with lots of information or a G-rated joke, yet still a bit stilted — perhaps because they take everything we say so literally. It can also be hard to believe they’re as remorseful as they say when they can’t answer our questions or understand our commands.
And then there are important differences. Alexa comes across as confident and considerate — she doesn’t repeat profanity and doesn’t even use slang very often. Siri, on the other hand, is sassy: Her personality is smart and witty with a slight edge, and she is prone to cheeky responses. When asked about the meaning of life, she might respond, “I find it odd that you would ask this of an inanimate object.” Siri can also become jealous, especially when users confuse her with another voice-search system. When someone makes that mistake, her retort is something along the lines of, “Why don’t you ask Alexa to make that call for you?” All this is very much in keeping with the Apple brand, which has long espoused individuality over conformity. Indeed, Siri seems more persona than product.
It might seem flippant to suggest that AI systems will need to develop specific personalities, but consider how a technology like Siri or Alexa has already become so closely associated with the Apple and Amazon brands. It’s no surprise then that “personality training” is becoming such a serious business, and people who perform that task can come from a variety of backgrounds. Take, for example, Robyn Ewing, who used to develop and pitch TV scripts to film studios in Hollywood. Now Ewing is deploying her creative talents to help engineers develop the personality of Sophie, an AI program in the health care field. As one of its tasks, Sophie reminds consumers to take their medications and regularly checks with them to see how they’re feeling. At Microsoft, a team that includes a poet, a novelist, and a playwright is responsible for helping to develop Cortana’s personality. In other words, executives may need to think about how best to attract and retain different types of talent that they never needed before.
In the future, companies might even be incorporating sympathy into their AI systems. That may sound far-fetched, but the startup Koko, which sprung from the MIT Media Lab, has developed a machine learning system that can help chatbots like Siri and Alexa respond with sympathy and depth to people’s questions. Humans are now training the Koko algorithm to respond more sympathetically to people who might, for example, be frustrated that their luggage has been lost, that the product they purchased is defective, or that their cable service keeps on going on the blink. The goal is for the system to be able to talk people through a problem or difficult situation using the appropriate amount of empathy, compassion, and maybe even humor.
The Curious Incident of Brand Disintermediation
As AI systems increasingly become the anthropomorphic faces of many brands, those brands will evolve from one-way interactions (brand to consumer) to two-way relationships. Furthermore, as those systems become increasingly capable, they could potentially lead to brand disintermediation. Alexa, for example, can already orchestrate a number of interactions on behalf of other companies — allowing people to order pizzas from Domino’s, check their Capital One bank balance, and obtain the status updates of Delta flights. In the past, companies like Domino’s, Capital One, and Delta owned the entire customer experience with their customers. Now, with Alexa, Amazon owns part of that information exchange and controls a fundamental interface between those companies and their customers, and it can use that data to improve its own services. This might be one reason why Capital One, which initially had built capability on top of Alexa, recently developed and introduced its own chatbot, Eno.
And then there are ethical challenges. Amazon, for example, recently added a camera to its Alexa/Echo platform so the company can use its AI technology to offer personality-driven fashion advice. But what are the ethical issues of potentially collecting photos of barely dressed consumers? And as these AI systems become increasingly adept at communicating, they could appear to act as a trusted friend ready with sage or calming advice. Have companies adequately considered how such applications should respond to questions that are deeply personal? What if a person admits to suicidal feelings or recent physical abuse? A 2016 JAMA Internal Medicine study looked at how well Siri, Cortana, Google Now, and S Voice from Samsung responded to various prompts that dealt with mental or physical health issues. The researchers found that the bots were inconsistent and incomplete in their ability to recognize a crisis, respond with respectful language, and refer the person to a helpline or health resource. For companies that are implementing such AI systems, an in-house ethicist could help navigate the complex moral issues.
With many new innovations, the technology often gets ahead of businesses’ ability to address the various ethical, societal, and legal concerns involved. With AI, any issues become all the more pressing as those systems increasingly become the face of many company brands. As Amazon CEO Jeff Bezos once remarked, “Your brand is what other people say about you when you’re not in the room.” And that would presumably hold true even if your AI system might be listening.
Rapid advances in robotics and artificial intelligence are making inroads in the workplace, with machines carrying out physical and cognitive activities. What will this mean for employment?