Seeing Around Corners

by Rita McGrath

  In 2004, fed up with prescription drug expenditures that rose an average of 14 percent annually between 1996 and 2004, managers at Caterpillar vowed that they would institute a new process because the current approach made them feel that they had “no leverage and are at the mercy of a confusing system. We wanted to challenge the system.” According to their estimates, that system entailed 10 to 25 percent waste.

  Using the analytical framework applied earlier in this chapter, we can see how the ways in which the PBM business had evolved represented an opportunity for Caterpillar to take action. The following table summarizes what Caterpillar did to free themselves from being “hostages” to the existing PBM arrangement.

  Consumption Chain Link

  Negative Attributes/Customer Pain Points

  How Did Caterpillar Address These?

  Selecting a benefit manager

  Conflicts of interest

  Found a PBM committed to transparency in pricing

  Understanding costs

  Lack of transparency

  Negotiated directly with major pharmacies

  Deciding which drugs will be in the formulary

  Little transparency, inflated costs

  Brought the decision in-house

  Pricing involving clawbacks

  Higher prices

  Pursued transparent pricing with suppliers

  The results of Caterpillar’s actions have been impressive. Although actual cost savings haven’t been reported, experts estimate that they could be in the realm of $37.5 million each year, with the company agreeing that its 2015 costs were actually lower than those in 2004.

  Todd Bisping, Caterpillar’s healthcare benefits manager, who led the charge in the company’s efforts, is also involved in the Health Transformation Alliance, a multiemployer membership group with the goal of helping other organizations to rein in their ever-rising medical costs, beginning with some of what Caterpillar learned. While the results of this initiative and others have been largely incremental to date, the commitment by CEOs such as Bezos, Buffett, and Dimon to accelerate businesses’ role in changing the way healthcare is delivered seems well on its way to marking an inflection point for that sector of the economy.

  Of course, Bezos’s Amazon itself seems poised to enter the healthcare sector, which could produce the same kind of sweeping changes it has triggered in other parts of the economy.

  Key Takeaways

  Practices that displease or even enrage customers can create an opening for a disruptive player to come into your markets and cause customers to defect.

  Even when you see an inflection point on the horizon, it can take a lot longer than you think for it to actually arrive.

  Customers will only remain hostages for so long. Eventually, the model that imprisons them is bound to collapse.

  It may make sense to separate the operating functions of a growing business from those of businesses in decline. The two require different metrics and operational considerations, and different points of value to customers.

  Deeply understanding the situations customers are in, the jobs they are trying to get done in those situations, and the outcomes they are seeking is vital to anticipating how those situations might change.

  Deeply understanding the structure of a system (such as the way prescription medicines are sold) can allow you to spark an inflection point, one pain point at a time.

  5

  * * *

  What Must Be True? Creating a Plan to Learn Fast

  People who are right a lot work very hard to do that unnatural thing of trying to disconfirm their beliefs.

  —Jeff Bezos

  The discussion of seeing around corners has taken us first to the edges—to those places where the potential for inflection points first begins to make itself known. We’ve created some approaches to establishing early warnings—imagining very different future scenarios and working backward to see where they might be in their evolutionary stages. We’ve explored the idea of an arena, rather than an industry, as being a crucial level of analysis. We’ve looked at how irritants and blockers in key stakeholders’ paths to getting jobs done can open the door to an inflection sparked by an organization that removes those attributes. We’re now on the brink of considering what actions should be taken next.

  But first, a few caveats. The techniques described here are not about making predictions and being right. They are about generating possibilities and opening your mind to what might happen, so that as evidence gets stronger, you are ready to take action. For any future state, there are many variables that can lead to one outcome or another. What is valuable in complex systems is to be able to keep multiple possible futures in mind so that if and when they unfold, the landscape is more recognizable.

  Intel, for instance, has employed science fiction writers, futurists, and students to produce creative works about aspects of the future, with the purpose not of making predictions, but of opening people’s minds to emerging possibilities. Seeing around corners is about broadening the range of possibilities you consider paying attention to. Your ability to look into the future is only as well developed as the set of possibilities you are prepared to entertain.

  The Inflection Point Gets Just a Little Clearer

  Having seen an inflection point coming, you are now at the moment of deciding what exactly to do about it. The real dilemma facing a decision-maker at this point is that uncertainty is still extremely high. In other words, the ratio of assumptions that must be made relative to the hard-core knowledge that one has is high in the extreme. As I have written about extensively elsewhere, the thought process that such precarious situations require is entirely different from the thought process that one follows when the direction is known.

  To be blunt, getting through this tricky process begins with confusion, experimentation, and a touch of chaos, followed by a single-minded determination to make progress against an overarching goal. You must be able to passionately advocate for the vision to drive the outcome, while also being prepared to change the means. This approach is consistent with futurist Paul Saffo’s recommendation to form as many forecasts as quickly as possible—and then prove them wrong as quickly as possible—as well as to “hold strong opinions weakly.”

  Discovery-driven planning, perhaps because more and more of us have been thrust into ever more uncertain circumstances, is gaining significant traction as a new generation of managers recognizes that the critical challenge is planning to learn. Briefly, discovery-driven planning asks you to set some parameters for a future state—and then to work backward to figure out what must be true to make that future state real. Similar to the weak signals exercise first discussed in Chapter 2, this process differs from the risk-averse, failure-avoidant nature of many corporate planning processes.

  The idea is not to plan out in great detail what should be done. Rather, with an inflection point in mind, the idea is to make what Peter Sims has called “little bets” and which I call “planning to learn.” Before making a large commitment, in other words, start by breaking the monolithic plan down into smaller pieces. These pieces are each punctuated by what I call a checkpoint.

  A checkpoint is simply a point in time at which you will learn something. At each checkpoint, you want to be asking two questions. The first is whether what you are learning is worth the cost (or risk, or time) required to achieve it. Elsewhere, I’ve referred to an organization’s “appetite,” which is the determination, in advance, of what we think we will learn and how much that learning is worth to us. The second is whether, given what you are learning, it still makes sense to continue with the plan or whether a shift of some kind is warranted.

  The catch you want to avoid is getting tangled up in the need to be right when you really don’t have all the facts. Huge amounts of human breath have been wasted in meetings where people argue back and forth about being right. Instead, think of a low-cost, creative way to determine how you might find out what the right answer really is. In tha
t case, even if your assumptions are not borne out, you’ve learned something. In fact, even if you’ve learned that you are at a dead end, that’s actually progress because you can always shift course, or “pivot.”

  An entrepreneurial mindset is incredibly useful here. The key is being discovery-driven. Stop pretending you know all the answers. In a highly uncertain and fluid environment, neither you nor anybody else has answers. Arguing about being “right” or having a detailed plan going eighteen months out is just wasting your breath. Instead, articulate and pinpoint the major uncertainties and how you might gain some insight about them.

  Nassim Nicholas Taleb offers a valuable perspective on this somewhat chaotic period in thinking about seeing around the next corner in his book Antifragile: Things That Gain from Disorder: “Anything that has more upside than downside from random events (or certain shocks) is antifragile; the reverse is fragile.” What you are trying to do as you formulate a discovery-driven approach to the next inflection point is capitalize on the upside of your potential learning, while containing the cost and downside. This type of planning exactly reflects Taleb’s point that the best way to approach change is to fail cheaply, demonstrate that your hypotheses are wrong quickly, and continue to see a big upside to your actions.

  But It Isn’t Based on Facts Yet!

  As I mentioned earlier, when an inflection point first appears on the horizon, it is extremely easy for us to leap into action too early and to invest substantial resources before a real understanding has emerged as to what that point really means. Many an organization has learned this lesson to enormous regret, especially with the advent of what we call the digital revolution.

  Making the wrong moves with respect to digital technologies often begins with misunderstanding the enormity of the change digital represents. The unique effects of digital were discussed in Chapter 1 in terms of combining bits of information that used to remain safely separate. If you think of digital’s effect on a traditional organization’s strategy and operating model, you can see why so many otherwise high-performing companies can easily make wrong strategic bets because they underestimated digital’s impact on their future business, as my colleague Ryan McManus has so clearly explained.

  The Melting Snow of Digitization

  Digitization, simply enough, means replacing activity that used to be done in an unconnected, analog way with connected activity intermediated by a technology layer. For example, once upon a time if you wanted to understand how well a company treated its customers, you would look up its ranking with the Better Business Bureau or Consumer Reports. Today, while those sources of information still exist, consumers are also able to view ratings by other consumers on a wide variety of platforms such as Yelp and TripAdvisor. Rather than rely on a salesperson to explain the benefits or differences between products in a store, potential customers can now click on reviews posted by other users presumably like themselves.

  Earlier, I used the idea that snow melts from the edges to suggest that if you want to be strategic about a potential inflection point, you need to be exposed to where it first turns up—usually at the edges of your organization. The digitization process takes us further with this idea, as it shows not just the early signals that something is changing (snow is melting) but how the change is progressing.

  Starting with Marketing

  Digitization began, sneakily enough, in realms that seem distant from the strategic core of a business, such as marketing. In the early dot-com gold rush, that meant securing the “.com” URL, capturing digital real estate for banner ads, and trying to get people to subscribe to blogs. The disruptive effects in this first phase were initially felt by organizations that were used to providing one-way flows of information to relatively powerless external players. The first wave of melting snow utterly changed the taken-for-granted assumptions about how organizations should present themselves to the world. But for many, this change didn’t seem central to future organizational survival, since it seemed only to be affecting marketing.

  Indeed, one of the more interesting effects of the early Internet wave was to increase the amount spent on—wait for it—analog marketing. Publications such as Wired, on the new-economy side, and even Fortune, on the more traditional side, were bursting with endless pages of advertisements. They could hardly fit in your mailbox. In 2000, advertising reached a walloping 2.5 percent of GDP in the United States, only to retreat back to historical norms of 2.2 to 2.3 percent with the ensuing dot-com crash.

  This early stage might well have lulled those providing traditional advertising into a sense of security. And yet, in 2000 Google launched its AdWords advertising product, which offered advertisers the ability—with relatively little personal effort—to put their advertisements in front of people searching for terms that might indicate a proclivity to purchase. By the end of its first year, Google AdWords had reached $70 million in revenue. Using the product, advertisers could determine how much they were willing to pay to have their advertisements appear next to designated search content. When a user did a search, Google’s algorithms scanned the database and generated a ranking based on willingness to pay. If a consumer clicked on the ad, Google would charge the advertiser a “cost per click” determined by the advertiser’s willingness to pay, what others had bid to be shown next to the same search subjects, and a small surcharge. Other advertising models charged advertisers a “cost per thousand impressions” based on how many viewers were likely to have seen the ad.

  This was the beginning of the transition from advertising that flowed through traditional analog channels to advertising that appeared on digital ones, a transition that has led newspapers, for instance, to suffer double-digit declines, from advertiser spending of close to $90 billion in 2005 to less than $50 billion in 2017. The shift decimated traditional media, but even as that inflection point rolled out, many traditional businesses still didn’t see how it would affect them.

  Creeping into Operations

  The next phase of the inflection point represented by the digital revolution was to change how companies performed their basic business functions. Firms such as CEMEX, the Mexican cement supplier, found that digital enabled them to service customers in entirely new and very compelling ways. For instance, in its ready-mix concrete business, CEMEX realized that a just-in-time service solution would be far more valuable to construction customers than a solution that relied on being able to project needs and utilization in advance. Using insights from companies such as Dell, CEMEX created a just-in-time call center mode. When customers needed cement delivered, they would place an order and, within a relatively short period of time, receive a delivery.

  This phase of the digital revolution rocked many of the taken-for-granted assumptions in the world of operations. Costly, high-friction transactions were replaced with relatively inexpensive, low-friction transactions, opening up vast possibilities.

  Next, to Products and Services

  In light of the new possibilities that digital enabled, it was only a matter of time until digital products and services became meaningful participants in a great many arenas. Entertainment and media were utterly changed as photos, books, movies, magazines, and other content “went digital” and created not only different products but also different consumption experiences. In effect, products came to consumers—consumers no longer had to go to them.

  The attributes of even ordinary products and services in this phase were altered as well with the addition of a digital component. Automakers and their suppliers, for instance, started to consider how the addition of communicative technologies to automobiles could result in a change from cars that are essentially focused on transportation to cars that are nodes in a connected network. Profits are likely to shift as well, away from selling the cars themselves to offering all kinds of services, including auto repair, diagnosis, and insurance. In addition, all the data cars generate will have value of its own, in applications that are still to be invented.

  And Then
to Business Models

  Digital is at its most disruptive when it changes the parameters that have held conventional business models in place. As discussed earlier in this book, it is extraordinarily difficult for incumbent decision-makers to “see” that the traditional rules they operate under have changed, often because they are simply not looking.

  Take insurance. Digital technologies have the potential to completely change the way the different components of the traditional value chain are managed. Further, digital business models can often be delivered far less expensively than traditional models, creating enormous pricing pressure for incumbents.

  Distribution—The traditional broker “selling” a customer on policies disappears, to be replaced by an interactive, on-demand experience, probably through a mobile device. Because this is now feasible and affordable, more and more insurance offerings can be provided “on demand” based on usage, more tightly fitting the provision of insurance services to the actual activity being insured. Smart contracts facilitated by blockchain technology could even become self-executing, eliminating piles of paper and tedious record keeping.