How to learn when you’re playing at scale has been saved
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The scalable efficiency model as it has traditionally been pursued—granular demand forecasting, rigid processes, and standardization to ensure predictability and minimize variances—is not only increasingly difficult; it is fundamentally incompatible with learning.
Anyone who has heard me speak knows that I believe we are seeing a shift away from traditional scalable efficiency as the predominant rationale for business. Technological advances have, in many cases, reduced the need for scale, and they are driving rapid and accelerating change; only organizations that are constantly learning will be able to keep up. Yet, the scalable efficiency model as it has traditionally been pursued—granular demand forecasting, rigid processes, and standardization to ensure predictability and minimize variances—is not only increasingly difficult; it is fundamentally incompatible with learning. Learning requires experimentation, and organizations that learn how to experiment faster will have an advantage.
How, then, does this square with our recently released paper on the future of the business landscape? In it, we posit that, over the next 5-10 years, while parts of the economy will fragment into a multitude of small players, the opposite of scale, other parts will concentrate into a few, large institutions competing on the basis of scale. The concentrated players will focus on a single business activity or function, and fragmented players will rely on these services for their very existence (for example, cloud services, online marketplaces). Because of economies of scale and scope as well as potential network effects, we expect concentration in a specific set of business types: infrastructure providers, aggregation platforms, and customer agent businesses. How can learning be a rationale for such large-scale concentrated businesses?
First, while scalable efficiency as it has been pursued may be incompatible with learning, scale in itself is not. Even at scale, concentrated organizations need to learn. In fact, learning may be more important than ever for the large players to maintain the distinctive, leading-class service and operations that allow them to prevail in their market. In the concentrating parts of this future landscape, there won’t be room for many players to compete. The ones that will succeed will do so through being very, very good at what they do. Pursuit of this excellence will lead many to eventually focus on this core capability and shed other activities that distract from it.
Part of the rationale for large, concentrated players to focus on only one type of business is to allow for more rapid learning. Consider the case of Cognizant, which started as an internal technology unit within Dun & Bradstreet in 1994. By 1996, Cognizant was serving external clients. In 1998, it went public on the NASDAQ. Separating from Dun & Bradstreet enabled Cognizant to focus on its capabilities as an infrastructure-type business providing IT services to the financial industry. Between 2002 and 2012, Cognizant made 17 acquisitions, broadening its scope from serving only the financial industry into providing IT services to the retail, manufacturing, logistics, and health care industries—all still focused in the infrastructure type of business. By 2012, Cognizant had over 800 individual clients, and revenues skyrocketed to $7.3 billion (from $368 million in 2003).1
Second, the concentrated players will learn from the multitude of touch points they have with both fragmented customers and other large customers. The key to scaling learning when you’re playing at scale is to be able to connect with and learn from the other highly focused participants, beyond your own organizational boundaries, who are also learning rapidly and are leading-class in their chosen business. As the landscape evolves such that most companies, large or small, are focused on just one type of business, ecosystems will include participants from a wide array of complementary domains that supply the capabilities the other companies have shed. These concentrated and fragmented participants will have to build trust-based relationships with each other as customers, suppliers, and partners to find ways to create value. These relationships are very different from the vendor/supplier/partner relationships most companies have today. This is more than just increasing the number of partners you work with or making the interactions more efficient; an organization must design, and foster, relationships with other participants with the intent to learn from each other and to engage around ideas. Taking our Cognizant example, the IT infrastructure service provider has a wealth of IT-related data to allow a company to gain insight into its performance relative to other companies both within an industry, like financial services, and across industries. The insights gained from analyzing differences in IT services usage patterns or overall IT services costs might point to opportunities for a customer to restructure its operations or redesign processes or products, opportunities that might have been hidden without the perspective of the concentrated service provider.
As the number of smaller players in an ecosystem grows, this ability to gain benchmarking perspective may prove especially valuable to smaller, fragmented players who have traditionally not had access to this type of comparative insight. It’s easy to see how a contract manufacturer, learning from encountering a wide variety of customer needs, would gain product and process expertise that would allow it to offer higher value to their customers. The customer, in turn, could learn ways to design its product for more cost-efficient manufacture or to use materials that result in less rework. This same type of learning, broadly across a customer segment and deep into an individual customer, can develop in customer relationship businesses, too.
Finally, talent development will continue to be critically important in concentrated players. Despite the fact that automation will likely play an increasing role in the scale and scope business types, infrastructure and platform businesses will succeed based on their ability to bring both their human and technological resources together in better ways to serve their customers and deliver increasing value. When a company chooses to remove distractions and focus on offering a core capability to an array of external customers, the employees of that unit are exposed to a wider variety of customer needs and performance issues. Consider the difference in exposure that an employee at a captive IT organization within a financial services company has compared to the exposure to varying customer circumstances and requirements that an employee in a company like Cognizant has. As focus and concentration become more pronounced in the future, the opportunity for employees in this domain to learn and gain expertise more rapidly is greater when they can work on really challenging problems with other leading companies who happen to be customers.
We talk about companies learning from each other and improving performance, but this implies individuals, both working on their own and within large companies, learning and improving their own performance from these encounters with other entities. Having this diversity of experience and exposure to a variety of customer experiences will actually attract leading talent because those individuals want to work in these types of learning environments. It becomes a virtuous cycle. The organization becomes better because it attracts better people and more learning occurs within. To the extent that such accelerated learning opportunities occur, the future business landscape could be very rewarding for individuals who want to learn.
As used in this document, “Deloitte” means Deloitte LLP and its subsidiaries. Please see www.deloitte.com/us/about for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting.