The Next Digital Frontier: Harnessing Automization and Emergence to Reinvent the Enterprise
By Martin Reeves and Kevin Whitaker
In today’s economy, digital capability is critical for growth and competitiveness.
A decade ago, only one tech company was among the top 10 global firms by market capitalization. Today, there are seven. The tech sector is even more over-represented in forward-looking metrics: it accounts for only 15% of the top U.S. firms, but 24% of the fastest-growing companies. And in the Fortune Future 50, our ranking of the firms best-positioned for future growth, they accounted for 52% of the top 25 Leaders and 76% of the top 25 Challengers.
Furthermore, digital players are tapping into the profit pools of other industries, as illustrated the efforts of Uber and Google to disrupt the car industry, or the impact of Netflix on film producers. Virtually every business is now also a technology business and an information business, and potentially vulnerable to disruption by digital challengers.
This is not news to today’s incumbents, who are well aware of the risks posed by technological disruption. Many have embraced “digital transformation” in an attempt to defend against threats and seize new opportunities. Such programs generally have two aims: First, to automate supply chains and internal processes. And second, to innovate by creating digital product and service offerings, as well as new business models to deliver these.
Such efforts can unlock enormous value. However, they also ignore two higher-level strategic impacts that are enabled by digital evolution: “autonomization” and “emergence”. Some digital leaders are already leveraging these advantages to great effect, effectively reinventing the enterprise model. For incumbents who need to compete directly with these pioneers, or who wish to anticipate where the digital game is heading, it is important to understand how these advantages work and how to realize them.
Autonomous systems can create massive learning advantage
Leading digital players are not merely automating traditional business processes or inventing new offerings, important though these moves may be. They are also fundamentally reinventing the operating system of the enterprise itself. Rather than following the managerial model of classical organizations, they are recasting the enterprise and its surrounding ecosystem as an integrated, autonomous learning system.
This involves not only adopting individual technological modalities (platforms, data, analytics, AI, etc.), but rather connecting them in a self-reinforcing system (see Fig. 1):
· Digital leaders develop platforms that match supply and demand. By nurturing both sides of the market, firms can create vast ecosystems of commercial activity, in which their platform services form an essential hub.
· As a result, these ecosystems generate enormous amounts of proprietary data. Not only does this confer an information advantage, but since it comes directly from the firm’s own platforms, it can be wired directly into other information systems.
· Thus these firms can then leverage artificial intelligence to generate real-time insights on consumer and supplier behavior from the data.
· Finally, wiring these insights directly to actions enables firms to learn autonomously, optimizing their services to changing conditions in real time.
· The effects of these actions then reverberate through the ecosystem, generating new signals to fuel further decisions.
Such autonomous learning systems allow for a much higher level of segmentation and personalization than would be possible with a traditional managerial system, which is constrained by the bandwidth and speed limits of human cognition and hierarchical organizations. For example, the offering, pricing and other aspects of the marketing mix can be tailored to each customer and updated continuously and automatically in response to changing conditions.
Most importantly, autonomous systems can easily outcompete traditional models on their rate of learning in complex, dynamic environments. The classical principles of the experience curve and time-based competition are endowed with new power through this switch from in vivo to in silico coordination and from the creation of closed learning loops.
The power of emergence
Autonomous learning systems can also unleash the power of emergence — the creation of a plethora of options beyond those explicitly planned or foreseen by a managerial process. In an increasingly complex and dynamic business environment, it is unrealistic to think that leaders will be able to envision or predict all relevant scenarios, as well as the optimal responses to each. Thus, harnessing emergence becomes a strategic imperative in the fast-evolving digital age. Harnessing emergence effectively liberates firms from the constraints of classical planning, and unlocks the creative potential of an evolutionary process.
When the effects of autonomization and emergence are combined, they effectively reinvent the operating model of the enterprise, by allowing the possibility of a self-tuning enterprise (see Fig. 2) — one that can learn and innovate at the “speed of data”, thereby enabling it to pull ahead of competitors.
Triple-loop learning in the self-tuning enterprise
Self-tuning firms effectively harness three learning loops (see Fig. 3). They systematically experiment to learn more about customer needs, by, for example, A-B testing different offerings. They modulate the rate of experimentation to optimize the long-run payoff, such as by experimenting more for new customers. Finally, they shape the environment to their own advantage, such as by developing new product categories to shape and attract new demand.
Through autonomization and emergence, self-tuning firms create significant advantages — well exceeding those that can be realized from programs aimed at increasing efficiency or product innovation alone (see Fig. 4). They can better understand customers by leveraging data from their own ecosystems and platforms to develop granular insights and automatically customize their offerings. They can develop more new, marketable products by experimenting with offerings and leveraging proprietary data. And they can implement change more quickly and at lower cost by acting autonomously. The benefits of autonomization and emergence are compounded by self-reinforcing network and experience effects: better offerings attract more customers and more data; experimentation brings knowledge that increases the value of future experimentation.
One such example of a self-tuning organization is Alibaba. Not only does its e-commerce platform provide a sea of user data, but the firm uses it to generate real time insights in a granular manner. It tunes the offering based on these insights in a real-time and highly segmented manner. And it continuously reshapes the enterprise to enable it to do these things more effectively, aspiring to “submit all aspects of the company to market discipline”.
Incumbents must reinvent their management model and mindset
Achieving these goals requires a different way of thinking about managing an enterprise. Instead of the traditional, mechanical mindset — in which circumstances, actions and outcomes are seen as being largely predictable and controllable — leaders must instead learn to think biologically and embrace the uncertainty and complexity of business.
Biological thinking recognizes that the business environment is a nested complex adaptive system: Individual employees are part of firms, which in turn are part of larger markets and industries, which are embedded in local and national economies and societies. Crucially, changes made to any part may reverberate through the entire system, with nonlinear and unpredictable effects.
As a result, deliberate changes to the system may not have the expected outcomes. Instead, firms must make indirect interventions, finding leverage points that are observed to have a positive impact on the broader system. They must make their enterprises resilient, ensuring that unforeseeable shocks will not have catastrophic effects. And they must experiment frequently, recognizing that there is often no way to predict ex ante what behaviors will emerge.
A leadership agenda for harnessing autonomization and emergence
In order to access the advantages of autonomization and emergence, incumbents need to reframe and expand the concept of “digital transformation” to go beyond new efficiencies and products, decided upon in a planned, managerial manner. Instead, they must strive to:
· Create integrated learning systems — To compete with digital pioneers, incumbents must adopt their methods and create autonomous, integrated learning systems. This involves linking data feeds from proprietary ecosystems, directly to decision engines and to direct business action, thus enabling autonomous action and rapid learning.
For example, Netflix engages its audience by making customized content recommendations to each user. The company reported in 2012 that three-quarters of viewings originated from such suggestions. To make personalized recommendations at such scale, the company leverages an autonomous, integrated learning system that receives feedback (in the form of user ratings or viewer behavior) and updates its suggestions accordingly. And it deliberately introduces variation into its recommendations, allowing new behaviors to emerge and letting the data speak for itself.
· Shift human creativity to high level tasks — As digital technology evolves, the comparative advantages of humans and machines are shifting. For problems that involve data acquisition, processing and decision making, algorithms should be given autonomy, removing the bottleneck of human decision-making. People should instead be focused on ‘meta’ tasks, like building and refinancing the autonomous learning systems, expanding ecosystems, or originating and designing entirely new business ideas. The result is an integrated strategy machine that appropriately leverages humans and technology, in a new balance.
For example, Amazon has dozens of data science systems that are closely integrated, allowing decision systems to react to new data immediately and consistently. If the popularity of one product in the market rises, it will cause automatic changes in the supply chain system (to optimize inventory), the recommendation engine (to suggest that product more often), and the pricing system (to optimize profits). Amazon calls this approach “hands off the wheel”. Humans focus their creativity on higher-level problems, such as specifying and evolving the design of those systems to account for new strategic priorities.
· Embrace biological thinking — To achieve autonomization and emergence, firms must abandon the top-down, mechanistic mindset that characterizes the traditional management model. Instead, firms must embrace the inherent complexity of business, recognizing that circumstances change, knowledge is always incomplete, causality is complex and outcomes are unpredictable. Instead of relying on unchanging unitary plans, firms must create and influence collaborative ecosystems, and experiment and co-evolve to find the best path forward.
For example, the Japanese firm Recruit orchestrates ecosystems of suppliers and customers that bridge the digital and physical economies. Recognizing the unpredictability of outcomes, Recruit builds entrepreneurial capabilities to develop new sources of growth, investing in more than a dozen ecosystems so far and soliciting thousands more employee recommendations. And rather than making unilateral interventions into the ecosystem for its own benefit, the firm emphasized “co-evolution” with other stakeholders to shape future opportunities. As a result, Recruit is an old incumbent company that is thriving in the digital environment, achieving nearly 20% annual growth over the last five years.
Digital challengers are reshaping the global economy, leaving many incumbents at risk of disruption. Digital transformation programs aimed at increasing efficiency or innovation can have significant benefits, but also leave much on the table. To compete with leading tech players on the rate of learning, firms must use technology to achieve autonomization and emergence, and in so doing reinvent the enterprise operating model.
 As of year-end 2017 (Apple, Google, Microsoft, Amazon, Facebook, Alibaba, Tencent)
 Based on the top 2000 U.S. firms by revenue, excluding energy; fastest-growing firms are top 50 based on 2013–16 revenue growth
 Leaders chosen from firms above $20B market cap; Challengers from firms below $20B
About the Authors
Martin Reeves is the Director of the BCG Henderson Institute and Senior Partner and Managing Director at The Boston Consulting Group. Follow him on Twitter @MartinKReeves and you may contact him by email at Reeves.Martin@bcg.com
Kevin Whitaker is a member of the BCG Henderson Institute. You may contact him by email at Whitaker.Kevin@bcg.com
About the BCG Henderson Institute
The BCG Henderson Institute is the Boston Consulting Group’s internal think tank, dedicated to exploring and developing valuable new insights from business, technology, and science by embracing the powerful technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand the boundaries of business theory and practice and to translate innovative ideas from within and beyond business. For more ideas and inspiration, follow us on Twitter @BCGHenderson