Accelerating learning for individual process steps, on the other hand, is insufficient to achieve aggregate learning or competitive advantage. Slow contextual change, fueled by social, political, and economic developments, is becoming just as crucial to the business as technology-driven accelerations. Leaders must reimagine their companies to utilize both human and computer skills synergistically to support learning on all timelines in order to compete on the ability to learn.
A brief history of educational institutions
Businesses learned how to perform existing processes more efficiently in first-generation learning organizations, as shown by the "experience curve." Firms tended to cut their costs at a steady and predictable rate as their cumulative experience increased, as Bruce Henderson discovered half a century ago. Automobile prices, for example, continuously reduced by around 25% every time the total product volume doubled. (As an example, see Exhibit 1.)
Learning was a game of constant improvement in this model, with the goal of lowering marginal costs. Competing on learning was all about accumulating more volume, and hence experience than your opponents. This allowed for a pricing approach based on the expected benefit of learning and systematic cost reductions employing mechanisms like statistical process control, kaizen, six sigma, and quality circles.
In recent years, a second-generation learning notion has risen to prominence: learning how to imagine and build new products. To put it another way, businesses must not only learn to descend experience curves but also to "jump" between them. (See Exhibit 2.)
In business, this second component of learning has always existed, but its significance has expanded. Because technological innovation has shortened product lifecycles, new learning curves emerge before the old ones have fully matured — and businesses must balance both learning aspects at the same time. In less than a decade, Netflix went from a DVD rental business to a streaming service to in-house content creation, all while growing to 190 countries.
A third phase of the learning game is starting to take shape today. Sensors, digital platforms, and artificial intelligence are examples of modern technology that promise to dramatically increase the rate at which data is generated, gathered, and processed. This could allow businesses to function at superhuman speeds, learning about the market in seconds or even milliseconds and reacting in milliseconds.
At the same time, as social, political, and economic upheavals reshape the business landscape, firms must broaden their learning capabilities to embrace longer durations. Most firms have woken up to the realities of time compression, but this is only half of the problem. The number of timescales that must be addressed is expanding in both directions. A third-generation learning company is one that can adapt to this new reality by embracing algorithmic principles on shorter timeframes while also adjusting to non-business forces on longer timelines.
Businesses cannot rely just on technology proficiency to make this jump. The evolution of the organizational model is required to unlock the potential of new technology, following a well-established historical pattern. Only when new industrial technology was combined with organizational innovations such as new factory layouts, revised worker roles (such as the assembly line), and new managerial approaches like quality circles and kanban could the original experience curve be fully realized. In the same way, leaders must redesign the enterprise to not only harness the potential of new technologies but also to synergistically combine the unique learning skills of both humans and technology — in other words, to create effective "human+machine" machines.
To learn algorithmic timeframes, "autonomic" the organization.
New technologies have played a big role in the most recent evolution of the learning game. Oceans of proprietary data may be collected in real-time using digital platforms and IoT devices, allowing for differentiated insights to be extracted. And AI algorithms can recognize complicated patterns that humans can't comprehend at rates that humans can't match.
However, in order to maximize the learning potential of new technologies, business executives must rethink how they operate. Traditional organizational structures have limited decision-making bandwidth and can only react slowly. Could your present organization respond to that knowledge even if you knew the best product selection, marketing approach, and pricing for every consumer in every second?
These timeframes necessitate a different business paradigm, one based on autonomy rather than hierarchy and management-centered decision-making. Leading learning firms do so by integrating data, AI algorithms, and automated execution in a way that requires little human intervention. This "closed-loop algorithmic learning" approach creates a virtuous cycle: more data makes learning algorithms more powerful, which aids decision engines in improving product selection or fulfillment, resulting in increased volume and more data. (For more information, see Exhibit 3.)
These systems can operate and learn at the "speed of data" rather than the "speed of hierarchy" since they don't rely on manual decision-making. Amazon, for example, organizes its dozens of data processing and interpretation systems into a completely integrated web, so that fresh information from any aspect of the business (for example, increased sales of one product on its e-commerce platform) automatically cascades to other parts of the organization (inventory forecasting, pricing optimization, and so forth). This "hands off the wheel" strategy enable Amazon to quickly comprehend and act on new market data as it emerges.
Autonomous learning avoids the typical managerial structure that has defined businesses. Instead, when properly structured, businesses become "self-tuning," quickly perceiving market changes and adjusting to algorithmic timeframes. This may be unsettling for CEOs who grew up in an era where managerial decision-making was king. Given the capability of today's technologies, however, leaders should focus on the crucial issues that demand distinctly human qualities rather than allowing robots to perform what they do best.
Human minds should be refocused on higher-level challenges and longer timescales.
Accelerating transformation and shorter cycle times are clearly understood by today's CEOs. However, longer timeframes are becoming increasingly significant, which is possibly underappreciated. Companies are slipping from their competitive peaks faster than ever before, and their longevity is dwindling. It's typically too late to avoid rapid deterioration by the time observable indicators of failing performance appear. As a result, multi-year horizons are becoming more important, and organizations must be adaptable across all timelines.
Longer-term causes affecting company often come from outside the industry:
The nature of political outcomes is becoming increasingly unpredictable and disruptive.
The stability of international institutions is deteriorating.
Within countries, social inequality continues to rise.
The character of consumerism is being reshaped by changing generational values.
The business-related social reaction is becoming more common and on a wider scale.
Worker skills and the nature of work are changing as a result of technological advancements.
Businesses might have been able to avoid focusing on these slow-moving forces and instead treat them as constants in more stable times. However, as recent events have demonstrated, these non-competitive challenges are becoming less predictable and more important to long-term firm performance, necessitating increased attention.
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2 years, 5 months ago
