Theory to Practice

How to create value in the age of the algo firm

When algorithms are used to run a corporation’s business-as-usual operations, they free up the cognitive resources of decision-makers who can then focus on low-frequency/high-impact strategic decisions on matters such as innovation, M&As, capital structure, and talent acquisition. In the age of the algo firm, a superior ability to frame firm strategies represents a key source of competitive advantageIndeed, the dissemination of algorithms in organizations is relieving top managers from the burden of routine decisions so they can concentrate their efforts on strategic decisions, which are characterized by fundamental uncertainty and conducive to higher value creation. 

The context

In the last decade, a few apparently unrelated trends have developed in the corporate world. For instance, a slew of superstar firms have emerged, operating in the digital sphere and characterized by hyper-growth. In addition, public managerial corporations are being eclipsed in favor of concentrated ownership, active investors, and high financial leverage. Despite these corporations becoming larger and more complex, owners/entrepreneurs remain at the helm for long periods of time. Also, firms are increasingly relying on M&As and external growth, flexibly reconfiguring their knowledge base and assets. Finally, customer-centricity and experimentation are on the rise. 


Managerial literature has addressed these subjects separately. However, according to our research, these questions are intimately related, and the explanation for them is grounded in the effects of the digital revolution on how the firms function. In fact, digitalization changes the very nature of the firm, radically transforming strategic decision-making and strategic management. 

The research

Building upon these observations, our framework posits that low-frequency/high-impact decisions are characterized by fundamental uncertainty. Consequently, they require human discretion and judgment, as well as decision-making “technology” that must rely on conceptual frameworks, beliefs, and experiments, since data might not be readily available. 


The technology we refer to is the scientific method. Specifically, when faced with low-frequency/high-impact decisions, managers first have to define the problem, and only after doing so, explore solutions. The main purpose here is to enrich their knowledge about the uncertain, granular needs of customers because meeting these needs is the primary source of value for a firm. 


Algo firms progressively embody knowledge related to solved problems and automate the associated decisions. This is what enables managers to dynamically explore new value- creation spaces, dedicating time and attention to solving problems that are ill-defined, complex, or not thought of, yet.  


As algorithms take over more and more high-frequency/low impact/low uncertainty decisions, the traditional role of professional managers in organizations gets thinner. Monitoring and coordinating algo firms is less demanding than the traditional coordinating and monitoring roles managers play in “analog” organizations. All else being equal, this means that owners can adopt a more intense, hands-on approach to strategy making.  

Conclusions and takeaways

We envisage three conditions that can produce superior performance as a result of the cognitive time and effort spent on low-frequency/high-impact decisions:


  1. A scientific approach to decision-making under uncertainty. High-performance/low-frequency/high-impact decisions should be made by applying:
    • well-defined theories or mental representations based on the search and use of canonical forms, simple rules, general categories, analogies, and first principles thinking;
    • appropriate evidence, data, and experiments.
    We call this approach “scientific” because it resembles what scientists do to develop and test their theories.
  2. Embracing fundamental uncertainty as the ultimate source of economic growth. In other words, opportunities for growth come from the unbounded exploration of unknown problems and the discovery/creation of market imperfections, which makes it possible to move the knowledge frontier. We argue in particular that the ultimate source of business and economic growth still centers on the granular and fundamentally uncertain nature of customers’ needs. This means that top managers have to allocate the extra time and attention they’ve gained from digitalization to acquiring knowledge about uncertain, variable, and increasingly differentiated customers’ needs, and then matching these needs to appropriate solutions.
  3. Owners as strategists. With fewer decisions to be made, the traditional role of managers loses importance, as well as the agency problems that come with it. Knowledge growth is hard to contract for since professional managers might not have the skills or the incentive to embark on moving the knowledge frontier. Besides, the cost of aligning their interests and solving agency problems might be prohibitive. Growing knowledge therefore becomes a task that owners tend to take on directly by becoming the firm strategists. Many superstar companies today (e.g. Amazon, Facebook, Google) are good examples of firms that have been sustainably and effectively managed by owners and their “top-management-team-as-a-club” members.


These three conditions are complementary. If companies adopt any subset of them, they will see significantly lower performance than any organization that deploys all three in combination.


Camuffo, A., Gambardella, A., & Pignataro, A. 2023. “Framing strategic decisions in a digital world.” Strategic Management Review, 4(2): 127-160.