ORGANIZATIONAL STRATEGY AND THE FUTURE OF AI FOR COMPETITIVE ADVANTAGE.

• Author: Rosendale, Joseph A.

INTRODUCTION

The future of business is in perpetual flux, perhaps hastened more quickly by technology now than at any point in the past. New business strategies, modeling, globalization, and regulations, among many other factors, all play a critical role in organizational advancement and executive decision making, while the need for firms to constantly innovate remains irrefutable. As a focal point in strategic management for decades, innovation through technological process improvement has largely produced positive returns. Currently, technology and digital transformations of traditional business processes are rapidly shifting toward an emphasis on artificial intelligence (AI) and smart learning systems. Implemented properly, AI presents the opportunity to significantly improve and standardize many organizational operations and processes, leading to sustained competitive advantage and value creation while also serving as a potential barrier to entry for rival firms.

Framed within the context of value creation, the primary purpose of this report is to conceptualize ways in which AI can serve as a firm resource and a source of unique competitive advantage. Not discounting the impact that a firm's external environment and overall industry positioning play on potential success, the scope of this review assumes an internal, resource-based view of competitive advantage and examines the latent sources of improvement offered through AI. Throughout, the article also makes note of managerial implications and integration considerations worthy of consideration.

After a brief elaboration on the concept of AI and its capabilities, the article discusses AI in relation to the other major conceptualizations of the piece: firm resources, value creation, consumer benefit, managerial perspectives and organizational behavior, and, finally, integration considerations. The conclusion contains a recommendation for leaders considering AI integration in their business for value creation and competitive advantage.

Background

A brief definition and clarification of AI establishes a foundation upon which to build. In general, AI is the study of intelligent behavior (Genesereth & Nilsson, 2012). Dash, McMurtrey, Rebman, & Kar (2019) offer an even more concise AI definition: the ability of a computer to independently solve problems that they have not been explicitly programmed to address. More specifically for consideration here, AI refers to a field of technology that aims to mimic or simulate the cognitive functions of humans by machine behavior through artificial learning and technological programs. For example, deep learning, a specific AI technique often used for business applications, requires the input of large amounts of data upon which algorithms act to enable completion of tasks, such as pattern recognition, predictive strategy, data synthesis and analysis, and other useful functions. As more data are received, the system becomes increasingly capable of successfully processing, analyzing, and integrating data and converting it into useful information. AI-enabled systems can create knowledge from data, respond appropriately to data inputs based on this knowledge, and evaluate the outcomes of responses, which serve as additional data input in an iterative cycle. Thus, the system "learns" and benefits its operators by becoming exponentially more knowledgeable and better at its tasks with more data and over time.

Neither inherently intelligent nor sentient, AI is, at its core, an algorithmic learning system. Recent improvements in computing speed and data management energized machine learning and have spawned an environment rich with opportunities for businesses to create market value and competitive advantage. Developing AI through applications like natural language processing, sales or supply chain forecasting, or recommendation engines offers deep-learning capabilities that contribute to value creation in a wide variety of industries. These applications can be classified as weak or artificial narrow intelligence (ANI). ANI, as opposed to artificial general intelligence or strong AI (AGI), is designed to accomplish specific tasks under a narrow set of constraints. Despite the misleading descriptor "weak," ANI can employ an expert-system approach to achieve very robust feats. All the AI now available, from chatbots to self-driving cars, are examples of ANI. AGI is currently out of our reach, but some prognosticators suggest it will be part of our near future, while others are skeptical (Dilmegani, 2020). Theoretically, AGI will handle multiple tasks seamlessly, much like a human and likely better, with faster time to mastery, instantaneous data processing, high levels of sophistication, and unending endurance. AGI will act autonomously with the cognitive abilities and freedom to solve problems beyond constraints.

Standing today in the realm of ANI, however, business leaders still have much to gain from developing, deploying, and delivering value with task-based machine learning, natural language processing, pattern recognition, decision support, etc. Bringing AI on-board instantly adds a non-human human resource, with strong implications for organizational improvement.

Firm Resources

In the traditional sense, firm resources are referred to as those positive attributes, such as assets, technology, information, knowledge, etc., which organizations can utilize to implement their strategies (Daft, 2006; Porter, 2008). Although numerous, firm resources can be aptly classified into three main categories: physical resources, human resources, and organizational resources. In this article, AI is deemed primarily as an organizational resource, with the potential to likewise be viewed comparably to animate, human resources. True, not long ago technology was considered to be an inanimate resource, purchased and functioning akin to buildings or steel and, as Bowman and Ambrosini (2000) observe, "incapable of transforming themselves into anything other than what they are" (p. 5). Mature AI technology, however, does not require the necessary intervention of people in order to create sustained value. This does not mean that human input is irrelevant; rather, the important point here is the opportunity AI presents as it ethereally exists across the contexts of both a human and organizational resource.

According to Barney's (1991) theoretical model, organizational resources that are considered sources of sustained competitive advantage are those conforming to requisite conditions and circumstances; they are primarily rareness, value, imperfect imitability, and non-substitutability. Apart from recognizing and understanding the criteria necessary for a resource to be considered valuable, firm leaders must also implement strategies to sustain them over a long period of time. A firm can expect to create a competitive advantage while in control of a valuable resource unavailable to competitors or when implementing a value creating strategy not currently employed by a competitor; sustained competitive advantages similarly exist when competitors cannot replicate the source of advantage (Barney, 1991). On the surface, AI technology seems to be a homogenous and imitable resource, when viewed in a conventional sense in that all firms having access to AI should, theoretically, be able to capture the same amount of value given a similar investment in technology. Differing advantages firms will realize, and the reduction of competitive parity through AI will be based on, the firm-specific data inputs and, ceteris paribus, the heterogeneity of algorithmic programs from which intelligent information is borne.

Organizational resources considered rare are those which are idiosyncratic and possessed by only a few competing firms within a similar industry. When resources are both rare and capable of persisting over a prolonged period of time, they then become a potential source of sustained competitive advantage (Knott, 2015). Resources that do not meet the criteria of rarity cannot be considered a source of competitive advantage. While many organizations are expectantly eyeing AI potential, a minority of them have integrated it, thus illustrating the present rarity of operational AI. According [DGD1] to a 2017 survey of over 3,000 business executives spanning 112 countries, only 23% of respondents have incorporated AI into their processes (Columbus, 2017). A similar 2019 survey of 500 US business and technology professionals conducted by CompTIA found that only 29% of companies regularly use AI technology, mainly implicating lack of organizational comfortability with the practice (Bayern, 2019). While more respondents reported the desire to increase future AI integration in their companies, AI presently represents a rare resource in many industries. The...