IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 3, NO. 2, JUNE 1991 zyxwvutsr 149 The Role of Artificial Intelligence in Understanding the Strategic Decision-Making Process William E. Spangler Abstract-Research in decision support systems (DSS) is con- ducted from two perspectives. The first views the mission of DSS research in terms of building specific tools for supporting decision making, while the other views a DSS as a mechanism for modeling, and thus better understanding, the decision process. Expert systems for strategic planning have generally taken the former approach. This paper argues that the latter approach is required in order to shed light on the early, and perhaps most important stages of strategic planning, particularly the early interpretive stages involving strategic intelligence analysis and issue diagnosis. Research in artificial intelligence-including investigations into diagnosis and situation assessment, analog- ical reasoning, plan recognition, nonmonotonic reasoning, and distributed intelligence, among others-can be used to build models of strategic decision making that help researchers in better understanding this traditionally unstructured activity. zyxwvutsrq Index Terms-Artificial intelligence, cognitive modeling, com- petitive intelligence, decision support systems, expert systems, strategic decision making. I. INTRODUCTION LTHOUGH the use of artificial intelligence (AI) tech- A nologies continues to expand in the corporate world, the growth of that same technology for the support of corporate or organizational strategic planning is somewhat slower. This is particularly apparent when comparing the strategy area to other domains, such as medicine [lo91 or engineering [84], or even to other traditional management disciplines, such as finance [53] or accounting [30], [110]. There are several reasons for this. First, while the application of AI requires an adequate understanding of a task and a problem-solver, the task of corporate strategy formulation is still relatively unknown. As a result, strategic problems continue to be characterized as complex, unstructured, fuzzy, ambiguous, and notoriously difficult to formulate [l], [29], (671, [72], [102]. Mason and Mitroff echo this sentiment by describing these so-called “wicked” problems in explicit metaphorical terms: Wicked problems are not necessarily wicked in the perverse sense of being evil. Rather, they are wicked like the head of a hydra. They are an ensnarled web of tentacles. The more you attempt to tame them, the more complicated they become. [67] Manuscript received October 1, 1989; revised October 1, 1990. The author is with the Artificial Intelligence in Management Laboratory, Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260. IEEE Log Number 9144302. Second, not only is strategy formulation a complex individ- ual task, but it also involves communication and negotiation with other individuals as part of an organizational decision- making process [29], [67], [92]. This group-oriented, heterar- chical process in turn has tended to complicate the construction of expert systems (ES), or other AI-based approaches, which have traditionally been more suited to modeling a single individual. Third, executives tend to resist using computer-based deci- sion support systems of any kind, let alone AI-based systems [24], [36], [115]. Formulation of strategy is instead often viewed as an art, a highly creative process which is not conducive to the intrusion of computers. Fourth, long-range strategic planning does not invite the type of validation found in the traditional domains. There may not be a “right” or “wrong” answer. Instead, the ultimate strategy may be a compromise from among several alterna- tives, constrained by many environmental factors, and subject to dispute from other interested parties in the organization. Furthermore, it may take years before the consequences of an implemented strategy are known. Despite these issues, however, researchers and practitioners are slowly pushing AI technology, and its applications, up the corporate ladder. This survey will focus on research issues involving strategic planning and AI, including the nature of the formulation task, cognitive studies of strategic planners, and computer-based support for strategic planning. We will begin by reviewing the research to date, and will argue that, like traditional decision support systems (DSS) research, much of the potential for future research in this area lies in modeling the ill-structured, early stages of the strategic decision making process. There- fore, we will discuss a specific model-based approach to the study of these early stages: strategic intelligence analysis and issue diagnosis. We will suggest relevant research in AI that can support this type of effort, and conclude by offering a research agenda intended to guide future efforts. zyxw 11. THE MISSION OF DECISION SUPPORT SYSTEMS RESEARCH There is a continuing discussion within the research com- munity concerning the objectives of DSS research. One school of thought views DSS research in terms of the computer-based support issues. Goul, for example, described the validation cri- teria for his strategic planning ES (described in the following section) as follows: Designers have typically validated systems by seeking to answer the question, ‘Does the system produce the same decision as the expert when given a particular problem?’ 10414347/91/060G0149$01,00 zyxwvuts 0 1991 IEEE - ~~