Can ChatGPT revolutionize executive decision-making? The buzz surrounding this formidable language model, a topic of conversation for months, provides a compelling answer: “Yes, indeed. I can enhance your management decisions by furnishing you with insights, data, analyses, and multiple perspectives to facilitate well-informed choices.” ChatGPT candidly acknowledges its limitations as well: “Nevertheless, it is essential to recognize that my counsel and recommendations are grounded in algorithmic data analysis. Ultimately, the human touch, stemming from your own experience, knowledge, and assessment of the situation, remains crucial.”
This dose of humility notwithstanding, language models like ChatGPT have the potential to become indispensable decision-making aids for both executives and organizations. Their true value lies not in supplying definitive answers but in guiding us through a more methodical decision-making process—something that’s often lacking, even in critical management decisions.
Informed decisions typically unfold in three phases. First and foremost, we must define our objectives and the context surrounding them. What precisely is the decision’s scope, and how does it align with our goals, values, and preferences? This initial phase establishes the decision-making problem and the framework within which it operates. The second step involves generating alternatives. What decision-making choices are at our disposal? The objective here is to cultivate a diverse array of possibilities, rather than fixating solely on the obvious ones. Only when we’ve extracted a sufficient pool of options from our decision-making framework can we effectively evaluate them and arrive at a well-informed decision—the third and final phase.
In its current state of training, ChatGPT skillfully lends a hand in all three phases of business decision-making. Practically speaking, this means we can engage in meaningful dialogues with the system throughout these phases. When assessing decision alternatives, for instance, we can inquire about the common missteps made by managing directors in the mechanical engineering sector when expanding into new markets. What were the key success factors for those who undertook successful expansions?
ChatGPT won’t provide us with a one-size-fits-all template for evaluating options in our unique context. However, it can aid in uncovering our inherent biases and prompting us to question preconceived notions. Strategic deployment of ChatGPT can serve as a valuable debiasing tool, one that appears to have absorbed the wisdom of Daniel Kahneman and Amos Tversky. It encourages us to contemplate how we can assess options from a more well-informed perspective.
Moreover, ChatGPT proves its worth today by helping us discover additional options that may elude our own creative thinking. This, in turn, broadens our decision-making horizons and reveals the multitude of possibilities, often more far-reaching than we initially perceive.
For instance, consider the challenge of reducing dependence on China and diversifying a supply chain—a decision unfamiliar to many managing directors and their teams. Here, ChatGPT could draw upon strategies documented online by companies in similar predicaments, potentially offering more innovative solutions beyond simply relocating production to Vietnam. This breadth of options stems from ChatGPT’s access to a substantial repository of publicly available insights specific to the industry or company class.
ChatGPT also excels at aiding in setting goals and preferences, assessing decision-making circumstances, and selecting the appropriate framework. Dialogue remains pivotal. By asking the right questions, we gain deeper insights into the context of a decision. With ChatGPT, we can swiftly access suggestions for typical goals that other companies have pursued in similar decision-making scenarios. For instance, a query might read: “Hello ChatGPT, I lead a successful mid-sized tooling manufacturer near Columbus, Ohio, struggling to attract engineering talent. What could be the underlying reasons, and what strategies have similar manufacturing companies employed to address talent shortages?”
In summary, ChatGPT is evolving into an increasingly sophisticated conversational partner and sparring companion. It doesn’t absolve us of the responsibility to define the decision-making framework, generate a wide spectrum of options, and assess them. However, as indicated in the introduction, it provides invaluable perspectives. A language model of this scale boasts several advantages over a human sparring partner: it lacks personal agendas, doesn’t seek to curry favor with top decision-makers for personal gain, remains impervious to internal groupthink and bureaucratic politics, and is substantially more cost-effective than external management consultants or in-house strategy departments. This democratizes decision preparation and assistance, especially for smaller companies.
Aspiring managers at business schools often indirectly acquire decision-making prowess through numerous case studies. The goal is to cultivate a repertoire of decision-making models by crafting and evaluating actionable options within a decision-making framework. Naturally, case studies don’t proffer perfect solutions to specific decision-making scenarios; instead, they pose questions, present frameworks, and outline potential choices. These case studies are not only instructive for budding managers but also serve as fertile ground for training large language models. Regrettably, this synergy has yet to materialize.
Currently, ChatGPT’s programmers have only introduced a fraction of publicly available case studies into the model. The real goldmine of data remains in the custody of major providers like Harvard Business Publishing, boasting over 50,000 case studies, and the non-profit Case Center. If these custodians collaborate with the creators of large language models, a language assistant—capable of handling programming, copywriting, and customer inquiries—could evolve into a formidable decision-making ally for enterprises.
Moreover, future advancements in learning algorithms promise more efficient models, paving the way for “medium-sized language models” that no longer require ingestion of vast swathes of the internet and complete libraries. Instead, they’ll focus primarily on texts and documents relevant to specific domains. This shift is inevitable. The economic incentive for more informed business decisions is irresistible, propelling ChatGPT’s transition from today’s version to a potent future iteration aptly named “DecisionGPT”
ChatGPT’s true strength, and that of similar systems, lies in their ability to juxtapose and contrast analogous situations—a critical need in many management decisions. Rarely are these decisions wholly unique; countless managers before us have grappled with similar choices. The better we articulate these decisions in human language, describing how the decision-making framework was established, options were weighed, and choices were made, the more powerful DecisionGPT becomes for fostering well-informed decisions.
In due course, numerous management decisions could become ripe for automation. Robo-managers might be deployed more frequently and sooner than many corner office executives currently anticipate.
However, for now, the advantage lies with those managers who harness available tools to refine their decision-making processes. Rather than seeking definitive answers from models like ChatGPT, we should engage them throughout each stage of the decision-making journey.
