Using ChatGPT to Make Better Decisions

Summary.    A successful decision-making process has three steps: Framing the decision, generating alternatives, and deciding between them. Large language models can help at each stage of the process. But while it may be tempting to merely ask ChatGPT for answers,...

Can ChatGPT help executives make better decisions? The large language model everyone has been talking about for months also has an eloquent answer to this question: “Yes, I can support you in management decisions by providing information, facts, analysis, and perspectives that can help you make an informed decision.” ChatGPT immediately follows up with a limitation of its own competence. “However, it is important to note that my advice and recommendations are based on an algorithmic analysis of data and information, and you, as a human being, still have to make the final decision based on your experience, knowledge, and assessment of the situation.”

Fair enough. But despite this dose of modesty — or because of it — large language models like ChatGPT can become powerful decision-making tools for managers and for companies. Their promise isn’t in providing us answers, but in helping us go through a more systematic decision-making process than is often the case today, even with important management decisions.

Three phases characterize well-informed decisions. First, we must define our goals and context. What exactly is the decision about, and based on which goals, values, and preferences? This way, we define the decision-making problem and set the decision-making framework. The second step is to develop choices: What decision-making options are available to us? The goal here is to generate many different alternatives and not, as is all too often the case, to focus just on the obvious options. Only when we have developed sufficient options from the decision-making framework can we evaluate them and make a well-informed decision in a third step.

Used skillfully, ChatGPT can already provide valuable services in all three phases for business decisions in its current training state. In practice, this means we can enter into a dialogue with the system on any of the three phases of a well-informed decision-making system. When evaluating decision-making alternatives, we can ask, for example: What mistakes do managing directors of large, medium-sized companies in mechanical engineering make when they decide to expand into new markets? And what were the success criteria for a successful expansion?

ChatGPT then does not provide us with a template with which we can weigh the options perfectly in our case. But it can help us uncover our own biases and challenge preconceived notions. Using ChatGPT cleverly can be like a de-biasing tool that has seemingly read Daniel Kahneman and Amos Tversky intensively. It thus offers food for thought to better reflect on how we can evaluate the options in a more well-informed way.

The system is already even more valuable today when it is employed to work out additional options that we can not think of or easily come up with. This way, it broadens our decision-making horizons, and we understand that there are many more and more far-reaching decision-making options than we realize.

How do we reduce our dependence on China and diversify a supply chain? A managing director and his team may never have dealt with this decision-making question before. ChatGPT, however, may be able to offer up many of the strategies documented on the internet by companies in a comparable situation and may come up with more original ideas than simply relocating production to Vietnam. This is because the system has access to a part of the publicly available treasure trove of options in the industry or company class.

Large language models can also help set goals and preferences, evaluate the decision-making circumstances, and select the decision-making framework. Again, dialogue is key. With the right questions, we become the interlocutor to better understand the context of a decision. For example, with ChatGPT, we can quickly see suggestions of what typical goals other companies might have had in mind in a comparable decision-making situation. For example, a prompt might look like this: “Hi ChatGPT, I am the head of a successful, mid-sized tooling manufacturer outside Columbus, Ohio. I am having difficulties attracting new talent, especially engineers. What may be the reasons for this? What strategies are similar manufacturing companies employing to cope with the talent shortage?”

The bottom line is: ChatGPT is becoming an increasingly intelligent conversation and sparring partner. It does not relieve us of defining the decision-making framework, working out a wide range of options, and evaluating them. However — and here, the self-assessment from the beginning of this article is correct — it does provide interesting perspectives. A large language model has several advantages compared to a human sparring partner: It does not pursue its own interests and does not want to please the top decision-maker, for example, to promote its own career. It is not subject to internal group thinking and bureaucratic politics and is also much cheaper than external management consultants or internal strategy departments. This also means that ChatGPT may make the preparation and assistance of decisions for smaller companies cheaper, leveling the playing field.

The future of case studies

Budding managers at business schools are already indirectly learning about decision-making through a large number of case studies. The aim is to acquire a repertoire of decision-making models by developing and evaluating possible options for action within a decision-making framework. Of course, case studies do not contain a solution in the form of a perfect answer to a specific decision-making situation. In case studies, questions are raised, decision-making frameworks are presented, and decision-making options are outlined. Not only can prospective managers learn from and with these case studies, but they can also be used to train large language models. However, this has not yet happened.

ChatGPT’s programmers could only feed their model a fraction of publicly available case studies. The real treasure trove of data is exclusive and stored at the major providers such as Harvard Business Publishing (HBR’s parent company), with over 50,000 case studies or the non-profit Case Center. If the custodians of these business case studies team up with the makers of large language models, a language assistant for programming, copywriting, and customer inquiries could turn into a powerful decision-making assistant for companies.

This will also get easier in the future because the learning algorithms are becoming more and more efficient, and thus “medium-sized language models” will also be possible, in which it is no longer necessary to feed half the Internet and entire libraries, but above all the texts and documents relevant to the specific field. It is only a matter of time before this happens. In any case, the economic incentive for more informed business decisions is excellent and will propel the transition from today’s ChatGPT to an even more powerful future we might dub “DecisionGPT.”

The great strength of ChatGPT and similar systems is to compare and contrast similar situations. This is precisely the most important need in many management decisions. Very few of the decisions managers face are unique. Thousands, sometimes even millions, of managers before them have had to make a similar choice. The better it is described in human language how they set the decision-making framework, weigh the options, and make their decision, the easier it is for DecisionGPT to become a powerful tool for more informed decision-making.

Eventually, many such management decisions could be automated. Robo-managers could be deployed sooner and more often than many executives in their corner offices may believe today.

In the meantime, though, the advantage will go to managers who use currently available tools to improve their decision-making process. Don’t ask models like ChatGPT for answers; probe them to each stage of the decision-making process.