Here is an excerpt from an article by Christian Stadler and Martin Reeves for the MIT Sloan Management Review. To read the complete article, check out others, and obtain subscription information, please click here.
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When generative AI’s capacity for strategy creation is put to the test, it reveals where its strengths lie — and where humans still have the edge.
When Geoffrey Hinton, a pioneer of deep learning, quit Google recently, he made it clear that he is worried about the risks of artificial intelligence. He is not alone. Following the launch of ChatGPT-4, thousands of artificial intelligence experts signed a letter calling for a pause in the development of more-powerful AI systems.
On the other hand, excitement about the opportunities arising from large language models (LLMs) and their speed of adoption is unprecedented. Microsoft was quick to integrate the new technology into its Bing search engine, and the company’s founder, Bill Gates, stated that ChatGPT will “change our world” without necessarily putting jobs at risk.
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While speculating about the future of AI is irresistible, the more practical question is how we can use it right now. Conversations about this are taking place in classrooms, newsrooms, and workplaces around the world.
As business strategists, we wanted to see what generative AI could add to our work. We explored this question through a series of experiments on different aspects of the strategy creation process. In each of the experiments, we put a realistic question of strategy to ChatGPT, followed by a lengthy back-and-forth to refine the initial responses. The intention was to understand how the tool can support ideation, experimentation, evaluation, and the building of stories — and where it falls down.
Three lessons emerged from these experiments. [Here is the first.]
1. Expect interesting input, not infallible recommendations.
In one of our experiments, we asked ChatGPT to suggest some disruptive business ideas for a large European transport provider. The chatbot suggested a personalized planning app, a ride-sharing service, hyperloop transportation, and a smart-luggage delivery service. Coincidently, the first three matched ideas from a recent workshop with a transport provider in another European country. The tool was also able to provide business models and cost estimates for these business ideas.
On the one hand, this is impressive. At the same time, it highlights that the tool seems unlikely to come up with ideas humans can’t, although it gets results faster and with less effort. Several other experiments confirmed this.
The tool seems unlikely to come up with ideas humans can’t, although it gets results faster and with less effort.
In another experiment, it also became obvious that humans are better at translating ideas into actions. For example, when we asked ChatGPT to come up with an idea for a new streaming service, the list of suggestions included a service focused on education. When we asked what it would take to realize such an idea, it proposed a partnership with either a university or an established consulting firm. While that made sense, ChatGPT’s suggestions for how to win over such partners were not sufficiently concrete and realistic. The current iteration of the tool, which is based on training set data in the public domain, isn’t able to capture the nuances of each corporate reality.
Still, LLMs can be helpful in the strategy process for a few key reasons:
They are fast and easy to use. You can avoid all the logistical effort required to get the right people in the room at the same time.
The tendency toward conventional thinking can be mitigated by asking the right follow-up questions. For example, when we asked for an innovative new concept for a bakery, ChatGPT initially suggested just offering savory products. We then asked for more creative ideas and got a space-themed bakery, with robotic assistants and bakers, zero-gravity dining, and 3D food printers, which was too expensive to realize. In a third attempt, the ideas became more interesting — for example, a bakery that uses artificial intelligence to analyze data about customer preferences and food trends to create and design unique flavor combinations for baked goods. From there, we were able to probe about steps to get started on realizing this idea.
It’s easy to generate many ideas. The most interesting ideas were, however, mixed with implausible, impractical, and untested ones. Only by sifting through them in a discerning manner can you get a better understanding of what might actually work.
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The bottom line: ChatGPT and similar tools are more likely to be useful in particular steps of strategizing (like idea generation and storytelling) and to experienced strategists rather than naive beginners. Such tools are, therefore, no substitute for the cultivation of strategic minds. And just as airline pilots must avoid over-relying on an autopilot, would-be strategists must avoid impeding the development of their own discriminating powers.
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