Every organization today is exploring ways to leverage generative AI (GenAI) to automate tasks and enhance efficiency. However, GenAI offers capabilities that go beyond routine automation. According to a study of online forum posts [1], the most common application of GenAI is idea generation. Millions of people are already turning to large language models (LLMs) for suggestions, creative solutions, and brainstorming. A recent article in Harvard Business Review [2] emphasizes that to unlock the full potential of these tools, we must train ourselves to work creatively with them by asking innovative questions.
The article notes that while many users rely on LLMs for straightforward tasks—such as retrieving information—this approach often leads to predictable results, missing opportunities for deeper creative exploration. To counter this, the article suggests adopting prompting techniques designed to spark originality and challenge the status quo. These techniques include asking “what if” questions, envisioning future scenarios, employing unconventional analogies, letting AI mimic personalities (like Steve Jobs), and exploring seemingly impossible ideas. For example, prompts like “What would this look like in five years?”, “Imagine productivity as a dance—how might my approach change?”, or “What ideas could eliminate the need for my work?” encourage innovative thinking. The article also recommends regular creative sessions to experiment with prompts and record the most effective ones. By cultivating a habit of inventive questioning, AI can become a powerful partner in driving innovation.
These recommendations are both practical and insightful. Using AI as a thought partner to push beyond routine thinking may indeed be one of its most valuable applications. However, the article overlooks some critical limitations of the current approach. First, AI often lacks contextual understanding of specific challenges or projects, which can result in outputs that are generic or insufficiently tailored. Second, because AI generates responses probabilistically based on patterns in its training data, its answers may lack the originality and diversity needed for true creative breakthroughs. Despite innovative prompting, the risk of receiving superficial or uninspired outputs remains.
For instance, consider the prompt, “What would this look like in 10 years?” While this may seem innovative, it’s likely that competitors could ask the same question and receive similar answers. Furthermore, AI’s limited understanding of your unique context could lead to misleading yet persuasive responses that may not align with your specific needs or goals.
Therefore, effective collaboration with AI requires us to sharpen our own creative and critical thinking skills, arguably more than ever before. Asking slightly creative questions won’t suffice if competitors are using similar strategies and AI tends to generate comparable outputs. Ultimately, our ingenuity, agency, and deep understanding of our work are what set us apart. So for one, we should follow the advice in the article and ask increasingly creative questions that help develop unique insights. For the other, we should also answer these creative questions ourselves, alongside AI-generated insights. Our ability to think independently and critically will remain an enduring advantage in an AI-driven world.
References
1. Zao-Sanders, M. (2024, March 19). How People Are Really Using GenAI. Harvard Business Review Digital Article. https://hbr.org/2024/03/how-people-are-really-using-genai
2. Solis, B. (2024, November 27). Train Your Brain to Work Creatively with Gen AI. Harvard Business Review Digital Article. https://hbr.org/2024/11/train-your-brain-to-work-creatively-with-gen-ai
