After working with thousands of professionals who aspire to excel in AI product strategy and development, I have observed a common theme that holds everyone back. It is not attitude (although I would rank it as the second most significant factor); it is not AI know-how, product know-how, project know-how, data science know-how, or even engineering know-how. It is the skill of formulating questions, a crucial part of the inquiry process. That is, a lot of people do not know how to ask meaningful, relevant, and clear questions that drive the inquiry process.
Questioning is the first step toward gaining a deeper understanding, solving problems, and making informed decisions. It involves recognizing what one knows and what one needs to know, and formulating questions that can lead to new insights or solutions. The worst scenario is not asking any questions at all, especially common in situations where we fail to recognize our own knowledge gaps. In other words, we think we know everything about a topic, and that marks the end of learning. Alternatively, some individuals are afraid to ask questions because they fear criticism, while others believe it is impolite to question others. There are even product managers who cannot ask questions to customers.
In my teaching experience, I have also noticed that when people receive information in response to questions they did not ask, they tend to dismiss and forget the information more easily. This may suggest that by asking questions, we establish a channel to receive information.
In the AI era, the ability to ask insightful and strategic questions emerges as the paramount skill. This skill is not just about seeking answers but about understanding the complexities of AI technologies, their ethical implications, and their potential impact on society. As AI systems become more sophisticated, the need for human judgment in guiding their development, deployment, and governance becomes more critical. Asking the right questions helps us identify opportunities for innovation, foresee potential risks, and navigate the ethical dilemmas posed by AI to ensure that AI solutions are aligned with human values and societal needs.
A good question, especially in the context of learning, research, or problem-solving, possesses certain characteristics that make it effective in driving inquiry, fostering understanding, and stimulating thought. Below are some key characteristics of a good question:
Relevance: It is directly related to the topic or problem at hand, contributing to the overall understanding or solution-seeking process.
Clarity: The question is framed in a clear, concise manner, making its intent understandable to others. It avoids ambiguity, allowing for a focused response.
Open-ended: Good questions often are open-ended, encouraging deep thinking, discussion, and exploration rather than merely yes/no answers or factual recall. These questions typically start with "how," "why," or "in what ways."
Provocative: It challenges existing assumptions, prompts re-evaluation of beliefs, or explores new perspectives. A provocative question stimulates critical thinking and debate.
Specific: While being open-ended, a good question also has a certain level of specificity, targeting particular aspects of a topic to avoid overly broad and unmanageable responses.
Inspiring exploration: It leads to further questions, research, or exploration, opening up new areas of inquiry rather than concluding the discussion.
Actionable: Especially in a professional or research context, a good question often leads to actionable insights, guiding the direction of efforts to gather data, conduct experiments, or implement solutions.
Reflective: It encourages reflection on personal experiences, knowledge, or beliefs, deepening personal engagement with the topic.
Structured for engagement: Good questions are structured in a way that invites engagement from the audience, making them feel that their responses are valuable and will contribute to a greater understanding.
Timely: It is posed at an appropriate time, when the audience is ready to consider the question's implications or when it aligns with the current stage of discussion or investigation.
Ultimately, the foundation of learning begins with the art of questioning. It is through our inquiries that we pave the way for knowledge, understanding, and innovation. So, if you want to be successful in AI, start asking questions, good questions, really really good questions.