The Hidden Power Of Questions In The Age Of AI

Forbes

11-07-2025

Bob Pearson , Chair, The Next Practices Group. getty
When a child learns to speak, they pepper us with questions—an instinct rooted in survival. In 2013, a British study of 1,000 mothers found that children asked their parents more than 300 questions per day at an hourly rate that rivals the pace of the Prime Minister's Questions time.
Questions help us navigate life and our roles within organizations—clarifying expectations, accelerating learning, building relationships and managing risk.
To understand how questions help cut through complexity, consider Shannon's Theorem, which was created by Claude Shannon, the father of information theory. The theorem offers an equation for how much data can be sent across a communications channel in the presence of noise.
Shannon was working at Bell Labs, so he was mainly focused on channels like a telephone line or a radio band. At organizations today, however, we still need to understand how to eliminate the noise that distracts us as we toil away on our projects. This is the role of questions: to help us focus.
Understanding which questions to ask at a given time point helps reduce uncertainty, which is fundamental to how we utilize machine learning, decision trees and the field of data science. This ability to ask the right question—especially as AI becomes central—isn't just a technical skill but a foundational one. The Value Of Questions Within Organizations
If we know which questions to ask at each key point for a given task, we can increase knowledge and reduce uncertainty. Relevant questions help us shape workflow, meet customer needs, teach teams and build trust.
Of course, being human can also be our biggest obstacle. Too often, we stifle questions, prioritizing output over whether work is done optimally or scalably.
So, what is the importance of questions within organizations, and why do we need to improve how we use them?
To start, questions can lead to new information, frame a problem or check our own bias. Questions can just as easily unlock innovation as they work their magic to keep us on track, so we scale workflow efficiently.
Imagine two scenarios.
In the first, you are leading an SAP transformation project for a Forbes 2000 company that will exceed $300 million in cost and occur over three years. Your job is to make it happen on time and on budget.
If you break down your project, you have 12 workstreams and 100 individual tasks per workstream. That is 1,200 different time points where you want to ensure your team understands what to do, how to handle unforeseen issues and accomplish each task. Email, teams and spreadsheets are not enough.
Now, imagine your friend leads the development of a new drug in BioPharma. She said she has 60 key decision points in discovery/preclinical, 20 for an Investigational New Drug submission and 40 for Phase 1 trials. Getting a new drug into the clinic has 120 key decision points.
In each case, we can proactively identify the top five questions that align with each decision. As your team gets ready for each decision point, they look at the five questions to ask themselves and their team. Did they address a certain problem? Did they categorize the expense associated with this action? Do they have a reason to believe this action could be improved?
This process of structured questioning is incredibly powerful, but also time-consuming. That's where AI enters the picture. How AI Can Shape Question Asking
The subject matter experts of the world are the heroes here. They have been there, done that and know what questions to ask at each point. AI can then supplement their knowledge to create a detailed list of questions for every decision point.
As the user touches any point on the SAP transformation, the key questions to address will appear. Those questions will be linked to background information, and questions answered by other project members will become available, as they apply to that particular task.
Now, questions are a quality check and a way to contribute to the enterprise workflow. It's a team sport.
AI is ensuring that knowledge gained anywhere in the world is being shared precisely to the right time points in a project in real time.
AI platforms can learn, in real time, what questions are most effective, what answers are most important and what type of backup information helps teach, educate and answer our questions. Questions and content can be translated into any language, enabling ideas to emerge from anywhere.
To achieve this vision, we need to adjust a fundamental habit that drives us today.
Ever since Google first set sail in 1998, we have been conditioned to write a few keywords or phrases, so our questioning ability is rusty. Now, with generative AI, we must flex our 'question muscles,' as we realize that the value of information we receive is dependent on our ability to ask the optimal question. This emerging skill—known as prompt engineering—is critical for tapping into AI's full potential.
Keep in mind that our effective use of generative AI will help us mirror our own intelligence. Conclusion
We start life as curious humans. We should never let that trait dissipate.
Our job, with AI, is to remain as curious and disciplined as we were as toddlers. We are just now applying this approach to major global projects.
A map of questions for each decision point. An AI back end to share answers and related content. The ability of any team member to contribute to the knowledge of the enterprise. Exploring the effective use of AI is more important than lamenting on what jobs will go away.
What is required? Pretty simple. We need to change our habits and approach as we embrace the advances of AI.
The 'curious companies' will complete that SAP transformation for $200 million and a year earlier, or they will create a more effective clinical trial design of a new treatment, based on a new style of learning and scaling.
The question we are left with is, when do we make this a reality?
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