In the Age of AI, Asking the Right Questions Matters More Than Having the Right Answers
AI can answer your clients' questions. Your job is to ask the ones they haven't thought of yet.
A client asks their Accountant “What’s the depreciation rate for my commercial espresso machine?”. The accountant pulls up a language model, types the question and hits enter. They watch as a clear, well-structured answer appears in seconds, complete with the 10-year effective life, prime cost and diminishing value rates, and a link to the ATO's asset classification.
The answer is correct.
This is the part people aren’t talking about enough. Not that AI might get answers right someday, it’s that it already does. And professionals across the knowledge work sector are quietly grappling with what that means for their role.
If AI can produce a technically correct answer in seconds, what happens to the people who have a part of their professional identity and worth built on having those answers?
The old model
For most of my career in financial services, value was tightly coupled to knowledge. If you were the person with the answer, you were the person with the value.
Clients called their Accountant, Adviser, or Specialist because that person knew the rules, the edge cases, the technical detail. Years of training and experience created a moat. Knowledge was scarce, and access to it mattered.
That world is changing fast.
What’s changed?
Pure recall knowledge is no longer scarce.
Today, a customer can ask a sophisticated tax question to a language model and get a clear response instantly. This is already happening, more often than many professionals would like to admit.
Think about the difference:
Old world: Professionals are gatekeepers of knowledge.
Example: You ring your Accountant and ask a specific tax question. They interpret the legislation, explain the rule, and tell you what applies.New world: Knowledge is democratised, available to anyone who seeks it.
Example: You ask an AI model the same question. It explains the rule, cites the conditions, outlines common pitfalls, and gives you a clear answer. Often good enough. Sometimes excellent.
So where does that leave the professional? If the answer itself is easy to retrieve, what’s the role of the expert?
The uncomfortable truth
AI machines will beat us all day long at question and answer.
They don’t get tired. They don’t forget. They don’t miss a clause because it was buried on page 73 of a ruling. And over time, they’ll only get better.
But this doesn’t mean humans are becoming irrelevant. It means the centre of our value is shifting.
The real move is away from being the person who knows the answer, and toward being the person who knows what to ask.
The big question is: are we professionals building that skill, or defending the old one?
What “asking better questions” actually means
Asking better questions means helping clients understand what problem they’re actually trying to solve.
Most clients don’t arrive with the right question. They arrive with a symptom, a constraint, or a vague sense that something isn’t working. A client might ask a tax question, but the real issue could be cash flow timing, risk exposure, business structure, or long-term trade-offs they haven’t articulated yet.
AI can answer the question that’s asked. Humans create value by framing whether it’s the right question at all.
That’s the difference.
In practice
Take another example. A business owner asks an Accountant whether they should switch from a sole trader to a company structure.
An AI model can explain the tax implications, the compliance obligations, and the costs involved. That’s useful. But a professional who adds real value will ask:
What’s prompting this question now?
Are you optimising for tax, liability protection, or something else?
What does the business look like in three years from now, and does this structure fit where you are headed?
Those questions often lead to a very different conversation, and often to a very different solution.
The answer was never the point. The journey to clarity was.
The bar is rising
Some of the best advisers will read this and think: we’ve been doing this all along.
And they could very well be right.
But what’s changing is the importance of this skill. As AI eats into the layer of technical recall and explanation, the remaining human contribution becomes more visible, more valuable, and more necessary.
Professionals who stay anchored to being the source of answers will feel increasing pressure. Professionals who lean into framing, judgment, context, and questioning will differentiate.
Where humans still win
It’s not all doom and gloom for professionals. Humans are still uniquely good at a number of key things. In the case of professional services, the most important quality they have is the ability to operate across ambiguity.
Humans can hold competing objectives, incomplete information, emotional context, and long-term consequences in our heads at the same time. We can sense when a client is asking the wrong question because they’re uncomfortable with the real one.
AI doesn’t do that, at least not yet. Certainly not in the way that matters in complex human systems like businesses, families and lives.
Our value isn’t in the answers we give. It’s in the questions we ask, the paths we help uncover, and the decisions we help shape.
We’re all in this together
I've used Accounting as the lens here because it's a world I know. But this story belongs to every profession built on expertise.
Lawyers, Consultants, Financial Advisers, Architects, Software Engineers, HR Professionals: anyone whose value is tied to specialised knowledge is navigating the same terrain.
The details differ. The underlying challenge doesn't.
The opportunity ahead
This isn’t a story of replacement, it’s a story of reorientation.
AI isn’t taking away the need for professionals. It’s removing the need for professionals to prove their worth through recall knowledge alone.
That’s a gift, if we choose to see it that way.
The future belongs to professionals who can guide, frame, challenge, and think creatively alongside their clients, not just give them answers. Professionals who internalise that shift early won’t just survive this transition, they’ll define what great looks like on the other side.
In the age of AI, asking the right questions matters more than having the right answers.
If this resonated, share it with a colleague who’s navigating the same shift.
Thomas.
PS - I’ve got a few other articles in the works. I’d love you to weigh in on which you’d like to see next.
Vote below:
Thanks for writing this, it clarifies alot. Is just asking enough when problems get really complex, or does understanding the context still matter most? Really insightful piece.
Great read, thanks! 🙏 a few thoughts
1. I think your framework is adequate in explaining the pattern for the next 3 years, absolutely. After that, it’s fair to say we are all guessing.
2. Further to 1, I believe a framework for what work and careers like has to focus heavily on an “industrial organization” lens. Meaning, decomposing the value chains of any particular service, and looking at how AI and humans factor in at specific points in the value chain. That analysis is fairly straightforward, but becomes complicated because the client industry is decomposed as well as the service providers industry.
3. Another piece of the analysis is grappling with the likelihood that 50% of service white collar jobs will be eliminated in the next 5 years. If we look at one industry, say Big law, we’d assume that those people (highly educated) get replaced into other industries. In this moment, that assumption fails because ALL industries are reduced by 50%…. So how do we model that?
4. My answer to 4 is to imagine a sort of entrepreneurial boom. Meaning that smart people with no formal jobs will create new products, services and industries. I’ve seen that happen in the past namely in tech. However we are talking about a massively different scale here… but this point (4) is probably the most interesting to unpack. It’s looking at the second - fourth order effects of industrial change, in value chains and then in the labor market.