As “Intelligence” Becomes Easier to Access, What Becomes More Valuable?

The first in a series of 3 thought leadership pieces on AI, human capability and the changing role of finance.

There is a workforce problem sitting underneath much of the conversation about AI.

Organisations are struggling to find experienced people. Expertise is expensive and often concentrated in too few hands. Junior employees take time to develop judgement. Meanwhile, skilled people still spend significant amounts of their day searching for information, reconciling data, preparing documents and completing work that technology is increasingly capable of doing.

At the same time, AI can access, analyse and synthesise more information than any individual could reasonably absorb.

So perhaps we are asking the wrong question.

Not simply:

What work can AI replace?

But:

As “intelligence” becomes easier to access, what becomes more valuable in people?

I use the quotation marks deliberately.

AI can increasingly retrieve information, recognise patterns, compare documents, analyse data and produce sophisticated answers.

But organisational intelligence was never simply about knowing the answer.

It is about knowing which question matters — and what to do with the answer.

Could AI extend expertise rather than simply replace work?

MIT economists Daron Acemoglu and Simon Johnson argue that technology does not have one inevitable impact on work. We can pursue automation that substitutes for people, or develop technology that complements workers by giving them better information and enabling them to undertake more sophisticated tasks. Their broader argument is particularly relevant as organisations decide how AI will be deployed.

Because one of AI’s most interesting possibilities may be its ability to extend expertise.

Research by Erik Brynjolfsson, Danielle Li and Lindsey Raymond studied more than 5,000 customer-support workers using a generative-AI assistant. Productivity increased by approximately 14% overall, but the gains were substantially greater—around 34%—for novice and lower-skilled workers.

That is fascinating.

It suggests AI may allow less-experienced people to benefit from patterns and practices that previously took years of exposure to accumulate.

Imagine applying that idea more broadly.

A junior professional approaching a difficult issue could have an AI tool asking:

• What evidence supports your conclusion?
• What are you missing?
• What alternative explanation could there be?
• Who will be affected?
• What happens if nothing changes?
• What would you recommend—and why?

Used this way, AI is not simply answering the question.

It is helping someone learn how to interrogate the problem.

That distinction matters.

The human premium moves

If AI can help with the what, the value of the person increasingly lies in the why, so what and now what.

• Why did this happen?
• What is connected to it?
• What doesn’t make sense?
• How does it affect different stakeholders?
• What trade-offs are involved?
• What action should we take?
• And how do we communicate that action in a way that brings people with us?

This shifts the premium towards capabilities such as critical thinking, synthesis, curiosity, judgement, problem framing, communication, influence and adaptability.

And one skill that may become especially important:

knowing enough to challenge the machine.

The danger is that we confuse better output with better capability.

If AI continually gives someone the answer, we may increase productivity without increasing judgement.

So organisations have a choice here too.

Do we build tools that replace thinking, or tools that develop better thinkers?

Development also becomes more personal

That leads to another question for individuals.

The capabilities required from us may be changing faster than our job descriptions.

Self-awareness therefore matters—not as another personality exercise, but as a practical development question:

How do I naturally work today, what will my role increasingly require tomorrow, and where do I need to stretch?

A work-preference framework such as Team Management Systems can provide one useful starting point for that conversation.

But preference should never become a limitation.

The purpose is not to label somebody as an analyst, organiser, explorer or adviser and leave them there.

It is to understand the starting point and deliberately build what comes next.

Because perhaps future employability will depend less on having accumulated the perfect set of skills and more on the ability to keep learning, adapting and moving beyond what comes naturally.

Finance offers an early glimpse of this future

Finance has been talking about transformation for decades.

• The strategic CFO.
• The finance business partner.
• The commercial adviser.
• The problem solver.

Yet many capable finance teams continue to spend enormous amounts of time collecting, reconciling and reporting information about what has already happened.

AI can accelerate much of that work.

That does not reduce the importance of finance.

It raises the expectation.

If technology can increasingly produce the analysis, the finance professional must provide the insight and action.

Not:

Revenue is below budget.

But:

Why?

• Is it price, volume, productivity, mix or customer behaviour?
• Is it temporary or structural?
• Who can influence it?
• What happens if we do nothing?
• What should management do now?

That requires much more than accounting capability.

It requires the finance professional to become an analyst, strategist, challenger, communicator and problem solver.

In aged care, the difference matters

The financial sustainability challenges emerging under Support at Home illustrate why.

StewartBrown’s March 2026 survey reported a sharp deterioration in performance, with average operating EBITDA in the March quarter falling to just $23 per client per annum.

But knowing that profitability has deteriorated is the easy part.

Understanding why requires connecting pricing, service volumes, utilisation, workforce capacity, productivity, care management, overhead recovery and possible revenue leakage. StewartBrown’s analysis shows, for example, how utilisation affects both margin and the recovery of overheads.

AI can help bring that complexity together.

The human still has to decide:

So what do we do?

That may ultimately be where human value moves.

Perhaps the opportunity is bigger than productivity

The question about AI and employment is often:

Which jobs will disappear?

I think organisations should spend just as much time asking:

What could our people become capable of doing that they cannot do today?

• Could junior employees reach higher-value work sooner?
• Could scarce experts extend their knowledge further?
• Could managers understand complex problems faster?
• Could skilled people spend less time assembling information and more time changing outcomes?

If so, AI becomes more than an automation tool.

It becomes a capability multiplier.

But only if we develop people alongside it.

AI can extend expertise.

It cannot make us curious.

It cannot build trust for us.

It cannot give us the courage to challenge an accepted view.

And it cannot relieve us of responsibility for the decisions we ultimately make.

As “intelligence” becomes easier to access, perhaps what becomes most valuable is people who know how to use it wisely—and who are prepared to keep developing as the definition of valuable work changes around them.

For finance, that raises an uncomfortable but exciting question.

We have spent years asking for a more strategic role.

AI may finally test whether we are ready for it.