When Intelligence Becomes Abundant, Trust Becomes the Advantage
I recently watched a short video (link in the footer) that begins with a tourist being offered a photograph of himself at Niagara Falls.
The image looks perfect. The lighting is right. The waterfall is behind him. It appears to capture the experience exactly as it happened.
There is just one problem.
No photograph was ever taken.
The photographer was there only to create the experience of being photographed. The finished image was generated entirely by AI.
The seller cannot understand the tourist’s objection. If the image is indistinguishable from a real photograph—and accurately represents what a photograph would have looked like—what is the difference?
But the tourist knows immediately that something important has been lost. “That isn’t me. That isn’t Niagara Falls. No moment in time was captured.” The image may be visually accurate, but it is not historically authentic. It depicts the event without being evidence that the event occurred.
The video then offers a useful analogy.
If I met Michael Jordan and received his autograph, but subsequently lost it, could I simply create an indistinguishable reproduction?
The meeting still happened. The memory is real. The recreated signature might be perfect.
But Michael Jordan did not touch that piece of paper.
Its appearance might be identical, but its provenance is not.
When appearance is no longer evidence
We are rapidly entering a world in which almost anything can be made to look convincing.
AI can generate a polished strategy, business case, financial analysis, board paper, project plan, risk report or transformation dashboard in seconds.
It can manufacture the appearance of intelligence.
But the quality of the presentation tells us very little about whether:
the underlying information is reliable;
the evidence is complete;
the assumptions have been challenged;
the controls actually operated;
the conclusions are logically supported;
someone accepts accountability for the decision;
the reported outcome genuinely occurred;
the organisation has learned anything.
Plausibility is not the same as truth.
Visual accuracy is not the same as authenticity.
And an impressive answer is not necessarily a trustworthy answer.
As intelligence becomes abundant and cheap, trust becomes scarce—and therefore more valuable.
What does this mean for accountants?
Throughout my career, I have believed that the defining contribution of accountants goes well beyond technical competence. We think about debits and credits, profits and losses, costs and benefits, risk and reward.
But we also think about right and wrong.
Ethics and trust are at the heart of what we do.
Historically, the accounting profession has helped create trust around financial information. We developed disciplines for testing assertions, examining evidence, challenging assumptions, establishing controls and holding people accountable for what they report.
Those disciplines become more important in an AI-enabled world—not less.
But accountants cannot assume that trust belongs to us automatically.
Trust cannot simply be claimed.
There is an old principle in comedy: don’t tell me you’re funny—tell me a joke.
The same applies here. Don’t tell me you’re trustworthy. Show me the evidence. Show me the controls. Show me how the conclusion was reached. Show me who exercised judgement and who accepts responsibility for it.
Don’t ask me to trust the answer. Show me why it deserves to be trusted.
If the profession remains focused primarily on transaction processing, compliance and the production of historical financial statements, much of that work will inevitably be automated.
The larger opportunity is to extend our role.
The accountant of the future should help organisations answer questions such as:
Can we trust the data?
Can we trace where this information came from?
Can we explain how the model reached its conclusion?
Have the assumptions been tested?
Who made the decision?
Who is accountable for acting on it?
Did the control genuinely operate?
Was the claimed value actually created?
Was it achieved ethically?
What has the organisation learned?
This is not merely assurance over the numbers. It is trust in how the organisation knows, decides, acts and improves.
Trust is organisational infrastructure
We often describe trust as though it were a cultural aspiration or brand characteristic.
I think it is more concrete than that.
Trust is an organisational capability.
It is created through a connected system of reliable evidence, transparent decisions, clear accountability, effective controls, governed action, verified outcomes and institutional learning.
When those elements work together, leaders can have confidence not only in the reports they receive, but in the organisation’s underlying ability to perform.
When they do not, management can be surrounded by increasingly sophisticated dashboards and still have very little idea what is genuinely happening.
That distinction will become critical as AI makes it easier to generate strategies, recommendations, explanations and apparent evidence.
The future will not suffer from a shortage of answers.
It will suffer from a shortage of answers that boards, executives, employees, investors and society have good reason to trust.
From artificial intelligence to organisational intelligence
This is why I believe the real competitive opportunity is not simply the adoption of more AI.
Every organisation will have access to increasingly capable technology. Every organisation will be able to generate analysis, content and recommendations that once required significant time and expertise.
AI itself will increasingly become a commodity.
The advantage will come from how effectively an organisation integrates that intelligence into its operating model: its processes, systems, governance, controls, decision rights and people.
And, critically, whether it can preserve trust while doing so.
I describe this as Organisational Intelligence: the organisation’s ability to convert information into sound decisions, coordinated action, measurable value and continuous learning.
AI can contribute enormously to that capability. But it cannot establish it on its own.
AI can generate an answer.
An intelligent organisation must establish whether the answer is reliable, decide what to do with it, assign accountability for acting and learn from the outcome.
That is fundamentally a management and governance challenge.
It is also an enormous opportunity for the accounting profession.
So, did I write this article?
There is, of course, an unavoidable question hanging over everything you have just read:
How do you know that I—Brendan Sheehan—actually wrote it?
The honest answer is that I developed it in conversation with AI.
I supplied the experience, the professional beliefs, the context and the central argument. AI helped me interrogate the idea, structure the reasoning and develop the language. I then reviewed it—very critically—to decide whether it genuinely represented what I believe and what I was prepared to put my name to.
That is not literally the Turing Test. Alan Turing’s “imitation game” asked whether a machine could communicate convincingly enough to be mistaken for a human. But it is a close relative of the question we now face everywhere:
Can you tell whether intelligence came from a human or a machine—and, increasingly, does that distinction matter?
I think the more important question is different.
Not: Who typed every sentence?
But:
Where did the ideas come from?
Has the reasoning been tested?
Has the use of AI been represented honestly?
Does the named author genuinely believe it?
Is someone willing to accept responsibility for it?
Authorship in an AI-enabled world may become less about who physically constructed every sentence and more about who exercised judgement, supplied meaning, verified the result and accepted accountability for publishing it.
I am comfortable putting my name to this article because it expresses something I genuinely believe.
That accountability is part of its provenance.
And provenance matters.
Because AI can manufacture the appearance of intelligence.
Trust must still be earned.
P.S. Oh yeah, the photograph. Niagara Falls is definitely on my bucket list—but I haven’t been there yet. That’s all AI too.
Check out the original video that inspired this article by @baron-ryan-7aa4b6197 (LinkedIn) and @americanbaron (Instagram) -> https://www.instagram.com/reel/DZ31r6jAlEF/?utm_source=ig_web_copy_link&stkn=NTc4MTIwNjQ2YQ==
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