Earlier this year, I was staring at our 2026 plan and thinking: there are too many things in here. Payroll, billing, cross-border payments, payment licences, more salespeople, more product development.
Individually, almost every initiative had a reasonable argument behind it. The problem was that they couldn’t all be equally important. So I uploaded the numbers, roadmap and assumptions into AI and asked one simple question:
“Assume I’m wrong. Destroy this strategy.”
I wasn’t asking AI to decide our strategy. I wanted it to find the weaknesses in mine.
My AI journey started with simpler things
At the beginning of 2026, I started using AI more seriously in my work as a senior manager. The first use cases were practical: researching regulatory changes, preparing weekly market briefs, summarising long documents and email threads, and reviewing things I had written to see whether I had missed an important point.
It saved time. An hour of reading could sometimes become a 10-minute first pass.
But after several months, I realised the biggest value wasn’t speed.
It was disagreement.
The higher you go, the harder it can be to hear: “I think you’re wrong”
After many years in management, especially in Indonesia, I’ve noticed something: the more senior you become, the harder it can be to hear a direct “I think you’re wrong.”
It isn’t always fear, and it doesn’t automatically mean the culture is unhealthy. Sometimes it is simply respect.
A younger manager may strongly disagree with an idea from the CEO, but instead of saying, “This won’t work,” you hear, “Maybe we should also consider another alternative.”
The disagreement is there. You just have to learn to hear it.
Of course, this is not everyone. I’ve worked with people who challenge me directly, and I value them enormously. But hierarchy changes the way people communicate, and that creates a real problem for senior leaders: the more authority you have, the easier it becomes for your ideas to be protected when they should be challenged.
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That is where AI became unexpectedly useful. It doesn’t care about my title, my reaction tomorrow, or its performance review. I can ask it to attack the idea without worrying about hierarchy.
Why I think of AI as the “final boss”
If you grew up playing games, you know the final boss is the last real test before you finish the level. That is increasingly how I use AI for important ideas.
Before I become too attached to a strategy, I want something to attack the assumptions, find the weak points and force me to defend it. If the idea survives, I trust it a little more. If it doesn’t, better to find out before the management meeting.
The goal is to find where my own argument breaks. So I ask questions like:
- Does the sales capacity actually support this revenue target?
- Are we spreading engineering resources across too many products?
- What happens if the licence arrives six months later?
- Which assumption am I defending simply because it is mine?
None of these questions is revolutionary. That is partly the point.
Good strategy often fails because we avoid obvious questions when we are emotionally invested in the answer. Twenty minutes of this exercise can make the next management meeting much more useful. Not because AI solved the problem, but because I arrived with better questions.
But the final boss sometimes cheats
There is one problem: AI can challenge my bad assumptions while introducing bad assumptions of its own.
I’ve seen answers that were beautifully written, numerically precise and completely convincing, until one important fact turned out to be wrong.
In financial services, that matters. A regulation that is “almost correct” can still lead to a very wrong decision. The same is true with financial models: the calculation may be perfect even when the commercial assumption underneath it is unrealistic.
So I now separate argument from fact. I let AI attack the argument. But regulations go back to the actual regulation, market numbers go back to the original source, and financial assumptions go back to finance or the people closest to the business.
AI can tell me where to look. It doesn’t get to decide what is true.
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There is still a decision it cannot make
Even after all the debate, there is a point where analysis ends.
AI can tell me Product A has better projected economics than Product B. It can argue that we have too many projects, or recommend moving resources from one team to another.
But eventually someone has to decide which consequence they are willing to own.
AI does not sit across the table from the employee affected by that decision. It does not know which customer relationship took three years to build. It does not have to explain six months later why the decision turned out to be wrong.
I do.
That is the line AI still does not cross for me.
What changed for me
Before AI, I thought better tools would mainly help leaders get answers faster. Now I think the more interesting benefit is getting challenged faster.
The blank page is cheaper. Research is faster. First drafts are easier. But that moves the real work somewhere else:
- Is this actually true?
- Which assumption is weakest?
- What am I not seeing because I want this to work?
For simple work, I still ask AI for answers. For important work, I ask it to push back.
And when I disagree with the challenge, I often find myself doing more work. I reopen the numbers, check the regulation, look for another source, or ask someone closer to the issue.
Sometimes I’m simply trying to prove AI wrong. But in doing that, I usually understand my own argument better.
It doesn’t just give me answers. It makes me earn mine.
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