I use AI almost every day. Sometimes, several times a day.
As someone managing a digital publication and overseeing marketing for an esports business, I’ve come to rely on ChatGPT (and sometimes Claude) for everything from writing and research to campaign planning and website troubleshooting.
It’s saved me countless hours, particularly when it comes to producing first drafts, organising information and working through tasks that would otherwise take considerably longer.
But there’s one thing I’ve learnt from using AI so extensively: just because it sounds confident doesn’t mean it’s right. And the more useful AI becomes, the easier it is to forget that distinction.
When AI gives you an answer that looks perfectly reasonable
One of my more memorable experiences involved something completely unrelated to work: checking Singapore’s TOTO lottery results. I’d asked ChatGPT to help me check the winning numbers against a particular set of numbers on a regular basis.
It seemed like a straightforward task. The results are publicly available, and comparing two sets of numbers hardly requires complicated reasoning. Especially when I had provided the URL to the results page. Except that there were several occasions when the results it provided were incorrect.
What made this frustrating wasn’t simply the mistake. It was the way the information was presented, as though it were factual and verified. I had to question the results and check them against the official source.
It might sound like a trivial example, but it illustrates a much bigger problem. If AI can confidently get something as straightforward as lottery results wrong, what happens when we’re using it to research business opportunities, interpret industry developments or prepare information that other people will rely on?
The issue isn’t necessarily that AI makes mistakes. People make mistakes too. It’s that AI can present inaccurate information in language that gives us very little reason to doubt it.
The more you use AI, the more tempting it becomes to trust it
When I first started using ChatGPT regularly, I approached its responses with a fair amount of curiosity and scepticism. I’d read through the output carefully, compare it with what I already knew and make adjustments.
But once you’ve used a tool successfully hundreds of times, it’s natural to become more comfortable with it. You start recognising how much time it saves. You learn which prompts work. You become accustomed to receiving useful answers almost immediately. And that’s where complacency can creep in.
For The Wellness Insider, the digital wellness publication I run, I frequently use AI to help turn press releases into articles, prepare SEO information and adapt content for social media. It’s an incredibly useful starting point, but I still need to ensure that names, dates, statistics and claims are accurate. A well-written article containing incorrect information is still an incorrect article.
As a publisher, I’m responsible for what appears on my platform. I can’t simply blame ChatGPT if something inaccurate gets published. That responsibility doesn’t disappear because the first draft was generated by AI.
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Sometimes, the problem isn’t accuracy. It’s continuity
Another frustration I’ve encountered is AI losing track of instructions during longer projects. This has been particularly noticeable when working on website development and marketing campaigns.
For example, while revamping two of my websites, I would establish certain design preferences, approve changes and move on to the next task. Then, later in the process, I might find myself having to repeat an instruction or explain a decision we’d already made. At times, something that appeared to have been fixed would require another round of checking.
It’s a strange experience because AI can be remarkably capable at solving a technical problem, yet occasionally struggle to maintain continuity across an extended project. The challenge is that its conversational nature creates an impression of shared understanding.
When you’ve spent hours discussing a project, you naturally expect the other party to remember what you’ve agreed upon. But AI doesn’t necessarily retain or apply every instruction consistently, particularly when a conversation becomes lengthy or complex.
I’ve learnt that important decisions sometimes need to be restated, and completed work needs to be verified rather than assumed. In practice, that means treating AI-generated work as something that requires review, not as a finished deliverable simply because the system says it’s done.
AI can help me make decisions, but it shouldn’t make them for me
One of the ways I find AI most valuable is as a sounding board. When I’m considering a sponsorship proposal, developing a marketing campaign or deciding how to position a service, I can use ChatGPT to explore different approaches. It can help me identify potential weaknesses, organise my thinking and consider alternatives I might have overlooked.
But it doesn’t know everything about my businesses. It doesn’t have the same understanding of my relationships with clients, the nuances of a particular negotiation or the practical constraints I’m dealing with at any given moment.
For example, AI might suggest an ambitious marketing campaign with multiple content formats, outreach activities and promotional channels. On paper, the strategy might look sensible. But if I’m the person responsible for executing most of it, I have to consider whether it’s realistic. A campaign that requires more time, money or manpower than I have available isn’t necessarily a good campaign, regardless of how impressive the plan looks.
AI can help me evaluate options. It can’t decide which trade-offs I’m willing to make. That’s still my job.
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I’ve learnt to manage AI rather than simply use it
The longer I’ve worked with AI, the more I’ve realised that getting value from it requires a certain amount of management. I need to provide clear instructions, explain the context, establish expectations and review the results.
When something is inaccurate, I have to correct it. When the output doesn’t fit my brand, I have to refine it. And when it makes a recommendation that doesn’t make sense for my business, I need to be comfortable rejecting it.
In some ways, this resembles managing a junior colleague, although the comparison has its limits. A human colleague can develop contextual understanding through experience, take responsibility for decisions and recognise consequences in ways that an AI system cannot. AI also doesn’t necessarily know when it’s wrong.
That’s why I think one of the most important skills for anyone using AI isn’t prompt writing. It’s judgement. You need enough understanding of the task to recognise when the output doesn’t make sense.
For factual information, that means checking reliable sources. For technical work, it means testing the results. For business decisions, it means evaluating the recommendations against your actual circumstances.
The amount of checking should also depend on the consequences of getting something wrong. A mediocre social media caption is relatively easy to correct. An inaccurate health article, broken website function or misleading business proposal could have much more serious implications.
I still wouldn’t want to work without it
Despite these frustrations, I remain an enthusiastic AI user. It’s made a meaningful difference to how I work, particularly as someone handling responsibilities that would ordinarily be spread across several people.
I can produce first drafts faster, explore ideas more easily and tackle technical projects that I previously wouldn’t have attempted on my own. It has also really helped me gather my thoughts and opinions when I’m doing a product or service review that may span over a few days or weeks. Those benefits are real. But so are the limitations.
I don’t think the solution is to stop using AI or to distrust everything it produces. That would mean ignoring a genuinely useful tool. Instead, I’ve become more deliberate about deciding when to rely on it and when to slow down and verify the output.
For routine drafting and brainstorming, I’m comfortable allowing AI to do much of the initial work. For factual claims, technical changes and important business decisions, I take a more cautious approach.
Ultimately, AI has changed how I spend my time, but it hasn’t removed my responsibility for the quality of my work. I may have gained an incredibly useful assistant, but I’m still the one who has to make sure it gets things right.
And that’s probably how it should be.
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