Honest take: 3 AI analytics projects tried this week — what worked, what didn't
A data analyst ran three real AI-assisted analytics projects in one week and shared candid results. They break down which worked well, which was so-so, and which flopped. It's a practical reality check for anyone thinking about using AI tools at work.
There's a lot of hype around using AI for data analysis, but real-world results vary widely. This post stands out because it's based on hands-on experience from a single week — not theory or marketing — making the findings grounded and relatable.
Each of the three projects reveals where AI genuinely helped and where it fell short. For solo developers or analysts considering similar tasks, this kind of first-person account helps set realistic expectations: when you can safely hand something off to AI, and when you still need to do it yourself.
Key points
Quick term guide
- analytics
- Stats that show things like visits, clicks, and user activity.
- share
- A server folder made available to apps or other devices.
- AI tools
- Software that can help create text, code, images, or other work.
- IDE
- A software tool that combines a code editor, a way to run code, and error checking all in one app.
- developers
- Developers are people who build software, apps, or websites.
- Elo
- A number that represents how skilled a player is in competitive games — it goes up with wins and down with losses.
- FIR
- A First Information Report — the official complaint filed with police in India that kicks off a criminal investigation.
- reference
- Using a source to find information or confirm facts while working.