Unpopular opinion: most "AI will replace analysts" takes are backwards. I've spent months looking...Unpopular opinion: most "AI will replace analysts" takes are backwards. I've spent months looking...
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
Unpopular opinion: most "AI will replace analysts" takes are backwards.
I've spent months looking at real business data gyms, real estate, cloud kitchens, tattoo studios. The hard part was never running the numbers. Any tool can average a column or draw a chart.
The hard part is knowing which question is even worth asking. A gym owner doesn't need to know their average attendance. They need to know which specific members are about to quietly stop showing up, and why. That's not a formula, that's understanding how a business actually breathes.
AI is very good at the first part. It's still bad at the second part, because it doesn't know your business, your customers, or your city.
So no, I'm not worried about being replaced by a tool. I'm more worried about analysts who stopped asking better questions and started just running reports.
We’re choosing the motion for an AI analytics product demo, and I can’t decide.
Both clips show the same flow. Someone asks a question in plain English. The agent checks permissions, plans the query, runs it past guardrails and builds the dashboard.
1. A walks you through each step as it happens.
2. B skips the steps and drops you straight onto the result.
A shows the work. B shows the speed. For a landing page I keep going back and forth.
Which one would make you trust the product more? Comment A or B, and tell me why.
Before you add AI to your product, find the screen where your users already copy and paste.
That's usually where the real AI feature lives. Not a chatbot in the corner, but a step people already do by hand: summarising a ticket, drafting a reply, tagging an upload, pulling fields out of a PDF.
Here's the order I follow when a founder asks me to add AI to an existing SaaS:
1. Pick one workflow, not "AI everywhere." Watch where users leave your app to paste something into ChatGPT and come back. That round trip is your feature.
2. Keep the model behind your own API. One service owns prompts, retries, logging and cost limits, so you can switch providers without touching the frontend.
3. Make it tenant-aware from day one. Each customer's data, prompts and usage stay scoped to their account, or the first enterprise security review will stop you.
4. Ship a draft, not a decision. Let the AI suggest and the user approve. You earn trust and get real feedback before you automate anything.
5. Measure accepted output, not tokens. If users rewrite most of what it produces, the prompt or the context is wrong, and that's your real roadmap.
Most AI features that flop didn't fail because of the model. They were bolted onto the wrong step.
What's the one task your users still copy into ChatGPT?