Nobody's actually mapped where AI work ends and human work begins. Everybody has a hunch. Almost no one has evidence. 

MakeGreen.ai helps businesses put AI exactly where it earns its keep and nowhere it doesn't, cheaply enough that the energy bill stays sane. But the whole field runs on guesswork about which jobs AI is genuinely good at. Vendors oversell. Skeptics underuse. And the business owner stuck in the middle ends up either handing AI work it fumbles or keeping it off work it'd nail. That guessing is expensive, and it's fixable, but only if someone goes and gathers the real numbers instead of theorizing from a conference stage. 

Why it matters 

Until that map exists, every recommendation about AI is a shrug with confidence. 

A business spends on tools it never uses. Or it automates the one task its customers actually wanted a human for. MakeGreen.ai can't give an owner a straight answer about where to start until we know, from data, which kinds of AI handle which kinds of roles well. That's the gap. This project fills it. 

The project 

So we're going to find out the old-fashioned way. Go ask. 

Students fan out across the city and collect first-hand data from real business owners: what work they do, what they've tried handing to AI, what worked, what blew up. Then they bring that messy real-world data back and use AI to analyze it, sorting it into a map of which kinds of AI fit which kinds of roles. Three phases. Gather the data, transform and analyze it, then present what the evidence actually says and what an owner should do about it. 

The output isn't a paper that dies in a drawer. It's a working map MakeGreen.ai uses to advise real companies. 

Why it's right for students 

Here's what you walk away with, and none of it is theory: 

Those are the exact muscles consulting, policy analysis, and strategic planning all run on. You can't fake them, and you can't get them from a lecture. You get them by going out, gathering evidence nobody's gathered before, and being right about what it means.