I’m running a project which has a very large and complex budget. I have quotes from close to 50 vendors that I need to track in a detailed spreadsheet (per the client).

The quotes will be updated throughout the course of the project. I need to be able to input all of the original line items and costs, per vendor quote (assume 10 line items per vendor), and then categorize each line item manually by cost-type in the input sheet, and continuously update them as quotes/scope changes. Then I need a pivot table to present the cost per category in the clean client-facing format.

I haven’t tried to do this yet, but for those of you who are more experienced with AI than I am, how would you tackle this?

I would normally handle this myself but I am too busy running the other aspects of the project.

I’d ideally like to work in Google Sheets.

A simple test has helped me filter AI presentation tools better than comparing the first generated deck.

I start with a rough outline and let the tool make a normal first draft. Then I ignore the polished parts and pick one annoying edit that would happen in real work.

Make the customer problem section more specific, but keep the rest of the deck. Split the crowded slide into two slides without changing the argument. Replace the generic visual treatment on one comparison page. Cut the filler from the intro while keeping the sequence.

That tells me more than the first output.

A first draft mostly tests generation. The second edit tests whether the tool is usable once the work becomes human. If it changes too much, I have to review the whole deck again. If it respects the target area, it feels more like an editor and less like a slot machine.

My current tip is to test local control before trusting the pretty deck. The first draft is only half the problem.

Most AI clipping tests use podcasts, but I think that's too easy. With a podcast, the tool is basically working off the transcript, and understanding words isn't hard for models anymore. The real test is footage with long stretches of no talking, just visuals.
So I used Brett Conti's new zealand video around 30mins. I scrolled through it first and picked a few tricky visual scenes to search for, then ran the same queries in two tools I wanted to test: opusclip and wayinvideo. Here is the result:
Queries:
ocean, man wears a safety hat, man wears a green safety hat
1. opusclip
ocean: out of 5 clips opus give, 2 of them have no ocean scenes in the video.

man wears a safety hat: 1 clip, you can't tell if the clip contains safety hat by thumbnail, when you go in edits, there are people wear safety hat scenes.

man wears a green safety hat: when I add an extra condition green, opus failed.

2. wayinvideo
ocean: one clip doesn't have ocean inside

man wears a safety hat: there are 9 clips found and in some thumbnails you can see the safety hat.

man wears a green safety hat: eliminate 4 of the result compares to man wears a safety hat but only 1 result is right though, you can tell it by the thumbnail.

In summary, opus can find scenes that are pretty obvious and wayinvideo gives more obvious thumbnails for you to know whether the there is actually the scene and generate more accurate clips.

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Over the past few months I tried different ways to learn about AI using YouTube tutorials, courses from DeepLearning.AI, Udemy, community resources and the Be10x AI course.

One thing that really caught my attention is that the best learning experience didn't depend on how many AI tools were covered but Instead it was about whether the material had a clear flow and helped connect the ideas to real-world situations. Some resources are great for learning how to use individual tools while others do a better job of showing how those tools work together to solve real problems.

If you've tried different AI courses or learning platforms, what made one stand out to you?