Spent part of this week comparing notes with a handful of GPs on how founders are using AI in a raise. Everyone landed in the same place: most founders have it backwards. They point it at the parts of the process where it costs them, and ignore the parts where it would actually change the outcome.

Here’s the split.

The deck. Nobody enjoys building one. It’s slow, you rewrite it eleven times, and you’d rather the pitch just be a conversation. So handing it to a model feels like finding time. It isn’t. An AI-generated deck is obvious to anyone reading 10+ of them a day, and here’s what the reader takes away: this founder doesn’t care about craft and will not care about their product, their voice is missing, and this partnership is a formality to them. None of that has to be true. It’s still the impression you left, and you don’t get to correct it in the room.

Unsupported facts. TAM figures, competitor lists, customer counts, market stats, even claims about what’s already in the investor’s portfolio. These land in decks now without anyone checking a source. One confidently wrong number does more damage than a missing slide, because it says you don’t know your own market. You’ll spend the rest of the meeting rebuilding credibility instead of selling.

Relationship moments. Meeting follow-ups, referral asks, monthly investor updates. A model can draft all of it. What it can’t do is remember that the partner spent twelve minutes on your churn assumption and wants that number in the next update. Draft with AI, supply the judgment yourself. Skip that step and you read transactional to the exact people you’re asking to be patient for ten years.

Your competitor set including the graveyard. You should already know who’s live. Use AI to build the complete list, then the harder one: everyone who tried this and died. Get a first pass on why each failed and where you differ. Then go past what the model can do, find those founders and call them. Surprisingly, most will take it. A competitor slide that names three dead companies and explains what changed since then provides instant credibility. It’s the difference between “we’re better” and “here’s why this works now.”

Investor targeting. Most raises still run off a list of 80 firms someone scraped from a shared doc. Use AI to filter for who actually invests at your stage and sector, who’s writing cheques this quarter, and who has a portfolio conflict you’d be walking into. Then go a layer deeper: how does that firm decide? One champion or full partnership consensus? Monday meeting or rolling? The data won’t be perfect. It’s still far better than what most founders walk in with.

Practice. Give the model your deck and read what it questions, what it can’t follow, and what it doesn’t find credible. Those are your holes, and they’re the same ones a partner will find on Tuesday. Then record your real pitches and query the transcripts for patterns: where do people interrupt, which line lands every time, which slide gets skipped. Ten meetings in, you’ll know more about your pitch than any advisor could tell you.

AI is good at the work you should have done before the meeting. It’s bad at the work that happens between two people.

Research, filtering, stress-testing, pattern-finding: hand it over. Voice, craft, judgment, and the memory of what someone actually cared about: that's what you're being evaluated on. Founders automate the second and do the first by hand.

If you’re raising in the next six months, run it in this order: competitor graveyard, then target list, then practice loop. Build the deck yourself. Last.

Fundraising is the conversation at every Together Toronto. The next one is Monday, August 10.

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