I just got back from two weeks in Riyadh mentoring nine startups for a 500 Global accelerator. Three of them were AI-focused. And that ratio – 3 out of 9 – tells you everything you need to know about where investor money is flowing right now.
If you’re a female founder building something that isn’t AI, you need to understand what just changed. Because the playbook is different.
I ran two workshops: one on investor-grade financial storytelling, and another on building your data room and defending your round. Both were packed with founders eager to impress investors but what I found interesting is that the AI startups were had completely different investor challenges than the non-AI ones.
AI founders are being pushed to prove their growth isn’t just hype. But traditional founders are getting hit from both sides - they need stronger traction, bulletproof unit economics, AND a credible forward looking AI strategy. Because investors want to make sure you’re not about to get steamrolled by the companies they’re actually excited about.
The investment landscape has shifted, it’s being split into two completely different worlds and when capital concentrates around one hot trend (and right now that’s AI), everyone else has to work twice as hard to stand out.
Investors have less patience.
They want proof faster.
They’re comparing your “boring” fintech or e-commerce business to some AI startup promising unicorn returns.
So you need to tell a tighter financial story. Not a prettier one, a tighter one.
Here’s what I workshopped with those nine founders, and what you need to steal if you’re fundraising in this new reality:
1. Lead with proof, not potential
AI startups can often get away with saying “we’re going to disrupt an entire industry” with relatively little proof, because investors are chasing the wave.
If you’re building a non-AI business, traction is going to matter more than ever. That also means the road to bulletproof traction may be harder, you may need to bootstrap for longer before capital becomes available.
On top of this, customer retention is critical. Your ability to demonstrate real stickiness is what will carry you across the chasm when you’re eventually ready to adopt AI. It will also give you cash flow security when you might need it the most.
2. Your unit economics need to sing
One of the founders I worked with had decent traction but couldn’t explain her CAC payback period. Another couldn’t tell me if her customer lifetime value included churn assumptions or was just wishful thinking.
Investors expect founders to prove their economics work. Which means they’re definitely asking you to explain in one sentence:
• Your customer acquisition cost
• Your CAC payback period
• Your customer lifetime value
• Your gross margin by product/service
• Your churn rate and what drives it
Not approximations - all must be actual numbers.
And if an investor asks “What happens if churn increases by 10%?” you should have run that scenario already.
3. Defend your round before you’re in the room
This was the second workshop I ran, and it’s where most founders were unprepared.
Defending your round doesn’t mean justifying your valuation. It means anticipating every objection an investor will throw at you and having your answer ready before they ask.
The AI startups were planning for:
• “How do you know this isn’t just hype?”
• “What happens when OpenAI releases a similar feature?”
• “Can you prove your moat is defensible?”
Right now, if you’re not AI, investors assume you’re “safe but boring.” Which means they’re going to poke holes in everything:
• Why is your growth slowing?
• How do you know customers won’t churn?
• What if a bigger competitor enters your market?
• Why should I invest in you vs. the 10 other similar companies?
Your job is to have the data-backed answer before they finish asking.
4. Build your data room like it’s already due diligence
OMG! I can’t stress how important being prepared matters when you are raising investment.
One founder I worked with had decent revenue growth but when I asked to see her data room, it was an absolute mess. Financial models in three different formats with errors everywhere. Customer data incomplete. Cap table outdated.
She wasn’t trying to hide anything. She just hadn’t realized that in this market, investors are doing soft due diligence during pitch meetings.
If you can’t instantly pull up:
• Your financial model with scenario planning (best case, base case, worst case)
• Customer cohort analysis
• Unit economics by channel
• Your cap table
• Key contracts and partnerships
...then you’re not ready to pitch. Because investors will ask for it mid-conversation, and if you don’t have it ready you will lose momentum and they’ll move onto the next.
Do you need some help figuring our what goes into your data room?
