You've raised your seed round. You have a small team, real customers, and investors watching your burn rate. AI at this stage is about multiplying what each person can do - not replacing people, but letting a 10-person team operate more like a 25-person team.
Seed-stage teams should spend $300-800/month on AI. Claude Team for the whole company, a vertical tool for your primary function (sales or marketing), and Fathom or equivalent for meetings. Everything else is probably premature. Focus AI on your highest-leverage bottleneck first - usually GTM or customer success, not product.
Seed is typically 3-15 people, $1-5M raised, initial product-market fit signal, and pressure to show investors you can grow efficiently. The constraint isn't ideas - it's execution bandwidth. Your team is doing jobs that at a Series A company would be split across three specialists.
AI solves this specifically. The right deployment gives every person on your team the ability to produce at a higher level in their non-core responsibilities. Your head of sales can write good marketing copy. Your ops lead can handle investor reporting. Your CEO can stay on top of competitive intelligence without a dedicated analyst.
At seed, you can justify more than at pre-seed - but discipline still matters. The right stack:
Total: $300-800/month for a 10-person team. See also: fractional CMO services and what fractional executives do.
Seed-stage startups often get sold into category tools that require more process maturity than they have. Hold off on:
Rather than deploying AI everywhere at once, pick the highest-leverage bottleneck and build one workflow well before expanding. For most seed-stage companies, that's one of:
Get one workflow producing measurable ROI before layering in the next. At seed, you don't have the management bandwidth to run AI adoption company-wide simultaneously.