Zola automates 600,000+ annual invoices with Tipalti, running AP with 2 people instead of 10
Zola's finance team processed more than 600,000 invoices annually through manual approval and payment workflows that were unsustainable at scale, and the company's previous Bill.com system still consumed significant time each week as payment volumes doubled.
Bill.com was Zola's original tool to streamline operations but could not keep pace as payment volumes doubled, leaving invoice approvals and payment processing still consuming significant time each week.
source quote
source quote
source quote
source quote
source quote
Zola now processes over 600,000 invoices annually through automation with just two AP staff instead of ten, and still closes the books by the second day of the month.
Show all 5 reported metrics
Compare to your context
Tell us your scale, team, and constraints. We'll show what changes at your size, what fails at your scale, and whether this case is a fit, needs adaptation, or won't scale to you. Free demo, no signup.
Frequently asked questions
What did this team achieve with this AI workflow?
Zola now processes over 600,000 invoices annually through automation with just two AP staff instead of ten, and still closes the books by the second day of the month.
What tools did this team use?
Tipalti, OCR, EDI, Intacct.
What results were reported?
Invoices processed annually: more than 600,000; AP headcount without Tipalti: 10 people; AP headcount with Tipalti: two people; Month-end close date: close on the second day of the month (source-reported, not independently verified).
What failed first in this deployment?
Bill.com was Zola's original tool to streamline operations but could not keep pace as payment volumes doubled, leaving invoice approvals and payment processing still consuming significant time each week.
How is this accounts payable AI workflow structured?
Vendor self-onboarding → OCR invoice extraction → EDI batch processing → ERP reconciliation → Accelerated month-end close.
Related accounts payable cases
Now compare it to your context
This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.