Asana and AI Studio have become the backbone of how we run FTMO. It is an orchestration layer that lets our team move with the speed of a company ten times our size. For a fintech serving 3.5 million paying customers globally, that kind of responsiveness is not optional.

Marek Vašíček

Chief Technology Officer

Use case 1: AI-powered incident and defect triage

Before building this workflow, reporting a bug was a point of friction for both the reporter and the engineer. FTMO solved this by stripping the intake form down to three simple fields and letting AI Studio handle the complex cognitive work of classification.

• Simplified intake: Users fill out a three-field form (title, description, impact), which has increased submission rates and improved issue visibility across 10+ teams.

• Intelligent classification: AI Studio reads the description to determine which system is affected (e.g., trading platform, payment system) and sets the priority level automatically.

• Automated dispatch: AI-powered rules multi-home the classified ticket into the central triage board and the specific engineering team's sprint board in seconds.

• Context-rich alerts: Engineers receive tickets with AI-generated summaries and tags for related past incidents, providing immediate context for the fix.

The biggest unlock was simplifying the intake form and letting the AI handle classification. We went from a process where people avoided reporting bugs because the form was painful, to one where the right team is notified automatically in seconds instead of hours.

Jan Vejmola

AI Lead

Use case 2: Accounts payable and payment automation

Invoice processing was previously a manual, multi-person chain prone to human error and delays. FTMO built an end-to-end pipeline where AI Studio scan, extract, and route invoices through a five-stage lifecycle without manual data entry.

• Two-stage AI pipeline: The moment a PDF is uploaded, AI performs OCR to extract key data (vendor, due date, reference) and auto-fills custom fields.

• Vendor registry matching: A second AI studio rule cross-references the extracted vendor name against an internal registry to handle minor variations and verify the vendor's active status.

• Dynamic role assignment: Based on the vendor data, the system auto-assigns roles for booking, payment preparation, and approval, ensuring no invoice stalls in the chain.

• Automated routing engine: A custom script watches the status field and reassigns the task to the next person in the five-stage lifecycle automatically upon approval.

The breakthrough was chaining two AI rules together. The first one reads the invoice, and the second one immediately matches it against our vendor list and assigns all the right people. The PDF upload is literally the only human action needed.

Eugen Proschek

Project Manager

Use case 3: Operational governance and exception handling

FTMO uses AI Studio not just for speed, but for rigorous financial and operational control. By designing AI to flag anomalies, the organization ensures that human judgment is applied exactly where it is needed most.

• Vendor check safety net: If AI Studio detects a terminated vendor or a mismatch in the registry, it pauses the workflow and routes the invoice back to a human with a clear explanatory comment.

• Audit-ready approvals: Every approval or rejection is automatically documented with a timestamped audit comment, meeting the compliance requirements of a global fintech.

• Smart status reporting: Smart summaries generate weekly resolution reports for management dashboards, helping leadership track incident patterns and platform health.

• Cross-functional transparency: 350+ people across 8 departments work from the same system, reducing status update meetings by 30% and eliminating ad-hoc interruptions.

AI Studio handles the routine work, but when something does not match, it stops and asks a human. That is exactly how it should work in finance. You want speed on the 90% of invoices that are straightforward, and a clear flag on the 10% that need attention.

Marek Vašíček

Chief Technology Officer

Conclusion

Before Asana, FTMO’s growth was threatened by manual administrative tax and triage delays. By building an AI-powered operating system, the company has decoupled its headcount growth from its operational capacity. This unified architecture ensures that as FTMO scales to serve millions of additional customers, its incident response remains lightning-fast and its financial operations remain audit-ready. For FTMO, AI is no longer an experiment, it is the fabric of their competitive advantage.