I’m Ankita, AI Product Lead at Payabli. I came into fintech from outside the industry, which means I ask a lot of “why do we do it that way?” questions. Here, I write about building AI systems that actually work—for our team, our customers, and the emerging world of agentic fintech.
When I joined Payabli six months ago, the company had already embraced AI with an impressive toolkit—ChatGPT, Claude, Cursor, Gemini, and more. Teams were actively using AI for writing, brainstorming, and research, signaling a strong foundation and a real appetite for innovation.
But I also saw an opportunity to go further. While people were using AI tools, they weren’t yet experiencing the transformative upside. Many workflows still relied on manual processes begging for automation, and teams wanted clearer direction on how AI could fundamentally reshape the way fintech work gets done.
Fast forward six months: we’ve built a suite of AI agents operating across the organization, contributing to more than 24 hours of manual work saved every week. We’ve consolidated our toolset to streamline learning, training, and sharing of best practices. And we’ve begun laying the groundwork for what we believe is the next frontier in the industry—agentic fintech, where autonomous systems handle operational complexity so humans can focus on strategy, relationships, and innovation.
This is how you evolve into a truly AI-native organization.
Start With a Clear Picture: Assessing Real AI Adoption
You can only fix what you can measure, so I started by getting a clear picture of Payabli’s AI usage.
Through surveys, conversations with team leads, and benchmarking with industry counterparts, I discovered that 75% of the company was using AI automations daily – a strong starting point. However, employees were working across various AI tools, and that fragmentation was holding us back. Training was inconsistent. And most importantly, teams were focused on surface-level use cases instead of the deep automation and integration work that would deliver real impact.
Automate the Pain Points First
My approach to demonstrate the latent value of AI was simple: identify the most time-consuming manual processes, automate them, and build a portfolio of proof points. Often people aren’t opposed to AI adoption – they just don’t even realize it can solve their specific problem.
I started with the low-hanging fruit – the repetitive, time-intensive tasks that were taking up hours of employee time and built tools to automate them:
- Chargeback AI Agent: Handles routine email responses, collaborates with human analysts on complex chargeback cases, and tracks action items – reclaiming hours previously spent on manual work.
- Engineering Ticket Monitoring: Automates the monitoring of support tickets to ensure high-quality descriptions that speed up engineering output.
- Sales Lead Qualification Tool: Automatically evaluates new customer leads against our criteria and notifies the sales team directly in their email inbox
These AI automations became our proof points. Everyone could see the tangible impact – colleagues reclaiming hours each week, faster response times, higher quality outputs – all within their existing tools. More importantly, it shifted the conversation from “Can AI help?” to “What should we automate next?”
Build AI Literacy, Not Just AI Tools
As important as it was to build automations, it was equally critical to create shared understanding around how AI should be used across the company. You don’t become an AI-native organization by deploying tools alone – you get there by ensuring every employee knows how and when to use AI to accelerate their work.
To support that shift, I created comprehensive internal AI documentation that outlines how we use AI at Payabli, including:
- Guidance on when to use different AI assistants — for research, analysis, content creation, or structured workflows.
- Instructions on leveraging our integrated workspace tools, including web search, database search, and project management.
- How to create specialized AI agents with custom instructions and knowledge bases
- Examples and frameworks employees can follow to identify automation opportunities in their own workflows.
The goal was not just to share information, but to instill an automation-first mindset across the organization. Instead of stopping at low-hanging fruit, employees now have tools and frameworks that help them consider where AI can meaningfully speed up processes or improve quality.
AI literacy isn’t a one-time initiative – it’s a cultural shift. By documenting, training, and creating space for experimentation, we gave every employee the confidence and skills to ask a powerful question: “How can AI make this faster?”
That’s when the real transformation began.
Turning Internal AI Wins Into Customer-Facing Innovation
AI automation delivers incredible value for internal teams, but the real opportunity is when you can extend that value to customers and enhance your product. Coming into fintech with fresh eyes helped me identify where we could make the biggest impact using AI virtual assistants.
I scoped several AI-powered features currently in development for Payabli’s 2026 production release, including:
- Analytics AI Agent – “Amigo,” Payabli’s embeddable chatbot, helps SaaS platforms quickly ask questions about transactions, identify trends, and find ways to improve their business.
- Vendor Enablement AI Agent – Helps merchants pay vendors faster by using an AI voice agent to encourage vendor enablement and determine payment preferences.
- Risk Scoring AI Agent – Machine learning models to score incoming transactions with an AI agent on top that conducts initial reviews and surfaces high-priority items for analyst investigation.
The key is identifying the highest-leverage areas for AI automations – not just adding it where it looks impressive. To effectively lead Payabli towards becoming an AI-native organization, I prioritize opportunities based on potential time savings, competitive differentiation, customer need, and strategic alignment.
Envisioning the Future of Agentic Commerce
Building for today isn’t enough – a big part of my role is anticipating where the industry is headed and positioning Payabli to lead that shift.
The agentic commerce wave is coming. AI agents will soon handle complex purchasing decisions autonomously – but there’s a problem: while e-commerce is racing to become AI agent-ready, the services industry isn’t getting as much attention. Service merchants lack the API infrastructure and tooling that would make them discoverable and transactable by AI agents.
That’s the gap we’re filling. We’re developing a strategy to ensure service-based businesses have what they need to participate in this shift – from merchant enablement toolkits to new payment token infrastructure designed for agent-driven transactions.
The AI-powered features we’re building now – risk scoring agents, vendor enablement voice agents, analytics capabilities – aren’t just standalone products. They’re building blocks for a future where payments infrastructure is intelligent by default and services are as accessible to AI agents as consumer products are today.
The possibilities ahead are endless, and we’re still early. Creating the mindset shift where every employee starts by asking “how can AI help?” has positioned Payabli to become a leader in AI-native payment infrastructure as the fintech industry continues to transform. There’s tremendous potential ahead for how we continue infusing AI into our product and organization – and we’re just scratching the surface.