This piece was originally written by Olivia McClure on Built In.
Airwallex is rewriting the rules of global finance, and AI is the pen. The fintech platform serves businesses worldwide with integrated solutions spanning payments, accounts and embedded finance. Here, Ishan Agrawal, Head of Engineering, Data and AI, shares how Airwallex embeds innovation into its culture, builds AI-powered products that simplify complexity, and balances rapid experimentation with the stability that financial infrastructure demands.
How does innovation show up in your company culture?
At Airwallex, innovation isn’t a department; it’s our operating system. We are redefining global banking with AI by discarding the traditional playbook. Every employee here is an AI practitioner. It’s not just engineers using coding agents. Our finance team is “vibe coding” their own applications, our talent team uses AI to streamline hiring, and our operations team is driving massive efficiency gains through automation.
In product and engineering, we’ve embraced a high-velocity “rebuild” mindset. In the AI era, building for the foreseeable future is a myth. We build knowing that as underlying models improve every few months, we may need to rebuild aspects of our product to unlock even more value for customers. This shift even extends to how we grow our team: Our engineering interviews now test for proficiency in working with AI rather than banning it. We aren’t looking for people who can memorize algorithms; we’re looking for “AI pilots” who can leverage these tools to build faster and smarter. If that sounds like you, explore open roles on the team.
“We aren’t looking for people who can memorize algorithms; we’re looking for ‘AI pilots’ who can leverage these tools to build faster and smarter.”
What’s one recent innovation that improved user or employee experience?
One of our most impactful innovations is our AI assistant embedded in the Airwallex product, designed to make opportunities obvious and finance nearly invisible. Airwallex offers a broad, powerful product surface that can be complex for new users moving from onboarding to real value.
The assistant acts as an intelligent guide. It understands your context, draws on information you share and relevant public signals, and creates a tailored journey that gets you to outcomes like funding accounts, issuing cards or scheduling international payments much faster. It doesn’t just answer questions. Behind the scenes, specialized agents execute complex tasks on your behalf, from configuring accounts to setting up cards and payments.
It sits on a context layer with access to structured and unstructured data about Airwallex, our products, the user’s business and the user’s Airwallex data. This real-time layer powers experiences across our product and the AI assistant. This is only possible because of advances in agentic architectures and foundation models, an early example of how those breakthroughs create a smoother customer experience and a foundation for the finance agents we’re building now.
How do you balance experimentation with stability?
We balance experimentation and stability by being very deliberate about how and where we take risks. Experiments start with clear hypotheses, success metrics and a bias toward running many small, fast tests rather than a few large ones. We learn quickly, kill what doesn’t work, and harden the ideas that do into our core product.
Because we’re a financial platform, stability is non‑negotiable, especially in our core money‑movement and risk systems. Most new ideas — especially AI‑driven ones — start behind feature flags and in constrained surfaces like onboarding or decision support, with offline and online evaluations, strong guardrails and close monitoring. Before promoting an AI experiment into production, we run automated evaluations, red‑team style testing, and monitor for regressions on key metrics so we can catch issues early and roll back safely. This lets teams move fast at the edges of the product while intentionally keeping the underlying financial rails stable.
