Diya Jolly, Chief Product and Technology Officer, Xero – Interview Series

Diya Jolly, Chief Product and Technology Officer at Xero, is a seasoned product and engineering executive with a proven record of driving innovation and growth at global technology companies. With experience spanning Google (GOOGL ), Okta, and now Xero, she has led cross-functional teams across product management, design, data science, and engineering to deliver transformative digital products. At Xero, Jolly oversees a world-class team of 2,500 professionals, guiding the company’s product and technology strategy to redefine accounting and fintech for small businesses through AI-driven innovation.

Xero is a global leader in cloud-based accounting and fintech software designed to empower small businesses, accountants, and bookkeepers. Founded in New Zealand, Xero serves millions of subscribers worldwide with tools that simplify financial management, automate bookkeeping tasks, and integrate with a vast ecosystem of third-party applications. The company continues to innovate through AI, data insights, and seamless user experiences, helping small businesses thrive by making financial management smarter, faster, and more connected.

You’ve had a fascinating journey— leading product at Google and Okta. How has that shaped the way you now build AI-native finance products at Xero?

My journey through Google and Okta fundamentally shaped how I approach product leadership—especially in building AI-native finance solutions at Xero. At Google, I led product for YouTube’s monetization, scaling revenue from $1.5B to $10B in five years and launched transformative products like Google Hub and Assistant. That experience taught me how to build for massive reach, user-centric design, and long-term scalability. At Okta, I drove product innovation and helped scale revenue from $400M to $1.8B, launching industry-defining solutions like FastPass and next-gen identity governance. There, I learned how to build enterprise-grade trust and navigate complex technical ecosystems.

Now at Xero, I bring those lessons to a new frontier—AI-native finance. I lead a globally distributed team of 2,500 across product, design, engineering, data science and security, with key talent from Amazon (AMZN ), Apple (AAPL ), Meta, Google, Uber, Intuit (INTU ) and Square. Together, we’re embedding intelligence directly into workflows to automate repetitive tasks like data entry and reconciliations, while unlocking deeper financial insights for small businesses. In the last calendar year, we have delivered twice as many product launches as we did a year ago, and we’ve consistently delivered over 20% revenue growth and a ‘Rule of 40’ outcome since FY24.

What drives me is the opportunity to reimagine financial workflows through AI—not just to make them faster, but smarter and more intuitive. My past roles taught me how to scale innovation responsibly, and at Xero, I’m applying that mindset to build products that are not only powerful but deeply empathetic to the needs of small businesses, which are the backbone of every economy.

How is Xero’s engineering organization evolving to support the demands of AI and real-time payments? Are you changing how teams are structured or how they collaborate? 

We’re undergoing a strategic transformation at Xero to meet the demands of AI and real-time payments—not just in what we build, but in how we build it. Our engineering organization is shifting from traditional silos to a more integrated, cross-functional model that brings together product, design, data science and security under one umbrella. This structure fosters tighter collaboration and accelerates decision-making, allowing us to embed intelligence directly into workflows and deliver customer value faster.

We’ve also invested heavily in distributed teams, which gives us access to global talent and diverse perspectives. This diversity is critical when building AI-native platforms that must be both scalable and sensitive to local financial regulations. Our teams now operate with shared OKRs aligned to our Responsible AI Principles, ensuring that every initiative—whether it’s generating an invoice or integrating Melio’s payment infrastructure—is grounded in transparency, reliability and customer trust.

To support real-time payments, we’re modernizing our infrastructure to handle high-volume, low-latency transactions, while maintaining enterprise-grade security. This includes rearchitecting parts of our platform to be more modular and resilient, enabling faster experimentation without compromising stability. The result is a more agile, empowered engineering culture—one that’s ready to meet the evolving needs of small businesses and their advisors in a digital-first world.

Following the acquisition of Melio, what are some of the biggest technical or cultural challenges you’ve encountered while integrating its payment infrastructure into Xero’s existing platform and engineering systems? 

Following our recent announcement of our agreement to acquire Melio, we’re currently planning and designing what our fully integrated payment solution will look like in Xero and we’ll share an update in due course. This includes aligning teams across geographies and disciplines to foster shared goals and values. Our experience integrating Syft and launching AI-powered tools like Just Ask Xero (JAX) (currently in beta) has helped us build a playbook for successful integration—one that prioritizes customer impact, transparency, and speed.

What are the most promising use cases where machine learning is already having an impact across Xero’s product suite?

Machine learning is already transforming how our customers interact with Xero. Tools like Hubdoc use next-gen OCR to automate data capture from documents, while JAX—our GenAI-powered smart companion—helps users manage tasks via SMS, email or WhatsApp. These innovations reduce friction in financial workflows, automate repetitive tasks and reveal deeper insights. We’re embedding intelligence into both customer-facing features and internal operations to drive smarter decisions and better execution.

How do you ensure your AI initiatives remain grounded in solving real customer pain points, especially in a space as sensitive and regulated as finance?

We start with the customer by researching their pain points, mapping their workflows, and assessing where and if AI can meaningfully help. Every AI initiative at Xero is designed to address a specific customer challenge—whether it’s simplifying generating invoices or editing a quote right from the apps and devices customers commonly use—JAX’s goal is to help small businesses get paid faster. We embed intelligence into workflows rather than creating standalone tools, ensuring that AI enhances the user experience without adding complexity.

What’s your approach to building AI responsibly—particularly in terms of data quality, model bias, and transparency?

Our newly launched Responsible AI Principles reinforce this approach by guiding how we use AI to create customer and shareholder value while managing risk responsibly. These principles are rooted in our values and purpose and our longstanding commitments to responsible data use. They ensure our AI systems remain transparent, reliable  and safe. Every AI system at Xero is overseen by a designated human, and we’re committed to helping users understand when and how AI is being used. We’re also rolling out internal training programs to ensure our teams innovate responsibly and with confidence.

Rather than relying solely on hiring, how is Xero investing in upskilling your current workforce to deliver on AI-native goals?

Upskilling is a core part of our strategy. We’re investing in internal training programs, cross-functional collaboration and hands-on experience with AI tools. By embedding AI into our workflows, we give teams the opportunity to learn by doing—whether it’s through building new features, analyzing data or refining models. This approach ensures that our workforce evolves alongside our technology.

This vision is supported by company-wide training programs, tech talks and Xero’s Masterclass series, which keep employees at the forefront of AI advancements. Xero’s twice-yearly global hackathons—one of which was entirely AI-focused—offer employees a platform to experiment, collaborate and build real-world solutions. These initiatives, along with leadership engagement and a patent program that encourages proprietary innovation, ensure that every team member is equipped to contribute meaningfully to Xero’s AI-native transformation.

Why do you believe distributed teams are essential to building scalable, AI-driven platforms today?

Distributed teams aren’t just a logistical choice—they’re a strategic advantage in building scalable, AI-driven platforms. At Xero, our global footprint allows us to operate across time zones, iterate continuously and bring richer perspectives into our product thinking. This diversity is especially critical in AI, where context and nuance matter deeply—from training data to user experience. By decentralizing decision-making and empowering regional teams, we’re able to build solutions that are both globally scalable and locally relevant, while maintaining the agility needed to respond to fast-moving technological shifts.

How do you balance the tension between platform stability and experimentation when integrating machine learning into a global SaaS product?

Balancing stability and experimentation is something we approach very deliberately. At Xero, we embed AI workflows incrementally—testing rigorously, gathering customer feedback,  and scaling only when we see clear value. Strong governance frameworks keep that innovation safe and responsible, while our culture of experimentation ensures teams can explore bold ideas without compromising platform reliability. The goal is to move fast in ways that matter most to our customers—delivering new intelligence and automation without ever disrupting the trust they place in us.

Are there emerging AI technologies or techniques that you’re especially excited about, either in-house or industry-wide?

I’m especially excited about the potential of large language models to transform accounting. I’m convinced GenAI will automate time-consuming, repetitive tasks like data entry, bank reconciliations and invoice management, freeing up time for deeper financial insights and decision-making. According to Xero data, half of US small businesses encounter fiscal challenges due to a lack of financial literacy. Working with accountants and bookkeepers as advisors, empowers small business owners to make informed financial choices, helping them overcome obstacles and seize new opportunities in today’s dynamic business environment. By combining the power of GenAI with the trusted guidance of accounting professionals, we can unlock a smarter, more resilient financial future for small businesses.

How do you envision AI transforming the role of engineers and product managers at Xero over the next 2–3 years?

Over the next few years, AI will change how product managers and engineers at Xero work—and, by extension, how quickly we can deliver value to our customers.

For product managers, AI will strip away friction in early product design. They’ll spend less time writing lengthy product requirement documents (PRDs) and more time prototyping, testing, and refining products with customers. For engineers, AI copilots are already accelerating coding, generating test cases, and allowing teams to spend more time on architecture, scalability, and problem-solving.

Ultimately, this all means we can build products better, faster and that reflect customer needs in real-time. AI will amplify human creativity and help our teams stay even closer to customers to continuously improve the experience they have on our platform.

Thank you for the great interview, readers who wish to learn more should visit Xero.