Anand Naidu is our resident Development expert. He is proficient in both frontend and backend and provides deep insights into various coding languages. With a background in applied AI, he’s at the forefront of helping enterprises scale their AI transformations. His work focuses on embedding AI engineering teams directly into product organizations, a model designed to bridge the gap between AI experimentation and full-scale production, delivering intelligent software at the speed modern businesses demand.
You’ve chosen to embed Augusta Labs’ teams directly into your product organization. Can you walk us through the practical differences between this integrated model and a traditional vendor relationship? Please share a specific example of how this accelerates your development cycle.
Certainly. In a traditional vendor relationship, you often get a black box. You hand over requirements, they go away and build something, and then you get a deliverable that might or might not truly align with your internal culture and long-term roadmap. It can feel very transactional. With this embedded model, Augusta Labs isn’t an external party; they are an extension of our own engineering organization. Their teams are sitting, metaphorically speaking, right alongside our teams working on key workstreams like Sage Payroll. This means every line of code they write, every AI feature they develop, is built with an intimate understanding of our Sage Platform. This eliminates the lengthy back-and-forth and realignment cycles, allowing us to move from a promising prototype to a production-ready feature far quicker than traditional models would ever allow.
Achieving “startup speed” while maintaining the reliability expected by millions of customers presents a unique challenge. Could you describe how this partnership helps you navigate that trade-off, perhaps with a story from the development of a feature for Sage Payroll or Sage Active?
That’s the fundamental challenge, isn’t it? Our customers trust us with their most critical financial and HR data, so reliability is non-negotiable. At the same time, the market demands rapid innovation. This partnership is our answer to that balancing act. By working with teams from Portugal’s vibrant startup ecosystem, we infuse our global AI delivery model with their high-velocity engineering and agility. Think about developing a new predictive cash flow feature for Sage Active. The embedded team can iterate on models with incredible speed, but because they are integrated into our organization, every step is checked against our rigorous governance and security protocols. It’s not about moving fast and breaking things; it’s about moving fast with the guardrails of an established, trusted enterprise. This structure ensures that the “startup speed” we gain never comes at the expense of the trust our millions of customers place in us.
The goal of creating “agentic workflows” to automate end-to-end tasks is quite ambitious. Could you provide a step-by-step example of a complex financial task you aim to automate this way, and explain what metrics you’ll use to measure its success for your customers?
It is ambitious, but it’s the future. Imagine a small business owner’s month-end closing process. Today, that involves manually reconciling bank statements, categorizing expenses, generating P&L reports, and then initiating payroll. An agentic workflow would automate this entire sequence. It would start by ingesting real-time transaction data, intelligently categorize each entry based on historical patterns, flag anomalies for review, and then automatically generate the necessary financial reports. Once approved by the user with a single click, it could then trigger the payroll process in Sage Payroll. Success for us is measured by time returned to the customer. We’ll track the reduction in hours spent on manual administrative tasks and the decrease in human error rates. The ultimate goal is to make these complex, multi-step processes feel completely effortless, giving our customers back their most valuable resource: time.
Transforming into an “AI-first” company requires a significant cultural and technical shift. Beyond building new features, how does embedding an external AI team influence your internal engineering culture and help upskill your existing talent? Could you provide some specific details?
This is a critical point. Becoming an “AI-first” company is as much about people as it is about technology. Bringing in an embedded team like Augusta Labs acts as a powerful catalyst for our internal culture. It’s not just about capacity; it’s about capability. Our engineers work side-by-side with applied AI experts, which creates a natural environment for knowledge transfer. They see firsthand how to move from AI experimentation to production at scale, a skill McKinsey’s latest report identifies as crucial for capturing value. This collaboration demystifies advanced AI concepts and fosters a shared language and methodology. The end result is that we’re not just getting new features; we are fundamentally strengthening our internal AI and data engineering muscles for the long term, which is essential for our transformation.
AI’s value is often tied to the depth of its data. How are you and Augusta Labs creating high-performance data pipelines for real-time insights, while ensuring the strict governance and privacy your customers in finance and HR expect?
The depth of our data is our greatest asset, but it comes with immense responsibility. Our partnership is focused on building high-performance data pipelines that can process and analyze information in real-time, but with governance baked in from the very first line of code. This integrated execution model is key. Because the Augusta Labs teams are embedded within our structure, they operate under our strict data privacy and security frameworks. Every pipeline is designed to be compliant with global standards, ensuring customer data is always handled with the utmost care. This allows us to unlock powerful real-time insights—like instant fraud detection or predictive resource planning—without ever compromising on the trust and security our customers rightly expect from a leader in finance and HR technology.
What is your forecast for the role of AI in accounting and HR software over the next five years?
Over the next five years, I believe we will see a fundamental shift from software that records what happened to software that anticipates what will happen next. AI will become the central nervous system of business operations. Instead of just being a feature, AI will be the fabric of the software itself, creating intelligent, adaptive experiences. We’ll move beyond simple automation to truly agentic workflows that manage complex, end-to-end processes with minimal human intervention. For our customers, this means their accounting and HR software will transform into a proactive business partner—offering strategic advice, identifying growth opportunities, and mitigating risks before they even arise. The future of this software isn’t just about making tasks easier; it’s about making businesses smarter.
