AI-powered customer service solutions are all over the internet, and Software as a Service (SaaS) technology has been completely changed by its potential.
Kishandth Sivapalasundaram is a Software Engineering Leader at Ujet, a cloud-based contact center platform designed to improve customer experiences through AI-powered tools, and it’s his job to find the best ways to integrate AI into customer service platforms. This takes creativity and technical vision, which Kishandth developed thanks to his unique background in actuarial science, data analytics, and software engineering. He’s worked across continents and industries and brings a unique perspective to solving complex technical challenges. His work at Ujet, which includes a partnership with Google Cloud Platform’s Contact Center AI, has significantly impacted how the company uses SaaS systems in the customer service sector.
Kishandth has taken the time to answer a few questions and provide insight into his career and how he sees the SaaS sector evolving in the future.
Q: To start with, your diverse background has been quite an asset in your current role, hasn’t it? Especially your background in science and finance. Do you feel your history has been a significant benefit to your work?
KS: You could say I took the scenic route to software engineering. I started as an actuary in Sydney, where I discovered my love for coding by automating insurance reports with VBA. As much as I enjoyed crunching numbers, I found myself more excited about writing code to crunch those numbers faster. Later, I spent time in data science at Quantium and then at One Peak Partners—a private equity fund focusing on B2B SaaS companies—which gave me some useful perspectives on how technology impacts business. I still use those perspectives today. Better understanding from more angles lets me make better use of technological tools.
Q: Did those perspectives help with the Agent Assist project you developed at Ujet? That has made a significant difference in customer service operations.
KS: That’s right. Agent Assist provides customer service agents with better tools for all kinds of work. We integrated Google’s Dialog Flow AI to help agents in real time. The AI understands customer sentiment during conversations, proactively surfaces relevant articles, and makes suggestions to help resolve issues faster. You can imagine it like having an extremely experienced, fast-thinking partner right there with the customer service agent.
The project was a key requirement for Ujet’s partnership with Google, and I’m proud of the difference it made in how we approach customer service. The AI doesn’t just wait for you to prompt it with questions anymore. It’s right there with you, predicting your needs and providing solutions before you’ve even asked for them.
Q: How much are AI tools like that doing to actually improve efficiency and scalability for SaaS platforms?
KS: The impact has been tremendous. For instance, one of our projects involved embedding Google’s translation AI model into customer conversations, and that helped prepare our SaaS platforms for international clients. We were able to use AI to remove the language barrier in customer service. In contexts like this, AI can help companies handle significantly more customer interactions without proportionally increasing their team size. We’ve seen companies using our platform to manage growing customer bases more efficiently while maintaining—or even while improving—the quality of their service.
Q: You mentioned AI that could understand customer sentiment on the fly. That sounds like a significant development! How much does that play into improving customer service interactions?
KS: Sentiment was a big blind spot for AI, but it can also be a big challenge for human customer service agents. Now that the AI has been improved to focus on sentiment analysis, it’s surprisingly good at it. The AI can detect subtle nuances in customer interactions and help agents adjust their approach in real time. It’s fascinating to see because it takes AI beyond solving technical problems and has them actually understanding and responding to human emotions. It’s been particularly powerful in helping companies maintain strong customer relationships, and it helps them improve retention rates.
Customers feel understood. It doesn’t matter that it’s an AI that’s understanding them; it’s still the company putting in the effort to understand, to care, and to provide a much more personalized and empathetic response.
Q: You’re one of the leaders deciding how AI will evolve in the SaaS industry over the next few years, so what kinds of changes and progress do you expect to see?
KS: I see three major transformations coming to AI in SaaS. First, we’ll move beyond basic automation and see true workflow intelligence – AI that understands the full context of business processes and can proactively suggest optimizations. For example, rather than just automating call responses, AI will identify bottlenecks in your call flows causing calls to go overtime and recommend specific process changes.
Second, we’ll see the emergence of ‘collaborative AI’ that acts as a true partner to knowledge workers. Instead of just executing tasks, these AI systems will engage in real-time problem-solving alongside humans – imagine an AI that can participate in strategy meetings, offering relevant data insights and challenging assumptions while letting humans drive key decisions.
Finally, I expect significant advances in what I call ‘adaptive personalization.’ Current SaaS personalization is largely rule-based, but future AI will dynamically evolve its interactions based on both individual and organizational learning patterns. A sales platform might automatically adjust its interface and recommendations based on how your specific team operates and what drives results in your industry.
The key is that we’re moving from AI as a tool to AI as an intelligent partner that amplifies human capabilities rather than replacing them. But we have to be thoughtful about implementation – success will come from finding the right balance between AI automation and human judgment in each specific business context.
Q: Finally, can you leave us with some advice for software engineers who are looking to make an impact in the SaaS industry?
KS: I always think back to my early days at that big data company in Australia, where I automated a significant manual report. I spent two weeks working on that code, and the code ended up being sold for five figures. The experience taught me something so important: to focus on solving real problems that create tangible value. Whether you’re working with AI, building user interfaces, or designing system architecture, always ask yourself, “How does this make things better for the end user?” Build something that works, has an actual application, and makes a real difference in a real way. Technology changes quickly, but good problem-solving is one principle that will always remain consistent.
