In a world where artificial intelligence increasingly defines the customer experience, Madhuri Somara stands at the intersection of technology, empathy, and strategy. A seasoned product leader and AI strategist with over a decade of experience, she has led the creation of intelligent agent platforms that automate complex workflows, reduce operational costs, and deliver more intuitive user experiences. Known for bridging technical depth with human-centered design, Madhuri brings a clear vision for how AI can enhance, not replace, human connection. In this conversation with AI Journal, she shares insights on designing transparent, trustworthy AI agents, balancing automation with empathy, and preparing organizations for a future where intelligent systems are not just support tools, but strategic partners in customer engagement.
You’ve built several AI agent platforms that have transformed customer interactions. Can you walk us through one of your most successful implementations and how it reshaped the customer experience?
One of my favorites is the case management Agent, which is the customer’s favorite Agent in automating the entire case lifecycle from the intent match, intent determination of the customer’s issue, case creation, case resolution to case follow-up & closure. While support representatives are focusing on empathy needed interactions, the case management agent does take care of all the admin activities and intelligently troubleshoots the case, it has semi-autonomous and fully autonomous modes where customers try semi before moving to fully. CMA achieved faster case resolution that reduced average case follow-up and closure time by 45–60%, depending on complexity, higher accuracy and reduced manual touchpoints. Engineers now intervene in only ~20% of cases, freeing capacity for higher-value work.
As AI agents become the first point of contact for customers, what do you see as the key design principles for ensuring those interactions feel authentic and human-centered?
The key principle is transparency: AI agents should never attempt to deceive users by pretending to be human. Customers trust an interaction more when they know they’re talking with an intelligent system that’s capable, respectful, and context-aware. After transparency, interactions should be efficient, empathetic, and relevant, meaning the agent listens carefully, provides the correct guidance, and respects the customer’s time if the AI Agent has no resolution in a few seconds, it should circle back with the customer immediately rather than trying to find resolutions. The goal isn’t to mimic humans, but to create an experience where the AI feels reliable, intelligent, and helpful, and to make it clear that the customer is interacting with a machine designed to make their life easier. I have seen AI Agents try phrases like “I understand how you feel.” No, you don’t. We should avoid mimicking humans.
From your experience leading AI-driven customer service solutions, what challenges do organizations face when introducing autonomous agents into existing support workflows?
The hesitation to use AI Agents and products especially the AI Agents which are customer facing that are having conversations or sending out the emails to customers, as we are still evolving, organizations still do have lot of questions such as, what if the AI Agent use profanity or rude tone, hence customers need hand holding while they launch AI Agents though they are available out of box today, but also maintaining transparency and providing options for customers to measure and monitor AI agent’s actions is the stepping stone.
How do you determine the right balance between automation and escalation and when should an AI agent hand off to a human, and how do you ensure that transition feels seamless to the customer?
Having humans in the loop is definitely the key factor to get customers try AI Agents and feel confident and secure to launch AI Agents at scale. The right balance between automation and escalation comes down to pairing the AI’s confidence in its response with the consequence of being wrong. High-confidence, low-impact actions should remain automated, while low-confidence or high-stakes scenarios should be routed to a human. The handoff must feel seamless, AI Agents should communicate with the humans, not just when necessary, but also should give continuous updates on what’s happening in the product and what actions are performed by AI Agent. Monitoring is the key, but again at the human agent’s convenience, and anything that AI Agent detects, such as negative sentiment or frustration, it should immediately hand over to the human where empathy is needed.
You’ve spoken about using data-driven insights to improve AI behavior over time. Can you share how feedback loops or analytics have helped enhance performance or personalization in systems you’ve built?
In every AI system, especially the case management agent and Intent agent that I’ve worked on, feedback loops have played a huge role in making the experience smarter over time. We track how users interact with the AI, what they accept, edit, or ignore and build that feedback right into the product. Analytics help us see patterns in where the AI adds value versus where human input is still critical. Over time, this kind of loop not only improves accuracy and reliability but also helps the AI adapt to how people actually use it, sounding more natural, context-aware, and aligned with real-world scenarios. I call it the learn and adapt loop.
In your view, how will AI-driven agents influence customer loyalty and brand perception in the next few years, especially as they increasingly handle first interactions?
AI agents are going to define the first impression customers form about a brand, for better or worse. In the next few years, Customer loyalty in the future won’t just depend on how fast an issue gets fixed; it’ll depend on how smart and empathetic that first AI interaction feels. If the AI understands the customer’s context and respects their time, it builds trust. But if it feels robotic or off, it can hurt the brand before a human even gets involved. The real opportunity is for companies to use AI not just to be faster, but to show their brand’s personality and care at scale. The most successful companies will design their AI agents to learn and adapt.
What steps should product leaders take to ensure AI systems remain ethical, transparent, and aligned with a company’s customer values, especially as agents become more autonomous?
Product leaders need to make sure AI systems are built with ethics and trust from the start. There is no other way and no other option; transparency is the key. That means setting clear rules for what the AI should and shouldn’t do, and making sure people can always understand why the AI Agent made that choice. They should regularly measure and monitor the system for mistakes or bias and keep humans involved to make important decisions. Most importantly, the AI should always act in ways that build trust; customers should feel it’s helping them, not tricking them. As AI Agents become more independent, leaders must make sure every action can be tracked, explained, and improved.
Looking ahead, what excites you most about the next generation of AI agents in customer experience, and how are you personally working to advance that vision in your current role?
I’m excited by the idea that AI agents will stop being just the support tools and start being trusted co-workers, capable of understanding nuance, context, and emotion in every interaction.
Day to day, I’m working on that frontier, pushing our AI Agents to handle cases end-to-end with human-level judgment, not just scripted rules. It’s less about hype, more about proving that AI can operate with both smartness, transparency, and accountability and trust.
