In a tech world sprinting toward “automate everything,” Madhuri Somara isn’t just keeping pace — she’s one of the individuals who are shaping the race.
She’s the product leader who turns AI ambition into systems that actually work. Not hype. Not vaporware. Real platforms that cut costs, boost efficiency, and make customer experiences feel less robotic and more human.
With 10+ years building intelligent platforms for global teams, Madhuri has designed AI agents that don’t just answer queries — they learn, adapt, and elevate entire customer journeys. Taking complex AI capability and translating it into products people instantly understand and trust is her super strength.
That’s why she’s become one of the sharpest voices on the future of customer service, autonomous agents, and how product teams must evolve as AI rewrites the operating manual for modern companies.
AllTech Magazine sat down with Madhuri to break down her playbook — how she builds AI systems that scale, where teams get integration wrong, and the massive opportunities ahead as intelligent agents move from “experimental” to the backbone of digital ecosystems.
Many companies talk about “human-centered AI,” but few execute it well. What does designing AI agents with empathy, trust, and transparency truly look like in practice?
Human-centered AI isn’t about making an agent sound friendly; it’s about removing the real pain people deal with every day.
For example, with AI Agents in customer service products, a case management agent, means the agent handling the late-night follow-ups no one has the energy for, cleaning up messy case data so agents don’t spend half their shift fixing fields, and explaining every automated action so nothing feels like a black box.
Empathy shows up in the details: predictable behavior, auditability, and giving humans the final say. That’s what actually builds trust in the real world.
How can AI systems be trained or structured to understand human context and emotion without crossing into the realm of surveillance or overreach?
AI can understand human context without becoming surveillance when it focuses on work context rather than personal signals.
The way we build our agents is simple: capture the operational breadcrumbs people already produce, case history, past actions, and resolutions, and use that to reduce cognitive load, not to profile anyone.
One of the best pieces of feedback I’ve heard came from a support engineer who said, “With your AI agents, I don’t have to remember the context every single time.” That’s the sweet spot. The agent tracks the work, not the person.
By keeping the data scoped, explainable, and tied to task-level outcomes rather than emotions or personal identifiers, you get an AI that feels supportive, aware, and helpful without ever crossing into overreach.
What are some of the most common mistakes companies make when they try to automate customer experience without considering the human element?
In my opinion, Companies go wrong when they automate without understanding the human realities behind the experience. They end up accelerating broken processes, forcing customers through rigid flows that lack judgment, and creating tools that add friction for the very agents meant to benefit from them.
They automate decisions without explaining why, optimize for deflection rather than real resolution, and overlook the emotional or high-stakes moments when a human absolutely needs to step in. The result: faster operations, worse experiences.
Can you share a real-world example where AI improved customer interactions by enhancing human touchpoints rather than replacing them?
A strong example is what we’ve seen with Case Management Agent in frontline support teams. Instead of trying to replace agents, the AI does the groundwork, scanning case history, pulling relevant knowledge, drafting follow-ups, and prepping the next best action.
One team told us that before this, they spent a huge amount of time just “getting up to speed” on each case. After deploying the agent, they were finally able to start customer calls fully prepared, without the scramble.
The customer immediately feels the difference: the agent comes in informed, focused, and ready to solve the issue instead of digging for details. The AI stays behind the scenes, clearing the clutter so the human can deliver a faster, more personal, and more confident interaction. That’s the kind of automation that amplifies the human touch instead of replacing it.
How do you measure whether an AI-driven customer experience is actually empathetic or trustworthy from the user’s perspective?
I measure whether an AI-driven customer experience is empathetic and trustworthy by observing real user behavior and outcomes. Key indicators include customers not having to repeat themselves, agents overriding the AI less, and sensitive situations being handled appropriately.
Improvements in customer sentiment, faster resolutions without pressure, and reduced cognitive load for agents all signal empathy in action. Finally, when users understand the AI’s decisions and can audit them, trust naturally follows.
What role do cross-functional teams, especially those outside of engineering, play in ensuring that AI products reflect empathy and ethical design principles?
Cross-functional teams are essential to making AI products truly human-centered. Designers, product managers, and frontline employees bring insights into real user needs, pain points, and emotional moments that engineers alone might overlook. Legal, compliance, and ethics partners ensure decisions respect privacy, fairness, and transparency.
Marketing and support teams provide feedback on how customers actually perceive the AI in real interactions. By combining these perspectives, the product balances technical capability with empathy, trust, and ethical considerations, ensuring the AI supports humans rather than surprises or undermines them.
As AI becomes more autonomous, how can product leaders balance efficiency and cost reduction with long-term customer trust and loyalty?
As a product leader, I balance efficiency and cost reduction with long-term customer trust by focusing on how AI enhances human outcomes, not just cuts effort. In my work with AI Agents, I prioritize automating repetitive tasks while keeping humans in control of critical decisions, ensuring the system is transparent, auditable, and predictable.
By designing for clarity and giving users the ability to override or guide the AI, I make sure efficiency gains never come at the expense of trust. This approach lets me scale automation while strengthening customer loyalty, turning AI into a tool that empowers both employees and customers.
Looking ahead, what skills will the next generation of AI product leaders need to build systems that are not just intelligent but also genuinely human-centered?
The next generation of AI product leaders will need a mix of technical fluency, human insight, and ethical judgment. Beyond understanding models and data, they must deeply grasp human behavior, workflows, and pain points, knowing when to let AI assist and when humans must lead.
They’ll need strong cross-functional collaboration skills, bringing together designers, engineers, support teams, and ethics partners to ensure decisions balance capability with empathy and fairness. Finally, they must be fluent in transparency and explainability, building systems that earn trust through clear reasoning and predictable behavior, not just flashy intelligence. The leaders who master this blend will create AI that truly amplifies human potential.
