AI, Empathy, and Leadership: 5 Questions for Tech Product Manager Bhoomika Ghosh

AI is reshaping not just products but the very way product teams operate. To explore how the rise of AI is changing the role of the product manager, we sat down with Senior Tech Product Lead Bhoomika Ghosh. to get a better idea of the necessary balance between data and human intuition, and what ethical leadership looks like in the AI era.

A passionate technologist with a background spanning engineering, consulting, and product management, Ghosh has led product innovation at the intersection of AI/ML and customer experience. Her fascination with technology’s ability to solve human challenges began early in her career, wherein as an undergraduate, she developed an application that transformed 2D MRI slices into 3D models, helping doctors accurately identify tumor locations and volumes. This early venture sparked Ghosh’s passion for building technology that creates meaningful impact efficiently, and at scale.

We’re thrilled to feature her insights ahead of her appearance at FinovateSpring, where she will speak on the panel exploring gender diversity and responsible AI leadership.

AI is changing how products are built, but how is it changing how product managers operate? 

Bhoomika Ghosh: The evolution of product management in this AI era has been nothing short of transformative. While our north star as a product manager (PM) remains unchanged—i.e., solving customer problems and delivering utmost value to customers—what has shifted is how we navigate towards that vision with AI. I see two dimensions of AI transformation within the product management space: first, we see a rise in product managers who leverage AI as a productivity accelerator. Tools like Bolt and Cursor are revolutionizing our prototyping capabilities, reducing prototype development cycles from weeks to mere hours, and initial design times by 35%. This efficiency gain allows PMs to invest more time in understanding deeper emotional user needs and ensuring our products create genuine value. Second, we see AI-enhanced PMs, who are using AI to fundamentally transform customer experiences in ways we never imagined. For example, Microsoft’s 365 Copilot leverages AI to revolutionize customer service interactions, which resulted in a 40% reduction in resolution time through AI-powered insights and recommendations. Looking ahead, I see AI enhancing our ability to make better quality and higher quantity decisions faster and evolve with customers in real time to deliver what matters the most to them.

What role does human intuition play in AI product management? 

Ghosh: In today’s rapidly evolving tech landscape, AI adoption has surged from 33% to 65% in just the past year—making the role of human intuition in product management more crucial than ever. While AI excels at processing vast amounts of data and automating routine tasks, our uniquely human capabilities of judgment, critical thinking, and empathy remain irreplaceable. Take the evolution of customer service chatbots, for instance. While AI can handle >50% of routine inquiries, it’s the human product managers who recognize that customers need occasional human intervention for complex emotional situations, leading to hybrid human and AI solutions. This exemplifies what I call the “PM’s AI Trilogy of Responsibility,” where product managers in the AI world are now responsible to safeguard customer trust, ensure scalable efficiency, and measure genuine success beyond just automation metrics. The irony isn’t lost on me that in pursuing “artificial” intelligence, we’ve heightened the importance of “human” intelligence.

Let’s talk leadership. How do you think the rise of AI is reshaping what good leadership looks like in product and technology teams? 

Ghosh: In the AI era, product and technical leadership demand a fundamental reimagining of how we guide teams and build products. What’s fascinating is that while 92% of global business leaders report positive ROI from their AI investments, success isn’t purely about technological implementation—it’s about creating an environment where both innovation and ethical considerations flourish. We see that the most successful AI products emerge from teams where leaders have mastered the delicate balance between data-driven decision-making and human empathy. Take Netflix’s AI-powered recommendation system, which generates $1 billion in annual value not just through algorithmic excellence, but through leaders who understood the critical intersection of technical capability and user psychology. This exemplifies how modern tech leadership requires a dual focus: pushing technological boundaries while staying deeply anchored in customer impact and responsible AI practices. As we navigate this transformation, I also see good leadership exuded in a way where teams are taught to watch over their shoulders and think beyond the happy path scenarios. For instance, what happens if AI was to fail? What would be your contingency plans? These tenets will help leaders foster an environment where teams feel empowered to innovate responsibly, ensuring our products genuinely enhance human experiences.

Many industries beyond big tech are leveraging AI. What advice would you give to product teams in a traditional industry like finance who are building their first AI-driven solutions? 

Ghosh: The financial sector’s AI transformation offers powerful lessons for product teams embarking on their AI journey. While our brains might be the most sophisticated decision-making system, AI serves as a powerful amplifier of human capabilities, particularly in areas like fraud detection, personalized banking experiences, and risk assessment. In my experience, the key to approaching AI implementation is to solve specific customer pain points, and not solely use it as a technological showcase or a competitive advantage. I suggest AI implementation using a three-pronged approach. First, start with well-defined, high-impact use cases where AI can demonstrably improve customer experience rather than implementing AI for its own sake. Second, build cross-functional teams that blend domain expertise with AI capabilities. For instance, when developing AI-powered fraud detection systems, its combination with financial security expertise and machine learning capabilities enables real-time transaction monitoring and anomaly detection, protecting both customers and institutional integrity. Finally, and most crucially, establish robust feedback loops with your customers early in the development process. I often challenge teams to consider, “How would this feature feel to a user having their worst day?” This perspective is particularly vital in finance, where AI decisions can significantly impact people’s lives. I’ve seen the most successful AI adoption use cases aren’t simply using the technology, but rather building trust through it using transparent, ethical, and user-centric solutions.

Finally, what aspect of FinovateSpring are you most looking forward to? 

Ghosh: I’m particularly excited about participating in the gender diversity panel at FinovateSpring, where we’ll explore the crucial intersection of diverse leadership and responsible AI development across industries. As a woman leader in tech, I advocate that diverse voices in product development aren’t just about equity or quotas, but rather about building better, more comprehensive solutions that serve entire customer bases. Beyond the panel, I’m looking forward to engaging with fellow industry leaders about responsible AI implementation in fintech. As we see AI adoption in financial services growing at an unprecedented rate, the conversations around ethical AI development and secure deployment become increasingly critical. I’m eager to both share insights from successful AI implementations I’ve seen and learn from other organizations’ experiences in navigating this complex landscape.

Don’t miss your chance to hear Bhoomika Ghosh, along with a wide range of other thought leaders and experts, on the FinovateSpring stage next month on May 7 through 9. Tickets are now available!

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