Pioneering AI-Driven Product Innovation Done By Yaswanth Jeganathan

Yaswanth Jeganathan is an accomplished product leader specializing in e-commerce and AI/ML innovations based in Seattle, Washington. With a comprehensive educational background that includes an Executive MBA from the Wharton School of Business (expected May 2025), a Master of Science in Information Systems Management from Carnegie Mellon University, and Bachelor of Engineering in Electrical and Electronics Engineering from Anna University, where he secured an impressive university rank of 11 among 5,547 students, Yaswanth combines academic excellence with practical industry expertise. With an industry experience of nearly 10 years in technology, he has built a successful career in product management in B2B SaaS and B2C space with noted strength in retail, logistics, marketplace solutions, and cutting-edge AI/ML applications.

Q1: What attracted you to product management specifically for e-commerce and AI/ML industries? 

A: I have been passionate about product management arising from a strong desire to bring solutions that can target business benefits versus consumer satisfaction.

 The e-commerce industry excites me because it is a never-ending state of flux and one that constantly brings forth challenges at the intersection of technology, user experience, and business strategy. Concerning AI/ML, I have always liked the fact that AI/ML technologies can transform how we enhance customer experiences and optimize operational performance. By product managing across these domains, I put myself in an enviable position where technical innovation gets aligned with real-world applications and measurable business outcomes.” 

Q2: What is your approach to leading new go-to-market strategies for new products/features? 

 

A: My approach to go-to-market strategies is very collaborative and data-driven. My starting point is to ensure the products adequately meet customer needs and pain points, constantly in touch with the research teams to validate assumptions. Afterward, I engage cross-functional teams, including engineering, data science, legal, selling/account management/vendor management professionals, marketing, and customer support, to forge common ground on messaging, timing, and execution plans. For example, in leading the go-to-market of a generative AI product, I ensured stakeholders were completely on the same page in terms of technical capabilities and business impact, whereby resulting in a smooth launch that increased sales conversions. I believe that successful product launches require equal attention to product excellence, operational readiness, and strategic internal and external coms.

 

Q 3: How have you used machine learning to address business problems?

A: Machine learning for me is an amazing tool that can solve difficult business problems, which is impossible with the conventional ways. One of the major problems I took on was search relevancy, improving it by trying to measure and reduce catalog metadata incorrectness. This was successfully achieving measurement and minus error across 8 countries via a multi-modal machine learning approach of structured text, unstructured text, and images. In another instance, with respect to enhancing the fraud detection algorithm accuracy from 60% to 98%, led strategy to integrate cybersecurity IP detection and multi-factor authentication. The key is finding the right problem where ML can give you an exponential rather than incremental improvement, and then carefully engineering solutions to solve it while always keeping business needs in view. 

 

Q 4: What methods did you find effective in growing an early-stage product into a significant revenue generator?

A: Growing a product is all about balancing between vision and execution. I was actively engaged with Pitney Bowes between 2017 and 2021 when the API for Shipping emerged as a $100 million-plus ARR business over a four-year journey. The singularly most effective “strategy” was to keep the undiluted customer focus through the roadmap-building effort. We would keep talking with them, trying to interpret changing needs, prioritize the highest yield features, and seamless onboarding-the time for an API integration reduced from 5 days to just 10 minutes through SDK development. It would be that metrics were built to follow progress, and we were not shy from doing what was necessary to pivot. Products built to real problems, with constant iteration on user feedback, are the most sustainable ways to grow.

 

Q 5: At which stage customer opinions impact product development? 

A: Product development is built on customer feedback, which does hold some sway in the theories behind product development. I collect insights through various ways from direct customer interviews, usability tests, analytics data, and feedback through NPS surveys. My work history would include having conducted focus groups by the dozen and client surveys and listening to customers’ pain points and areas of opportunity. Another major point being that the value of this feedback is not just the collection of it, but what is done to implement feedback towards real product enhancement. User feedback gathered during the merchant and developer portal improvement iterations were implemented radically improving the Net Promoter Score in our SaaS API services by 500 basis points. I believe in creating loops with customers to show them how their feedback is directly influencing product development. This builds further customer loyalty and nurtures customer engagement.

 

Q 6: What tools and methodologies are relied upon for effective product management?

A: Essentially, I use various tools that can support new product success through the entire development cycle. Google Analytics, Adobe Omniture, Hotjar, and UserTesting.com can tell you about all sorts of user behaviors. For the planning and execution of products, we had Jira, Balsamiq, and Figma to prototype A/B testing frameworks while Googling Analytics. But for my tools, I lean more toward agile methodologies, intending to identify regular sprint planning, daily standups, and retrospectives to keep the impetus and learn along the way. Looking at product roadmaps, we are data-driven and will build out quantitative metrics against qualitative insights…This is the thinking that helps us to set the right feature in the right way so that we always have an outcome focus.'
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All through the management of cross-functional teams, it becomes most helpful to create an enabling environment of shared purpose and structure for effective communications. I have often coordinated product development in several matrix organizations across a wide variety of functions: sales, marketing, law, engineering, ML operations, and customer support, covering many geographical landscapes. To me, precelebration of wins and establishing clear roles, responsibilities, and success metrics up front in the project is most important. I try my best to respect others’ time for regular touchpoints and alignment. Good documentation throughout is paramount so all decisions and rationale are recorded and readily available. Creating a culture of open conversation about hurts, help, and ideas-for-all members-excluding no rolodex or rank-was also emphasized. Recognizing diverse points of view and celebrating team successes builds high-performing cross-functional teams that are results-oriented and very consistent.

Q8: Give Advice to Someone Approaching Product Management:

A: Advice for product management aspirants is to start building a strong foundation in the hard and soft skills random. Look for opportunities where one can really understand what the customers go through either by internships, in customer-focusing work, or even in projects that you can do yourself. Most importantly, develop your analytical skills to extract data into meaningful insights into possible actions. Important as well are communication skills; practice making difficult ideas simple and convincing. Include perseverance; this field is quite competitive, so do not let rejection dampen your spirits. I started my journey with an internship at a real estate site – while there I did customer surveys and focus groups-and reported straight to the CEO. Those first lessons were ways of figuring out what users need, and translating them into business opportunities. And while you do this, keep questioning, and keep learning; product management changes very fast, mostly from the new technologies of AI/ML.

 

Q9: How do you keep up with the industry’s changes and new technologies? 

A: Keeping oneself abreast of the changes in the fast-evolving world of technology demands intentional and deliberate time and curiosity. This is precisely what would apply much in a year of attending industry conferences and webinars pertaining to product management, e-commerce, and AI/ML innovations.

Thought leaders in the space are followed on platforms like LinkedIn and Twitter, as well as newsletters and publications that focus on trend-emerging topics. Being at the heart of professional communities and networking events offers diverse perspectives on challenges that peers might be facing. Not to mention, creating time to personally dabble in technologies, say fine-tuning LLM with guardrails as well as RAG architecture, is my method of understanding their practical viability. Learning never ends in this field is the reason currently pursuing an Executive MBA at Wharton to get better on the business side with some technical knowledge. 

 

Q10: What are your long-term career aspirations? What steps are you currently taking toward those aspirations?

A: My ultimate goal is to provide success with product strategy and innovation at the executive level, an arena where I can set down the path for the organization and nurture other product leaders. What really excites me is working in a space where AI/ML meets consumer-facing products and has tremendous potential for creating transformational user experiences. Opportunities in this space are what I am working toward by doing an Executive MBA at Wharton School of Business for further development of my strategic-thinking and leadership competencies. Further, I constantly seek out opportunities for tough product work that increases my capability set for different domains and business models. Over my entire career, I have a proven ability to affect business through product innovations, ranging from conversion improvements on millions of products in 21+ countries to ML algorithm accuracy improvements from 70% to 95%. These opportunities combined with my ongoing learning and networking will launch me into leadership opportunities, where I can have a larger impact on organizations and industries.

About Yaswanth Jeganathan

Yaswanth Jeganathan is a product leader with nearly 10 years of proven technology industry experience, passionate about using AI/ML innovations to grow businesses. Yaswanth’s strong academic foundation from Wharton, Carnegie Mellon, and Anna University is complemented with proven experience in delivering end-to-end e-commerce products, SaaS products, and AI innovations. Top career achievements include growing a SaaS API product with over $100 million in Annual recurring revenue and leading go-to-market strategies for AI initiatives that lifted conversion rates by a major percentage. Yaswanth possesses the rare mix of technical depth and business insight required to create profound product experiences in diverse global markets.

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