AI and E-Commerce: Indranil Guha on What’s Next for Marketing Innovation

The way we shop, engage, and interact with brands is shifting faster than ever. At the heart of this transformation is artificial intelligence, no longer confined to back-end algorithms. AI now shapes the entire customer experience, from personalized product recommendations to marketing outreach. 

We spoke with Indranil Guha, General Partner at VMG Partners and a veteran investor in commerce infrastructure and enterprise AI.

Guha has spent over 15 years exploring how AI can drive real business value and improve consumer engagement. He is investing in companies like Attentive, Boulevard, Weee!, Zitcha, and Auxia. Guha’s perspective bridges investment strategy, product innovation, and the future of AI-driven commerce. 

Before joining VMG, Guha led go-to-market teams at Signifyd, the world’s most widely deployed provider of payments optimization and fraud management, serving clients such as Walmart, Samsung, Abercrombie, and Vuori.

Prior to that, he invested in early and growth-stage enterprise AI applications at Bain Capital Ventures, supporting high-profile companies like Signifyd, 6Sense, Wrike (acquired by Citrix for $2.25B), Moveworks (acquired by ServiceNow for $2.85B), and Gainsight. 

Guha’s long-standing passion for e-commerce enablement is reflected in his investments in SquareTrade (acquired by AllState for $1.5B), BloomReach, and Optimizely. With a career spanning enterprise AI, commerce infrastructure, and go-to-market leadership, he bridges the worlds of technology innovation and venture investment, helping companies translate AI-driven insights into real-world business impact. 

In this conversation, Guha shares candid insights on generative AI, hyper-personalization, adoption challenges, and advice for the next generation of tech innovators. 

Indranil, thanks for taking the time today. To start, could you give our readers a sense of your journey, what drew you to AI in the enterprise world, and what continues to inspire your work today? 

Indranil Guha: Absolutely. I’ve been immersed in enterprise AI since around 2007. Back then, we didn’t call it SaaS; we were talking about “application service providers,” and machine learning applications were labeled as “big data apps.” I began as an investor at Bain Capital Ventures, focusing on security and compliance automation, where my curiosity about AI first took root. 

By 2009, I joined BloomReach as an early product manager. We were working on digital experience personalization, an early form of machine learning in the enterprise. We stitched together different data sources to generate insights about consumer behavior and deliver the “next-best action” for the enterprise.

That idea, creating real business value through intelligent automation, has driven me ever since. Over the last 15–16 years, I’ve focused on how machine learning and now AI transform enterprise applications, and how that can intersect with the consumer world, where you can measure impact almost in real-time. 

That’s incredible. With AI’s growth, it seems every business is thinking about it. In your view, how has AI started to actually change the customer experience in enterprise scenarios, particularly in e-commerce, this year? 

Guha: We’re in the middle of one of the most significant shifts in consumer behavior since online shopping began. At the start of this year, maybe 1–2% of buying journeys began with a generative engine like ChatGPT. Now, within months, that’s jumped to roughly 10–20%, and in some categories, such as travel, it’s over 25%. 

This changes the “front door” to the Internet. Google, for instance, now prioritizes AI search results at the top of pages, pushing traditional ads below the fold. Consumers are searching in a conversational, contextual way. Instead of “pre-workout protein bar,” they type “free workout, low-sugar, gluten-free protein bar with protein.” Companies have to reimagine both front-end experiences and back-end systems to keep up. 

That’s fascinating. So, with these changes, are businesses seeing impacts on customer retention? How does this shift influence loyalty and repeat engagement? 

Guha: If your brand has low customer loyalty, this is a scary moment. Historically, you could spend to reacquire customers, but now, if discovery happens inside a generative AI engine and consumers bypass intermediaries, that playbook doesn’t always work. 

Highly branded products, like Apple, face a different challenge: they already have an audience. Their focus becomes ongoing education and engagement. But for less-branded products, the risk is real. Companies have to rethink retention in a world where the “front door” isn’t theirs anymore. 

One approach that’s gaining traction is hyper-personalization. From acquisition to engagement, it seems critical. How are companies using AI to scale personalization without overwhelming users? 

Guha: Hyper-personalization is essential. The old “one homepage fits all” model is outdated. You now need potentially hundreds of homepages, or even millions, based on context: who the user is, where they’re coming from, what events or seasons are relevant. 

Managing this manually is impossible. AI can ingest brand guidelines, product assets, and optimize experiences in near real-time. For example, a sports merchandise site can rotate product assortments by team, season, and events multiple times per week, automatically generating tailored experiences. This creates a scalable infrastructure that meets customer expectations without requiring the hiring of hundreds of web developers. 

That makes sense. However, some people worry that hyper-personalization, especially in emails or outreach, can come across as robotic. How should brands avoid that pitfall? 

Guha: Exactly. Many “AI personalization” tools today blast more content cheaply; that’s spam, not personalization. True hyper-personalization begins by consolidating all customer data, including email, SMS, video, and purchase history, into a unified understanding of preferences. 

Then, predictive models determine what to send, when, and with what incentive. Companies like Auxia automate this, while keeping humans in the loop for review. This enables a segment-of-one experience at scale, offering highly personalized interactions without being robotic. 

From an investor’s lens, how has generative AI influenced VMG Partners’ strategy? Are you evaluating products differently now? 

Guha: Generative AI highlights the importance of solving complex, high-value enterprise problems. Foundation models are powerful, but they can’t replace deep domain expertise. Real enterprise impact comes from automating workflows that were previously manual, financial planning, inventory, or customer support, but you need intimate knowledge of business context. 

We evaluate founders on domain expertise because they understand workflows, escalation points, and operational nuances. Architecturally, we look at four layers: data, model, workflow, and application. A defensible AI business needs to excel in all four areas, and founders with domain experience have a significant advantage. 

When brands attempt to integrate AI, where do they often encounter friction, and how can leadership navigate it? 

Guha: The main friction is change management, not technology. If you want to automate a contact center, you can’t hand it over to AI overnight. You need small-scale testing, a gradual ramp-up, and a structured cutover process that spans months or even years. 

Success depends on three key factors: a top-down commitment to transformation, selecting high-impact use cases, and clarity on business objectives. Leadership must sponsor the change internally; external vendors alone can’t drive adoption. 

And what about cases where automation starts to overpower other business priorities? How should CTOs or founders balance AI with existing workflows? 

Guha: Prioritize AI for high-value areas where political resistance is lower, like outsourced contact centers or marketing content creation. Align automation with clear business objectives so teams see tangible benefits. Gradual adoption, human oversight, and alignment with organizational goals are key. 

For Gen Z or millennial beginners exploring consumer tech and AI, what would you tell them? How should they start building a meaningful career in this rapidly evolving space? 

Guha: Give yourself permission to build. The old barriers around who can develop technology are disappearing. But pairing this with domain expertise is critical; you want to be an “orchestra conductor” in a multi-agent AI world. 

AI can’t generate taste, creativity, or cultural intuition, which remains human. Focus on what excites you, build expertise, and engage with the creative core. That combination opens up career paths that didn’t exist before and positions you to thrive in a world where AI amplifies human capabilities rather than replacing them. 

That’s such invaluable advice. So, if you were speaking directly to someone who’s just starting today, they’d hear experiment boldly, learn deeply, and trust your instincts, but always couple that with understanding the domain you care about. 

Guha: Exactly. Don’t be afraid to try new things, even if they seem daunting. Build, fail, iterate, repeat. The opportunities today are incredible, but the ones who combine creativity, domain knowledge, and technical experimentation will genuinely stand out. 

Thank you so much for sharing your insights today, Indy. It’s been a pleasure hearing your thoughts on AI, e-commerce, and the next generation of tech innovators. I’m confident that our readers will walk away with a wealth of actionable wisdom. 

Guha: Thank you. It’s been a pleasure talking with you. I hope it inspires some readers to get started, experiment, and really build something meaningful in this AI-driven world. 

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