Artificial Intelligence has been pitched as the miracle fuel of modern capitalism. Generative AI, in particular, has been hailed as the tool that can do it all — write our emails, reinvent customer service, transform logistics, and even reshape entire economies. But as we step closer to 2026, the shine is beginning to fade, and the questions are getting louder. Is AI really delivering on its promises, or are we trapped in another bubble inflated by hype?

For Sumit, a professional who has spent years working at the intersection of AI and e-commerce, the answer is both more sobering and more hopeful. His work, his observations, and his experiments suggest that while AI may be oversold in glossy presentations, its potential remains immense when applied with discipline, humility, and focus. His journey is one of balancing theory with practice, cutting through hype to find real value, and urging entrepreneurs to see AI not as a buzzword but as a scalpel — sharp, precise, and effective when used in the right hands.

Sumit’s fascination with technology began not in boardrooms but in the details of everyday commerce. “Every click, every cart abandonment, every delayed delivery tells a story,” he says. Watching how e-commerce platforms handled (and often failed to handle) those signals drew him toward AI. The first time he saw a personalization engine recommend products more accurately than a merchandiser, it felt like a glimpse into the future. “That was a genuine game-changer,” he recalls. From that moment, the intersection of AI and e-commerce became more than an interest — it became his focus.

Balancing research and reality is what sets him apart. He calls academic research “the compass” and hands-on pilots “the map.” Reports from MIT, McKinsey, or BCG reveal why 95% of AI pilots fail in enterprises: unclear ROI, lack of data readiness, or cultural resistance. But for Sumit, true understanding comes when you test models in the real world — when you discover where systems break, where employees push back, and where customers actually feel the difference. “Case studies inspire, but experimentation teaches humility,” he explains. What worked for Amazon or Alibaba may not work for a regional marketplace in Southeast Asia or a mid-sized retailer in the Middle East. Scaling AI, he insists, is far messier than it looks in glossy case studies.

One of his personal “aha” moments came during a demand-forecasting pilot with a grocery retailer. By predicting stockouts in real time, AI aligned replenishment with actual buying patterns. The result? Empty shelves disappeared. Customers got what they wanted, when they wanted it. “That’s when I realized AI isn’t just dashboards,” he says. “It can keep promises to customers at scale.” That moment sealed his belief that AI’s role in commerce is not just about efficiency, but about trust — ensuring that companies deliver what they say they will.

Still, he admits that his understanding of AI has changed over time. “I assumed AI would replace decision-making,” he says of his early career. Over time, he saw the truth: AI does not replace, it augments. It provides signals, but humans provide context. “AI is a scalpel, not a sword,” he explains. Used carefully, it sharpens strategy; used recklessly, it can cut in all the wrong places.

So why do 95% of enterprise pilots fail? For Sumit, the answer is blunt: because companies confuse building shiny tools with solving real problems. Too many executives want headlines, not outcomes. They invest in dashboards to impress investors, while core issues like inventory accuracy or fraud prevention go untouched. “Start with boring but impactful problems,” he advises. “Fix returns prediction. Improve last-mile routing. Solve fraud. If you do that, ROI follows. If you chase AI pitch decks, you’ll stay in perpetual beta.”

He has seen the difference firsthand. A logistics partner he worked with used AI to optimize last-mile delivery routes. The dashboard itself wasn’t flashy, but the results were undeniable: 20% fuel cost savings and faster delivery. “Nobody celebrated the dashboard,” he says. “But everyone noticed the bottom line.”

This, he believes, is where AI’s future in e-commerce lies. According to UNCTAD, global e-commerce sales surpassed $6.5 trillion in 2023, with B2B trade making up nearly 80% of the total. By 2030, B2B e-commerce alone is expected to top $26 trillion. At this scale, small improvements in fraud detection, supply chain optimization, or cross-border customs clearance can unlock billions in value. Sumit is particularly excited about cross-border e-commerce, where AI can simplify paperwork, hedge currency risks, and detect fraud across geographies. “That’s where the next trillion-dollar opportunity lies,” he says.

He measures success using a triangle: revenue lift, cost reduction, and customer delight. Hit two out of three, and the project is a success. Anything less, he insists, is hype.

The risks of hype are real. Companies that pour millions into AI without strategy will burn cash, alienate customers, and fall behind competitors who focus on fundamentals. He compares it to the dot-com bubble, when every startup added “.com” to their name. Today, AI is the suffix of choice. “But just like Amazon survived that wreckage, enduring AI companies will emerge from this bubble,” he says. Survival, in his view, depends on utility, not buzz.

He sees clear parallels between failed dot-coms like Pets.com and some of today’s AI startups. Many are thin wrappers around large language models from OpenAI or Anthropic, with no defensible moat. “When margins tighten, they’ll vanish,” he predicts. The ones that endure will be those solving boring but universal problems: fraud detection, payments, supply chain visibility. These, he insists, are the innovations that will outlast the hype cycle.

Sumit is not blind to the risks. Over-reliance on AI in customer service, for instance, could backfire. “Nobody wants to argue with a bot about a wrong delivery,” he warns. Automation without empathy erodes trust. His mantra is clear: automate the process, not the relationship. Use AI to predict delays, but let a human make the apology call. That balance, he believes, is what builds loyalty.

He is equally vocal about ethics. Data privacy and algorithmic bias, if ignored, will trigger regulatory crackdowns and destroy customer trust. “If recommendation engines prioritize vendors who pay more instead of what’s best for the customer, regulators will step in — as they should,” he says. Trust, in his view, is the ultimate currency of commerce, and AI systems that undermine it will fail.

Looking ahead, he believes the AI bubble will stretch further, but eventually burst. The survivors will define the next era of technology — just as Amazon and Google rose from the wreckage of the dot-com crash. He places his bets on predictive supply chain analytics, cross-border fraud detection, and hyper-personalized recommendations that actually drive conversions. These solve core pain points and will endure beyond the noise.

On a personal level, Sumit prepares himself by living at the intersection. He reads MIT and BCG reports, but he also pilots tools hands-on. He mentors startups, runs small experiments, and stays close to practitioners. For him, relevance comes not from titles or conferences but from curiosity plus execution.

When asked what advice he would give to entrepreneurs chasing AI trends, he sums it up simply: “Don’t pitch AI. Pitch outcomes. Investors and customers don’t care if you used GPT or regression. They care if you saved costs, boosted sales, or reduced churn.”

And to those dreaming of the next big thing, his message is both hopeful and cautious: “Dream big about what AI can unlock, but proceed with the caution of a CFO. The winners won’t be those who shouted ‘AI’ loudest, but those who quietly solved real problems.”

In the end, his philosophy is not about hype cycles or buzzwords. It is about impact. AI will not disappear — it will mature. Its true revolution, like the internet’s, will take time. The companies and leaders who endure will be those who resist the lure of shiny objects, who embrace the unglamorous work of fixing foundations, and who blend technology with human judgment.

For Sumit, the story of AI in e-commerce is still being written. The bubble may swell and burst, but beyond it lies the next chapter: one where efficiency, scalability, and human trust matter most. In that world, AI will not just be another tool. It will be the quiet engine that powers the promises commerce makes to its customers — and keeps them.