The Excel Macro Prophecy: From 1996 Debugging to AI-Driven Fashion

A three-decade journey from warehouse floors to AI boardrooms - and why the Hanes-Infosys partnership validates everything I learned debugging macros in Sharjah

The Day I Became a Prophet (Without Knowing It)

Picture this: Sharjah, UAE, 1996. I'm on the phone in my office by the creek, 7UP in hand, staring at a computer screen that's mocking me. The Microsoft Excel macro I've been debugging for hours—the one that's supposed to track inventory for Hanes European operations—keeps crashing. The Brussels team is waiting for answers. And I'm wondering if my N.I.I.T Systems Management degree was worth more than the paper it was printed on.

I had no idea I was witnessing the future.

This week, Infosys announced a 10-year strategic alliance with HanesBrands, deploying AI-first solutions and generative AI to transform their entire digital landscape. The irony isn't just poetic—it's prophetic. That crashing Excel macro I was debugging 30 years ago was the primitive ancestor of the AI agents Infosys is now building for the same company.

But here's the thing nobody talks about in these transformation announcements: the future was always hiding in plain sight, wrapped in the mundane problems we solved every day.

Chapter 1: When "Advanced" Meant Excel Macros That Actually Worked

Fresh out of Delhi University with a Commerce degree and armed with what I thought was cutting-edge knowledge from N.I.I.T's Advanced Systems Management program, I landed what seemed like a dream job. MEXX European Brand led to Future Apparels Ltd. in Sharjah—one of Hanes EV's core suppliers.

The year was 1996. The internet was something computer scientists talked about. E-commerce was a fantasy. And yet, there I was, working with what were genuinely revolutionary systems for the fashion industry.

While most apparel companies were still operating on phone calls, faxes, and gut instinct, Hanes was building something different. They had created sophisticated, custom-built Excel macros integrated with ERP and EDI systems that could manage global supply chains in real-time. These weren't simple spreadsheets—they were complex, interconnected systems that could predict demand, optimize inventory, and coordinate with suppliers across continents.

At the time, Infosys was barely a blip on the radar outside Bangalore's tech ecosystem. The idea that this small Indian company would one day become a global transformation partner for the very brands I was working with seemed as likely as me becoming a technology entrepreneur.

Spoiler alert: both happened.

Chapter 2: The Patterns Were Always There

Working closely with the Hanes EV team based in Brussels, I began to see patterns that wouldn't become obvious to the broader industry for another decade. The companies that were investing in technology—even primitive technology—weren't just more efficient. They were fundamentally thinking about their business differently.

Hanes wasn't just using Excel macros to track inventory. They were using data to predict consumer behavior. They weren't just implementing EDI systems to communicate with suppliers. They were creating digital ecosystems that could adapt to market changes in real-time.

I was witnessing the birth of what we now call "data-driven retail," except nobody called it that yet.

The global journey that followed—UAE to Sri Lanka to New York City, back to India, then Dubai, and finally back to India—wasn't just career progression. It was a masterclass in how technology adoption varies across markets, cultures, and business models. Each location taught me something new about the relationship between technology and commerce.

In Sri Lanka, I saw how emerging markets could leapfrog traditional infrastructure. In New York, I experienced the epicenter of fashion's digital evolution during the early internet era. Each move revealed new pieces of a puzzle I didn't even know I was solving.

Chapter 3: The Great Pivot (That Wasn't Really a Pivot)

By the early 2000s, what seemed like a career shift from commerce to technology was revealing itself as something else entirely. I wasn't abandoning my fashion industry expertise—I was applying systems thinking to solve the same problems I'd always worked on, just with exponentially better tools.

This realization led to founding MapleSage, where we're now building AI-powered solutions for the exact challenges I first encountered in that Sharjah warehouse. The Sage AI ecosystem we've developed represents the natural evolution of those early Hanes systems—but instead of debugging Excel macros, we're orchestrating AI agents that can handle complete Product Lifecycle Management from conception through recycling.

The parallels are striking:

1996 Hanes Challenge: Track inventory across multiple suppliers and predict stockouts 2025 MapleSage Solution: AI agents that predict demand patterns and automatically optimize inventory across global supply networks

1996 Hanes Challenge: Coordinate quality control across geographically dispersed manufacturing 2025 MapleSage Solution: Computer vision systems that provide real-time quality assessment and compliance monitoring

1996 Hanes Challenge: Manage supplier relationships and communication 2025 MapleSage Solution: AI-powered supplier ecosystem management with automated ESG compliance tracking

The tools evolved. The problems remained remarkably consistent.

Chapter 4: Why the Hanes-Infosys Partnership Is Actually Revolutionary

When Scott Pleiman, HanesBrands' Chief Strategy, Transformation, Analytics and Technology Officer, talks about seeking "an experienced collaborator with deep domain expertise and advanced capabilities in AI-driven transformation," he's describing exactly what I learned was necessary back in 1996.

The companies that will thrive aren't just adopting new technology—they're partnering with organizations that understand both the technology and the domain. Infosys isn't just bringing AI to HanesBrands. They're bringing AI that understands fashion retail at a molecular level.

The 10-year timeline is particularly telling. This isn't about quick wins or surface-level digitization. It's about building new organizational capabilities that will define the next generation of retail operations. Having lived through multiple cycles of fashion industry transformation, I can tell you that the companies that think in decades, not quarters, are the ones that survive disruption.

Chapter 5: The Broader Context - What Gap and H&M Taught Us

My recent analysis of retail transformations provides crucial context for understanding why the Hanes-Infosys partnership matters. Gap's struggles with tariff pressures and inventory management, and H&M's successful profit rebound through operational efficiency, demonstrate a fundamental truth: in today's retail landscape, technology isn't a competitive advantage—it's table stakes for survival.

But here's what's different about the Hanes approach: they're not just implementing technology to solve immediate problems. They're rebuilding their entire operational philosophy around AI-first thinking. This is the difference between companies that use AI and companies that are transformed by AI.

Chapter 6: The Infosys Renaissance - From Bangalore Startup to Global Transformation Partner

The Infosys of 2025 bears little resemblance to the company that barely registered outside tech circles in the 1990s. Their evolution from a services provider to a transformation partner mirrors the broader evolution of how we think about technology in business.

Their work isn't limited to fashion retail. This year alone, they've become the digital innovation partner for Glion Arena Kobe in Japan using Infosys Cobalt, enabled Bank CTBC Indonesia's cloud-based digital banking transformation through their Finacle suite, and partnered with Mastercard to simplify cross-border payments.

What's remarkable is how they've maintained domain expertise across vastly different industries while building platform capabilities that can be adapted to sector-specific challenges. This is exactly what I learned was necessary from working with Hanes in the 1990s—you need both technological sophistication and deep industry understanding.

Chapter 7: The MapleSage Parallel - Building Tomorrow's Solutions Today

As I built MapleSage and our Sage AI platform, I kept returning to lessons learned from those early Hanes systems. The most important wasn't technical—it was philosophical. The companies that succeeded weren't just adopting new tools. They were rethinking their fundamental approach to business problems.

Our AI ecosystem doesn't just automate existing processes. It reimagines how fashion retail can work when you have perfect information, predictive capabilities, and the ability to optimize across variables that humans can't even track simultaneously.

We're handling challenges that seemed impossible in 1996:

• Real-time ESG compliance monitoring across global supply chains
• Predictive sustainability impact modeling for product decisions
• AI-powered demand forecasting that factors in social media trends, weather patterns, and economic indicators
• Automated circular economy optimization for product lifecycle management

But the core insight remains the same as it was in that Sharjah warehouse: the companies that survive are the ones that use data to make better decisions, faster.

Chapter 8: The Cosmic Joke of Career Trajectories

There's something beautifully absurd about spending 30 years working on the same problems with exponentially better tools. What seemed like a dramatic career pivot from commerce to technology was actually just a natural evolution along a consistent trajectory.

The buyer who debugged Excel macros in 1996 and the entrepreneur building AI platforms in 2025 are solving identical problems—they just have radically different weapons at their disposal.

This perspective gives me a unique vantage point on the Hanes-Infosys partnership. I know what Hanes was capable of with primitive tools because I built those tools. I know what's possible with today's AI because I'm building those solutions. The gap between those two realities is where the magic happens.

Chapter 9: What This Means for the Next Decade

The Hanes-Infosys partnership isn't just about one company's transformation—it's a template for how traditional industries can evolve without losing their core identity. HanesBrands isn't becoming a technology company. They're becoming a fashion company that uses technology to be better at fashion.

This distinction matters because it addresses the fundamental question that every traditional industry faces: How do you embrace radical technological change without losing the domain expertise that made you successful in the first place?

The answer, as I learned from working with both sides of this equation, is partnership with organizations that understand both the technology and the domain. Infosys brings AI capabilities. HanesBrands brings fashion retail expertise. The combination creates something neither could achieve alone.

Chapter 10: The Patterns That Predict the Future

After three decades of watching technology transform fashion retail, I've learned to recognize the patterns that signal genuine transformation versus surface-level change. The Hanes-Infosys partnership has all the markers of something significant:

1. Long-term commitment (10 years) that acknowledges transformation takes time
2. Platform thinking (LEAP + Topaz) rather than point solutions
3. AI-first approach that rebuilds processes rather than automating existing ones
4. Domain expertise that understands fashion retail's unique challenges
5. Executive sponsorship at the strategic level, not just operational

These are the same patterns I recognized in those early Hanes systems that seemed so revolutionary in 1996. The tools have evolved exponentially, but the markers of successful transformation remain consistent.

Conclusion: Full Circle with Better Weapons

As I write this, I'm struck by the realization that my career has been one long research project into a single question: How do you use technology to make better decisions in complex, rapidly changing markets?

The Excel macro I was debugging in 1996 and the AI agents we're building at MapleSage in 2025 are both attempts to answer that same question. The scale, sophistication, and capabilities have evolved beyond recognition, but the fundamental challenge remains unchanged.

The Hanes-Infosys partnership represents the culmination of everything I've learned about successful technology transformation. It's not about replacing human judgment with algorithms—it's about augmenting human expertise with capabilities that were previously impossible.

Karmesh Vaswani's comment about creating "substantial business benefits and enhanced customer experiences" through AI innovation captures the essence of what I witnessed in those early Hanes systems and what I'm building with MapleSage today. Technology should make companies better at what they already do well, not transform them into something they're not.

Sometimes the most profound journeys are the ones that bring you full circle, but with exponentially better tools and a deeper understanding of the problems you're trying to solve.

That crashing Excel macro in 1996 wasn't just a technical problem—it was a glimpse of the future. It took 30 years, but the future finally caught up.

Parvind Dutta is the founder of MapleSage, where he builds AI-powered solutions for fashion retail transformation. His unique perspective combines three decades of global fashion industry experience with deep technical expertise in AI and enterprise systems. He has lived and worked across UAE, Sri Lanka, NYC, and India, witnessing firsthand the technological evolution of global retail operations.