As a Staff Product Manager at one of the world’s largest retailers and the recipient of the Best E-Commerce Product Manager of the Year at The ECDMA Global Awards 2026, Niki Aghaei has redefined how heavy-industry enterprises bridge the gap between technical deployment and genuine business value. From leading high-stakes fulfillment products across the Americas to mentoring female-led startups in the Middle East, she has spent her career ensuring that technology solves real problems rather than just filling a roadmap. Her perspective is shaped by a unique journey through Big Four consulting, major banking institutions, and global consumer electronics, making her a leading voice in the strategic integration of artificial intelligence.
In this discussion, we explore the shift from “bolted-on” to “built-in” intelligence, the importance of business architecture in product success, and the methods for maintaining operational agility in markets where data is scarce. We also examine why many organizations struggle to see a financial return on their AI investments despite the rapid pace of adoption.
When intelligence is treated as an afterthought in product development, why does it consistently fail to keep pace with the market?
Most teams I have worked with tend to view AI as a layer to be added after the core product is already functional, but by that point, they are essentially chasing a moving target. In the time it takes to build a standard product and then “bolt on” an AI feature, the underlying data has evolved and the market demands have shifted, leaving the tool lagging behind reality. Intelligence needs to be woven into the system’s DNA from the very first day so that the product doesn’t just generate a static report on what happened last quarter. Instead, a built-in approach allows the system to react to what is happening in the moment, such as real-time inventory shifts or sudden demand spikes across different regions. When we integrated these capabilities into our forecasting tools, we weren’t just adding a feature; we were creating a system that lives and breathes with the business.
With reports showing that nearly 40% of business applications will use AI agents by the end of 2026 while only 39% of companies see a financial benefit, how can leaders ensure their AI investments actually move the needle?
The disconnect between deployment and profit usually happens because companies prioritize the model over the decision it is meant to improve. To bridge this gap, you have to start with the specific business decision that needs to be made—like how much inventory to ship to a specific hub—and then work backward to the data. I have found that when we identify the exact signal that can improve a decision and select a model based on that clarity, the financial impact becomes measurable. In my work across Canada and Mexico, we applied this rigor to inventory assortment tools, which were projected to improve internal team efficiency by 20%. It is not about how many models you deploy, but about how many high-stakes decisions you can successfully optimize through those models.
Your experience at a Big Four firm involved an eight-figure marketing platform that faced significant hurdles; what did that teach you about “organizational readiness”?
That project was a turning point for me because it demonstrated that even the most expensive, technically sound requirements rarely survive their first contact with a live organization. I watched as beautifully designed roadmaps diverged from the actual way people worked on the ground, creating friction that stalled the entire implementation. Since then, I’ve treated business architecture as a fundamental part of the product itself, essentially checking if the organization is “ready” to use what we are building before we write a single line of code. You have to account for the human element and the existing workflows, or the technology will simply sit on a shelf. This lesson was vital when I later moved to a major U.S. bank, where we used customer behavior data to boost conversion rates by a fifth, simply by aligning the product with real-world user habits.
When you are operating in genuine uncertainty, such as entering Middle Eastern markets where consumer benchmarks are thin, how do you build a product without a traditional roadmap?
Operating in markets with limited historical data feels like flying through a fog, where there is no instruction manual and every step is a test of a hypothesis. In those environments, you cannot rely on what worked in a data-rich market; instead, you must build measurement mechanisms into the strategy itself so that the product generates its own signals as it moves into the market. This approach allows you to gather data in real-time and examine what it is telling you, even when the results challenge your initial assumptions about consumer behavior. It’s a process of moving with intense intention and being willing to pivot based on the breakthrough understandings that only happen when you are deep in the work. By the time we launched high-volume consumer laptops or mentored startups in these regions, we were creating our own benchmarks rather than waiting for them to appear.
As a judge for Techstars Startup Weekend Women 2026, you’ve seen over 1,000 participants from 25 countries; what common mistake do you see founders making when they pitch AI-driven solutions?
The most frequent pitfall is what I call “searching for a problem to fit the model.” It is very easy to fall in love with a sophisticated new algorithm and then go hunting for a place to use it, but that is the reverse of how successful technology is built. I always tell the founders I mentor that the best technology starts with a visceral, real-world problem that someone is currently struggling to solve. You have to ask yourself a very simple, honest question: Does AI actually make this decision better, or are we just using it because it’s the trend? When you see a founder who understands the decision-making process of their customer inside and out, that’s when you know you’re looking at a product with the potential for an enduring business advantage.
What is your forecast for the evolution of AI agents in the enterprise space over the next few years?
We are moving away from the era of “AI for the sake of AI” and into a period of extreme accountability where every agent must prove its worth through specific business outcomes. I expect to see a shift where AI is no longer a separate department but is fully absorbed into the core architecture of every successful product, making the distinction between “software” and “intelligent software” obsolete. The companies that will thrive are not those rushing to deploy the most agents, but those that master the art of defining the right signals to improve their most critical business decisions. Success will be defined by the ability to turn data into immediate, localized action across global operations, effectively closing the gap between a change in the market and a company’s response. My focus remains on ensuring that as intelligence scales, it remains grounded in the practical realities of logistics and customer needs.