Today, enterprise AI is often deployed faster than it delivers measurable business value. According to Gartner, around 40% of business apps will use AI agents by the end of 2026. At the same time, McKinsey found that only 39% of businesses can see that AI usage has positively influenced the financial side.  

Why does enterprise AI so often fall short of its promise after deployment? What should product teams consider before scaling it? And how can companies build systems they can actually trust? To explore these questions, we turned to Niki Aghaei. She is a Staff Product Manager at one of the world’s largest retailers, and leads fulfillment products for the US, Canada, Mexico, and Chile, including forecasting tools and inventory decision systems. She developed her unique approach through roles at a Big Four consulting firm, a global digital agency, a leading consumer technology company, and a multinational retailer. Named Best E-Commerce Product Manager of the Year at The ECDMA Global Awards 2026, Aghaei also judges Techstars Startup Weekend Women 2026 and mentors female-led startups in the Middle East. 

Logistics operations at scale constantly receive data on demand and product availability across various locations. Niki focuses on ensuring the system can rapidly process this data and utilize it for daily inventory management, thereby minimizing the lag between shifts in demand and the company's response. This approach lays the foundation for the long-term development strategy for international logistics services at the company where she currently works. 

“Most teams I’ve worked with view AI as something to be added after the product is already built. But by then, they are already lagging behind because the data and the market have changed,” says Aghaei. “Intelligence needs to be built-in, not bolted on. When it is part of the system from the very beginning, the product doesn't just report on what happened last quarter; it reacts to what is happening right now.” 

Across Canada and Mexico, Aghaei applied the approach to inventory assortment and forecasting tools, leading their development from initial research through pilot launch. The tools were projected to improve internal teams’ efficiency by 20%. 

The principle of “built-in” stemmed from Niki Aghaei's personal experience.  

She found that product roadmaps often diverge from the actual way companies operate. Even before the current AI boom, at the Big Four firm, Niki helped implement an eight-figure marketing technology platform. The project showed her that requirements drawn up in planning rarely survive contact with a live organization, so she began treating business architecture as part of the product itself, checking organizational readiness before any implementation. 

This lesson also proved valuable when Niki led digital product development at a major U.S. bank. Her team leveraged customer behavior data to enhance the user experience. As a result, conversion rates rose by a fifth, and validating product decisions against real-world data became standard practice. 

“When you're creating something the world hasn't seen before, you're operating in genuine uncertainty. There's no roadmap. No instruction manual. What you have to do is move with intention, test hypotheses, gather the data, and really examine what it's telling you, even when it challenges your assumptions. That's where breakthrough understanding happens.” 

Running her own venture added another layer: helping companies enter Middle Eastern markets where historical data and consumer benchmarks were often thin. Aghaei built measurement into the strategy itself, so usable signals accumulated as a product moved into the market. 

She later helped launch a new generation of high-volume consumer laptops, coordinating engineers, operations specialists, and marketers. This experience reinforced her conviction that analytics must be built into the product during development. 

Niki Aghaei starts with the decision of what a product is supposed to improve. Then the team identifies the data that can support that improvement and selects a model only once they clearly understand the problem and how to measure it. 

Judging at Techstars Startup Weekend keeps showing Niki the same pattern from the other side: “I’ve always believed the best technology starts with a real problem, not with the technology itself. It’s easy to start with a model and search for somewhere to use it. The better approach is to start with a decision someone needs to make, understand what information could make that decision better, and then ask a simple question: does AI actually make this better?” 

She brings the same reasoning to the female-led startups she mentors in the Middle East, and to emerging ventures she has evaluated as a judge at Techstars Startup Weekend Women 2026, which brought together more than 1000 participants from over 25 countries. 

Thus, Enterprise AI will make it easier to deploy intelligence at scale, but that will only raise the bar for proving its value. The companies that benefit most will not necessarily be those deploying the most models, but those starting with the right business decisions, defining the signals that can improve them, and building intelligence into the product from the beginning. For Aghaei, that is the difference between deploying AI and turning it into an enduring business advantage.