Enterprise AI is frequently rolled out faster than it can generate tangible business results. Gartner projects that about 40% of corporate applications will use AI agents by the close of 2026. Concurrently, McKinsey found that a mere 39% of organizations notice a positive financial impact stemming from their AI usage.
Why does enterprise AI so frequently fail to live up to expectations following deployment? What factors should product teams weigh prior to scaling? And how can businesses construct systems that earn genuine trust? To address these inquiries, we spoke with Niki Aghaei. Serving as a Staff Product Manager at one of the globe’s largest retailers, she directs fulfillment products across the US, Canada, Mexico, and Chile, which encompasses forecasting tools and inventory decision systems. Her distinct methodology was forged through positions held at a global digital agency, a leading consumer technology company, a multinational retailer, and a Big Four consulting firm. Recognized as Best E-Commerce Product Manager of the Year at The ECDMA Global Awards 2026, Aghaei also evaluates entries for Techstars Startup Weekend Women 2026 and acts as a mentor to female-led startups throughout the Middle East.
Large-scale logistics operations continuously process incoming data regarding consumer demand and product availability across multiple sites. Niki concentrates on making sure systems can process this data swiftly and apply it to daily inventory management, thereby narrowing the gap between demand shifts and corporate response. This methodology serves as the foundation for the long-term development strategy governing international logistics services at her current employer.
“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.”
In both Canada and Mexico, Aghaei applied this framework to inventory assortment and forecasting tools, guiding their progression from initial research to pilot launch. These tools were estimated to boost internal team efficiency by 20%.
The core concept of a “built-in” approach originated from Niki Aghaei’s personal background.
She observed that product roadmaps frequently deviate from how companies actually function. Even prior to the current surge in AI, while at the Big Four consultancy, Niki assisted in deploying an eight-figure marketing technology platform. That undertaking taught her that planning requirements rarely survive contact with an active organization, prompting her to treat business architecture as a core component of the product itself and to verify organizational readiness prior to any rollout.
This insight proved equally beneficial when Niki managed digital product development at a major U.S. bank. Her group utilized customer behavior data to refine the user experience, resulting in a 20% increase in conversion rates and cementing the validation of product decisions against real-world data as 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.”
Managing her own enterprise introduced another dimension: helping companies break into Middle Eastern markets where historical data and consumer benchmarks were often scarce. Aghaei integrated measurement directly into the overarching strategy, ensuring that actionable signals accumulated organically as products entered the market.
She subsequently contributed to the launch of a new generation of high-volume consumer laptops, aligning engineers, operations specialists, and marketers. This undertaking solidified her conviction that analytics must be woven into the product during the developmental phase.
Niki Aghaei begins by establishing the specific decision a product is intended to enhance. Only after clearly defining the problem and its measurement criteria does the team pinpoint the relevant supportive data and choose an appropriate model.
Her evaluation work with Techstars Startup Weekend consistently highlights the exact same trend from a different perspective: “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 applies this identical philosophy to the female-led startups she advises in the Middle East, as well as to the emerging ventures she reviewed as a judge for Techstars Startup Weekend Women 2026, an event drawing over 1,000 participants spanning more than 25 countries.
Ultimately, while enterprise AI will simplify the deployment of intelligence at scale, it will simultaneously raise the standard for demonstrating value. The most successful companies will not necessarily be those fielding the highest volume of models, but rather those that start with appropriate business decisions, outline the metrics capable of improving them, and embed intelligence into the product right from the start. For Aghaei, that marks the difference between merely deploying AI and converting it into a lasting business advantage.
