Today, we’re diving into the transformative world of retail planning with Zainab Hussain, an esteemed e-commerce strategist whose deep expertise in customer engagement and operations management has helped countless retailers navigate the complexities of modern inventory challenges. With a staggering 2.5 to 5 billion excess items produced in 2023, costing up to $140 billion in lost sales, and the rise of ultra-fast fashion compressing speed-to-market to just 15 days, the stakes for accurate planning have never been higher. In this conversation, Zainab unpacks the critical shifts in retail technology, exploring how AI-driven solutions are empowering planners, the impact of upcoming regulations like the EU’s 2026 ban on destroying unsold goods, and the future of merchandising as a strategic growth driver. Her insights offer a compelling glimpse into how technology and human expertise can align to tackle overstock, optimize forecasts, and redefine retail operations for a fast-paced, data-driven era.
How did you first recognize the massive issue of excess inventory in retail, and what inspired you to address this gap with innovative planning solutions?
I’ve been in the retail space long enough to see firsthand how excess inventory can cripple even the most promising brands. When I learned that 2.5 to 5 billion excess items were produced in just 2023, translating to as much as $140 billion in lost sales, it hit me like a ton of bricks—there had to be a better way. Early in my career, I worked with a mid-sized apparel brand struggling with overstock after every seasonal launch; their warehouse was practically bursting, and the markdowns were eating into their margins. We dug into their processes and realized their planning was reactive, based on outdated spreadsheets and gut instinct, with no real-time visibility. That was the spark—building connected, predictive systems became my mission. I knew technology could bridge that gap, turning planning from a back-office chore into a strategic advantage, and I’ve been obsessed with refining that approach ever since.
With ultra-fast fashion brands slashing speed-to-market to just 15 days, how do modern planning tools adapt to such rapid cycles, and what’s an example of navigating this pace successfully?
Ultra-fast fashion’s 15-day speed-to-market has turned planning into a high-wire act, where a single misstep can mean massive losses or missed opportunities. Modern tools adapt by leveraging real-time data and AI to make instant adjustments—think of it as having a co-pilot who’s always recalculating the route as conditions change. I recall working with a trendy online retailer who needed to pivot their inventory forecasts mid-cycle when a viral social media trend spiked demand for a specific style overnight. We used a cloud-based platform to pull in sales data as it happened, adjusting purchase orders on the fly to avoid stock-outs while preventing overbuying. The biggest challenge was convincing their team to trust the automated insights over their instincts, but once they saw the numbers—monthly profit losses from stock-outs dropping by nearly 20%—they were all in. It felt like we’d turned chaos into a competitive edge, and watching their relief was incredibly rewarding.
AI is revolutionizing retail planning by handling complex tasks like data cleansing and real-time forecasting. How do you build trust in these AI-driven recommendations among planners, and can you share a moment where AI made a game-changing call?
Building trust in AI starts with transparency and collaboration—planners need to see the ‘why’ behind every recommendation, not just the ‘what.’ We design systems to show confidence scores and detailed reasoning, so users aren’t left guessing whether to act. I remember a case with a multi-brand retailer where our AI flagged a looming stock-out on a best-selling product line during a peak holiday season. The planner initially hesitated, worried about over-ordering, but the system broke down how past demand patterns and current sales velocity pointed to a shortage, with a high confidence score. When they followed through and reallocated inventory, they avoided a 20% monthly profit dip that stock-outs typically caused. Seeing their skepticism turn to relief as customers kept buying without interruption was a powerful moment—it reinforced that AI isn’t here to replace judgment, but to sharpen it with data they couldn’t crunch alone.
With 75% of fashion executives now prioritizing data-driven tools, how do you see platforms differentiating themselves in this crowded tech space, and what’s a standout success story of quick impact for a brand?
In a crowded tech space, differentiation comes down to usability, speed to value, and integration—tools need to feel intuitive, deliver fast results, and connect every piece of the planning puzzle. I think what sets the best platforms apart is a focus on empowering users rather than overwhelming them with complexity. Take the example of a women’s apparel brand with a catalog-based model I worked with—they were stuck planning launch-by-launch with no visibility into ongoing customer demand. By implementing a unified platform that gave them a full inventory view, from seasonal to clearance stock, they slashed buy cycles by 30%, boosted inventory turnover by 5%, and saved their team 10 hours a month on manual tasks. The key was a user-friendly interface with visuals and collaboration tools, making adoption seamless. Watching their team shift from frustration to excitement as they saw markdowns drop was like witnessing a weight lift off their shoulders.
The EU’s 2026 ban on destroying unsold products is pushing retailers toward proactive planning. How are you helping brands prepare for this shift, and what’s an example of a client rethinking their approach?
The EU’s 2026 ban is a game-changer—it’s no longer just about economics; it’s about compliance. We’re helping brands move from reactive markdown strategies to preseason forecasting, ensuring they get inventory levels right from the start. I worked with a European retailer who was nervous about the regulation’s impact on their high-margin lines, where overstock was previously absorbed by destruction or heavy discounts. We guided them to overhaul their preseason planning using predictive tools to align inventory with real demand trends, cutting excess by focusing on core and carryover stock. They reduced overproduction by nearly 25% in their first adjusted cycle, which not only prepped them for compliance but also freed up cash flow. Seeing their team’s stress turn into confidence as they realized they could plan smarter, not harder, was incredibly fulfilling.
Your vision of ‘full-stack merchants’ empowered by AI to juggle multiple roles sounds exciting. How do you imagine this reshaping retail teams, and what might a planner’s daily routine look like in this future?
The idea of ‘full-stack merchants’ is about breaking down silos—imagine planners who can oversee financials, product selection, and vendor coordination all in one flow, thanks to AI handling the grunt work. Over the next few years, I see retail teams becoming leaner but more impactful, with fewer people doing more through technology. Picture a planner’s day: instead of drowning in spreadsheets, they start by chatting with a conversational interface, asking, ‘What’s my forecast for this line?’ or ‘How do tariffs impact margins if I add 10 styles?’ AI agents cleanse data, model scenarios, and draft reports in minutes, so the planner focuses on strategy—deciding which trends to chase or where to allocate budget. Their role evolves from number-cruncher to orchestrator, blending creativity and analytics. It’s like giving them a superpower; I can already sense the excitement this freedom will bring to teams who’ve felt bogged down by manual tasks for too long.
Conversational interfaces in planning tools are changing how planners interact with data. How did this intuitive approach come about, and can you share a memorable first-time user experience with it?
Conversational interfaces emerged from a simple realization: planners shouldn’t need to be data scientists to get insights. We wanted to mimic how people naturally think and ask questions, turning rigid tools into something as easy as texting a colleague. I’ll never forget the first time a planner at a mid-sized retailer tried our system—she sat down, typed, ‘What if I add 10 more styles to this collection?’ and got a detailed breakdown of inventory and margin impacts in seconds. Her jaw dropped; she said it felt like having a brilliant assistant who spoke her language, not tech jargon, and it cut hours off her usual process. That feedback—her genuine awe and the way she immediately started experimenting with more ‘what-if’ questions—pushed us to refine the interface further. It was a reminder of how much joy can come from making complex work feel effortless.
Automated scenario planning for variables like tariffs or store expansions is a powerful capability. How do you ensure the accuracy of these simulations, and can you walk us through a specific scenario a client explored?
Accuracy in scenario planning hinges on rigorous testing and transparency—we validate algorithms against historical sales data and expose confidence metrics so users know how reliable the outputs are. We also co-develop with clients to ground simulations in real-world variables, ensuring they’re not just theoretical. One client, a growing retailer planning a store expansion, used our tool to model how opening five new locations would impact inventory needs and sales projections. The simulation factored in regional demand trends, logistics costs, and even potential economic shifts, showing they’d need a 15% inventory buffer to avoid stock-outs. They applied this by adjusting their distribution strategy upfront, which saved them from a costly misstep post-launch, ultimately boosting new store sales by 10% above forecast. Watching their team review the insights in a boardroom, nodding with relief as they dodged a bullet, underscored how these tools turn uncertainty into actionable clarity.
Looking ahead, what is your forecast for the future of retail planning as AI and regulatory pressures continue to evolve?
I believe retail planning is on the cusp of a profound transformation, driven by AI and regulations like the EU’s 2026 ban. Over the next five years, I foresee planning becoming entirely proactive—focused on predicting demand with precision before a single item is produced, rather than reacting to excess or shortages. AI will evolve from a tool to a partner, with conversational interfaces and autonomous agents handling 80% of routine tasks, freeing planners to innovate and shape customer experiences. Regulatory pressures will force sustainability into every decision, pushing brands to adopt circular models and zero-waste strategies as core planning principles. We’re also likely to see a talent shift, where planners become strategic visionaries, blending data insights with creative merchandising. My hope is that this synergy of tech and human ingenuity will not just solve today’s pain points, but redefine retail as a more responsive, responsible industry.
