Inventory stocking and planning remains a critical pain point for fashion retailers today.
Between 2.5 billion and 5 billion items of excess stock were produced in 2023 alone, representing between $70 billion and $140 billion in lost sales value, according to BoF and McKinsey & Co.’s The State of Fashion 2025 report.
Concurrently, stock-outs — when retailers run out of products or sizes customers want to buy — continue to impact the industry, with inaccurate size purchases resulting in an average 20 percent loss in monthly profits.
Inventory planning has long been treated as a reactive, back-office operation, siloed across merchandising, sourcing, logistics and supply functions. But with ultra-fast-fashion players compressing speed-to-market to as little as 15 days, the stakes for getting planning right and adjusting quickly have escalated dramatically.
Having a competitive edge no longer lies in reacting to demand, but predicting it; while planning once relied on instinct and manual data analysis in spreadsheets, it has since moved to connected systems and now learning systems that understand context and act faster than humans can react.
Seventy-five percent of fashion executives plan to prioritise data-driven planning tools, recognising that technology-enabled transformation is mandatory amid mounting regulatory pressure. The EU’s Ecodesign for Sustainable Products Regulation will prohibit the destruction of unsold products in 2026, making accurate planning not just a competitive advantage, but a compliance imperative.
Cloud-based merchandising platform Toolio is helping retailers meet this moment with AI-powered planning technology — turning the function into a strategic lever for growth.
Toolio harnesses agentic AI — an emerging artificial intelligence tool that can complete multi-step tasks autonomously on a user’s behalf — to improve retail forecasting accuracy. Rather than requiring planners to manually adjust for variables, Toolio’s agentic AI proactively identifies and accounts for out-of-stocks, promotional impacts and other anomalies that traditional planning tools often miss, making real-time adjustments to forecasts throughout the planning process.
The shift towards AI-powered planning reflects a broader transformation underway across retail. From Ralph Lauren’s new AI-powered shopping tool “Ask Ralph” to fashion brands experimenting with AI-generated model advertisements or AI-assisted HR transactions, the technology is reshaping both consumer-facing experiences and behind-the-scenes operations.
Now, BoF sits down with Toolio’s chief executive officer Eytan Daniyalzade to discuss how the company is reimagining retail planning as a growth driver, the role of agentic AI in the future of merchandising, and why the company believes the greatest opportunity lies not in replacing planners, but in empowering them.
Why have traditional planning systems faltered and what fundamental shifts are needed?
Most traditional planning solutions were built in the late ’90s and early 2000s and can’t account for the speed, complexity or agility modern retail demands. Many brands and retailers are still using outdated systems with green-screen interfaces.
After the financial crisis of 2008, a new generation of digital-first brands emerged with completely different planning needs. What’s needed now is a shift toward integrated, flexible and user-friendly platforms that connect all planning functions in real time. We built Toolio to close these critical gaps.
Toolio initially resonated with brands like J.McLaughlin, Tommy John, Lulu + Georgia and Mackage, and that momentum carried us into larger enterprise clients. We have built a modern solution that packages optimisation and intelligence in a way that is genuinely usable for the people doing the work.
How does Toolio’s integrated platform approach differ from traditional systems?
We focus on four things: ease of use, flexibility, time to value and connected planning. Toolio was built with usability at its core, with interface resembling the Excel grids that planners already know, but with images, visualisations, collaboration tools and AI built in. We prioritise explainability, ensuring planners can understand, trust and act on insights.
We have done over 100 implementations in five years. So, instead of customising for each client, we built one highly configurable platform to allow for flexibility. This means we can innovate faster and serve everyone from million-pound retailers to multi-billion-pound enterprises on the same system without costly upgrades or disruptions.
The EU will ban destroying unsold products in 2026. Accurate planning is now a compliance imperative, not merely an economic one.
We deliver value in months, instead of years, and since we’re cloud-native and modular, customers see value fast, driving quick adoption and tangible ROI. Lastly, we integrate the entire planning process on one platform. Most legacy systems only tackle one piece, like allocation or markdowns, but we believe planning works better when everything — from merchant workflows to designer and production processes — lives on the same platform.
Combined, these benefits provide a platform that’s as intuitive as a spreadsheet, but as powerful as an enterprise system.
What are examples of how better planning translates to better margins and business outcomes for a retail brand?
Two examples stand out: first, a women’s apparel brand with catalogue roots. They were planning for each catalogue launch separately, with little inventory visibility beyond that drop. But customers shop continuously, not just during launches. This created leftover inventory between launches and missed sales opportunities.
By giving them a complete view of all their inventory — seasonal, core, carryover, clearance — they could align with real customer demand, have fewer markdowns and minimise the risk of overstock. The shift led to measurable results: buy cycles were reduced by 30 percent, inventory turnover improved by 5 percent, and the team now saves about 10 hours each month on planning tasks.
Another example is a multi-brand retailer that was buying into too many styles for each brand, resulting in too much cash tied up in excess inventory. Our SKU [stock keeping unit] rationalisation tool showed them exactly how many styles to carry in each category and how much inventory to buy for each. They reduced SKU count by over 50 percent while exceeding sales goals. Those kinds of results happen when teams stop working in disconnected tools and start planning more efficiently and profitably.
How are you leveraging agentic AI in these use cases?
We use AI agents for several key tasks. AI cleans historical data so brands are not repeating past mistakes. If you stocked out or only sold through because of heavy promotions, the system adjusts that data to show true underlying demand.
Toolio’s philosophy is that AI should eliminate the chores of planning, not replace planner judgement. Our data cleansing runs continuously, but planners stay in control.
Another aspect is that AI handles the tedious work so planners can focus on strategy. Toolio’s AI generates actionable recommendations across the business and creates reports that normally take analysts hours to compile. It summarises performance and provides specific next steps, like “mark down this product”, “cancel this purchase order” or “pull this shipment forward to meet demand”.
In essence, Toolio’s agentic layer helps planners move faster from “what happened?” to “what should we do about it?”
How does Toolio’s AI augment and empower human decision-making?
Toolio’s philosophy is that AI should eliminate the chores of planning, not replace planner judgement. Our data cleansing runs continuously, but planners stay in control. We highlight exceptions and let users choose what to do.
So, when Toolio makes recommendations, it explains the reasoning and shows confidence scores for our forecasts. When confidence is high, we make that clear so planners don’t waste time there. When confidence is low — maybe a product is hard to predict — we flag it and let planners make the call. It’s fundamentally an exercise in human-computer collaboration.
How are you accounting for early-development errors, particularly when AI is making recommendations that affect millions of pounds in inventory decisions?
We invest heavily in the underlying science. We rigorously test everything against historical sales data to build confidence in our algorithms’ accuracy. Exposing these confidence metrics to end users is essential for building trust.
Just like full-stack developers replaced specialised teams in software, we’ll see full-stack merchants who handle financial planning, product selection and even vendor collaboration. AI enables fewer people to do more.
Beyond internal validation, we co-develop with our 100+ customers throughout the product cycle, from design to prototypes to feedback. We collaborate with leading retail consultancies like Deloitte Digital, Parker Avery, and Columbus Consulting for functional expertise, and partner with Microsoft on technical architecture, go-to-market strategy, and AI education in retail.
How are new regulations making accurate planning a compliance issue?
The EU will ban destroying unsold products in 2026. Accurate planning is now a compliance imperative, not merely an economic one. Historically, if you had high-margin products before, you could absorb forecasting mistakes. Not anymore.
This is also shifting when retailers focus on planning. Historically, the focus was reactive: “I have excess inventory. How do I mark it down?”. Now the focus is moving to preseason forecasting and planning — getting it right upfront is becoming critical. The entire planning cycle is moving faster and becoming more proactive rather than reactive.
Looking ahead, how do you envision the role of retail planners evolving over the next three to five years as AI capabilities advance?
Just like full-stack developers replaced specialised teams in software, we’ll see full-stack merchants who handle financial planning, product selection and even vendor collaboration. AI enables fewer people to do more.
This evolution brings merchandising back to its essence — a single, connected view of the business. Over time, planning became fragmented across systems and teams, each focused on a narrow slice of the process. AI now allows planners to operate holistically again — connecting financial, product and inventory decisions in one continuous flow.
The interface will also change. Instead of spreadsheets and formulae, planning will become conversational. You can ask, “Help me forecast this product,” then follow up with “What if I add 10 more styles?” or “How will tariffs and price increases affect margins?”. It’s like chatting with ChatGPT instead of building Excel models.
Planners will orchestrate AI agents — one for data cleansing, one for forecasting, one for scenario modelling — rather than doing everything manually. They’ll level up, directing agents instead and focusing more on strategy, rather than executing every task manually.
We are especially excited about automated scenario planning. You can already model different scenarios in Toolio — store expansion, tariffs, economic shifts — and see impacts on inventory, sales and margins instantly. Soon, agents will automatically generate and test relevant scenarios, making strategic planning even more dynamic and comprehensive.
Ultimately, AI won’t replace the planner; it will empower them. Those who lean in early will redefine what planning means altogether, evolving into new roles that blend creativity, analytics, and strategy. By pairing human curiosity with technology, planners will uncover entirely new ways to add value to their businesses; not just reacting to the market, but actively shaping retail’s future.
This is a sponsored feature paid for by Toolio as part of a BoF partnership.
