For decades, retailers have been forced to choose between building custom software (read: expensive and slow to implement) or buying rigid, off-the-shelf legacy suites (bloated, rigid, and a poor fit for their business-specific workflows). When the trade-offs didn't make sense, they ended up building in Excel.
James Townsend, CEO, Pattern. Image supplied
The great news is that AI has shattered this dichotomy.
With AI-accelerated engineering and deep domain expertise, we can now configure enterprise-grade platforms to a retailer's exact specifications in days, not months. We can map complex data schemas and build custom adapters at speeds that were previously impossible.
The idea that an implementation must take 18 months is a relic of the pre-AI era. In weeks, we should expect to see working tests of systems tailored to the business's unique requirements.
But, despite this technological leap, the operational reality inside many retailers remains stubbornly stuck in the past.
In fashion retail, the most dangerous place to be on a Monday morning is often the boardroom. Across the industry, from high street giants to luxury houses, the same ritual plays out every week.
It is the ‘Monday Trade Meeting’, a high-stakes gathering intended to steer the ship, but which frequently devolves into a forensic audit of masses of data points.
The 'head of buying' and 'head of planning' have both spent their weekend wrestling with the industry’s most ubiquitous and destructive tool: the spreadsheet. They are exhausted, their teams are burnt out, and despite being awash in numbers, no one has a single source of truth.
We are living through a paradox in modern retail: we are data-rich, but insight-poor. While manufacturing and logistics generate petabytes of data annually, the connective tissue holding these complex systems together is often a brittle, high-risk layer of Excel sheets.
This reliance on 1980s technology to manage 2020s complexity is not just an operational nuance; it is a multi-trillion-dollar blind spot that is costing retailers their competitive edge.
The era of decision distress
The pressure on supply chain and merchandising leaders has never been higher. Recent data indicate that business leaders are making 10 times as many decisions every day as they were a decade ago.
Yet, visibility into the consequences of those decisions has diminished due to data overload. It is no surprise that 85% of leaders report suffering from "decision distress".
In fashion, this distress manifests as the ‘merchandise planning and buying cycle’, a relentless, repeating loop of strategic planning, purchasing, and sourcing that varies by season and territory.
To navigate this, teams must marry item-level performance with open-to-buy budgets, supplier constraints, and emerging trend analysis.
Legacy enterprise resource planning (ERP) systems are notoriously rigid, failing to provide the agility required for this synthesis. Consequently, teams build “spreadsheet empires”, complex, bespoke models that only they understand. When that planner leaves, they take the "manual" for the company’s brain with them, creating massive key-person risk.
The cost of complexity
The financial toll of this inefficiency is staggering. According to Accenture, businesses have lost out on $1.6 trillion in revenue opportunities due to supply chain disruptions over the past two years alone. Much of this wastage in fashion retail manifests as deep markdowns and unsold stock piling up in landfills.
This huge problem stems from an inability to accurately and swiftly align demand with supply.
When your "source of truth" is a static grid that takes three days to update, you are reactive by definition. You are looking at a snapshot of the past rather than a forecast of the future.
The spreadsheet cannot warn you that a specific colourway is trending down in a specific region; it can only tell you, weeks later, that you bought too much of it.
Enter the age of Agentic AI
This is where Artificial Intelligence, specifically ‘agentic’ AI, marks a definitive break from the past. We are not talking about slightly smarter dashboards or generative text. We are talking about agents that can act as intelligent collaborators, freeing professionals in fashion retail to do what they do best.
Imagine ‘Better Mondays’. Instead of spending the weekend compiling reports, the planning team at big fashion retailers arrive at 8am to find that their agent has already triaged the week’s trading.
It has identified the top five margin risks and the top three replenishment opportunities. It hasn't just presented data; it has synthesised conclusions.
This shifts the merchant's role from data entry clerk to strategic decision-maker. It eliminates the "analysis paralysis" that plagues modern retail. By automating the drudgery of data cleaning and integration, AI frees human teams to do what they do best: apply judgment, creativity, and nuance.
The verdict for fashion retailers
The spreadsheet was a necessary bridge when data was siloed, and solutions were too generic to solve the real business problem. Today, it is a crumbling piece of infrastructure that cannot support the weight of modern retail.
The winners of the next decade will be those who recognise that spreadsheets are not a safety net but a straitjacket. By embracing AI and agentic workflows, fashion retailers can finally move from reactive survival to predictive excellence, turning their data from a burden into their greatest asset.
