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Tom Bowman
Lead Partner, Retail Consulting EMEA Region, TCS
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Every day, thousands of choices are made across merchandising, supply chain, pricing, promotions, and marketing. Some decisions are routine, such as a price tweak on a low impact product like ketchup or jam. Others are strategic, such as replacing a supplier that provides a cornerstone product line. Although however strategic or tactical each choice is, no decision lives truly in isolation.
A small price cut may boost sales volume, but it also shifts demand forecasts, stresses distribution networks and reduces margins. A supplier delay in one category may undermine promotional calendars in another. The retail environment is a web of interdependent choices.
A supermarket chain manages thousands of SKUs, each with their own pricing, placement, and promotional decisions. Every week, hundreds of micro-decisions ripple through supply, marketing, and financing systems.
Traditional decision-making approaches were designed for an era of slower change and clearer boundaries between functions. In an interconnected world, ambiguity about who decides what and when leaves, leads to under optimized choices and overextended employees.
TCS and MIT Sloan Management Review research shows that Intelligent Choice Architectures (ICAs) provide a structured solution to mitigate this challenge. ICAs can classify decisions along two axes: impact and complexity.
The role of leadership shifts from making individual decisions to designing the decision environment. They must do so by setting clear objectives, defining constraints, deciding what constitutes success, and establishing accountability through decision rights to address two key questions:
Being responsible for the outcomes of AI requires a rethink of how decision quality is measured. Tracking outcomes such as revenue lift or margin percentage is no longer enough. The health of the decision environment itself must be monitored.
Embedded within the ICA framework, key performance AI indicators (KPAIs), provide this view. They measure framing agility, option innovation rate, speed of feedback loop integration, and decision cycle time. These metrics reveal whether the system is learning and improving or drifting toward misalignment.
Category buyers excel in their domains. Their intuition, honed by years of experience, has shaped product ranges, built supplier relationships, and launched promotions that drive sales. However, by operating in silos, they end up causing inconsistent pricing, ad hoc promotions, and inconsistent timing.
ICAs resolve this by embedding brand values, financial targets, and category roles into the decision framework. Every choice is tested against enterprise strategy before being executed. Guardrails are explicit. Buyers still have the freedom to innovate or experiment, but the system ensures the move does not undermine broader commercial goals. This consistency pays off in the long term.
A unified ICA framework gives procurement teams clarity in negotiations. They enable supply chain partners to plan confidently. They allow promotions to launch reliably across channels.
At the same time, ICAs preserve and elevate human intuition. For example, consider a retail copilot where an analyst can ask for scenarios that “increase margin by 2 percent without losing share.”
Within seconds, the copilot surfaces options that balance pricing tweaks, assortment changes, and supplier negotiations, with quantified trade-offs. Another analyst asks, “Model the impact of dropping SKUs with more than 8 percent waste.” The copilot simulates substitution patterns, supplier reactions, and margin outcomes. What would once require weeks of siloed analysis now happens in minutes.
Walmart uses an AI-powered talent architecture to map associate skills and surface growth opportunities, allowing HR decisions to align with enterprise strategy. Pernod Ricard uses ICAs to accelerate creative testing, expanding campaigns from three concepts to twenty and reducing cycle times from months to weeks.
Retail’s fragmented systems and reliance on structured data slow decisions. By combining the power of generative and predictive AI, ICA let retailers ingest unstructured inputs such as contracts, customer feedback, and store notes, along with structured data such as pricing, weather, and supply ops and turn them into actionable insights.
The operational benefit is twofold: decision-makers get a richer, more current view of reality, and employees are freed from low-value work.
When people no longer spend most of their day cleaning spreadsheets or reconciling reports, they can focus on higher-value tasks such as supplier strategy, product innovation, and customer experience design.
Today, the retail sector’s razor-thin margins leave little room for error. Every delay in a pricing decision, every inconsistency in category strategy, every inefficiency in supply allocation is an erosion. ICAs offer a way to reclaim that margin by rethinking how decisions are made, with what information, and by whom.