AI

How agentic AI is powering operational agility in retail

Looking ahead, agentic AI will power a new wave of adaptive retail where D2C, social and just-in-time commerce converge. These models close the gap between discovery and delivery, enabling retailers to anticipate demand and respond promptly.

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January 6, 2026 by Padmanabhan Venkatesan — Senior Vice President - Consumer Tech, Persistent

It was the weekend before a long holiday season when a leading fashion retailer spotted an unusual pattern in sales data. Online searches for a winter jacket were surging, but store sales remained flat. Social chatter soon revealed an influencer had featured the product, sparking digital buzz. Within minutes, the retailer's AI system recalibrated prices, moved stock from slower outlets and updated promotions.

This example reflects a challenge every retailer faces in bridging the gap between online discovery and in-store demand. Success today depends on reacting to fast-changing signals across search, social and in-store behavior, keeping pricing and inventory in sync. Achieving that level of agility requires systems to sense, reason and act in real-time.

That's where agentic AI comes in. By orchestrating multiple specialized agents, each designed to analyze data, predict demand and coordinate decisions, it helps retailers move from reactive automation to proactive intelligence. McKinsey estimates agentic commerce will generate $3-5 trillion in global value by 2030, as nearly half of consumers who have tried AI-powered search already prefer it.

From automation to agentic intelligence

Traditional automation has always been about efficiency, running reports, adjusting prices and scheduling reorders based on static triggers. But those workflows are often reactive and fragmented. Each role - pricing analyst, category lead, marketing manager, works in isolation, slowing down decisions that should happen in real time.

Agentic AI simulates a network of domain specialists, such as data fetch, pricing and customer segmentation agents and orchestrates them toward a shared goal: optimizing profitability and customer experience.

Unlike traditional systems that simply execute rules, agentic systems can reason across multiple signals and trade-offs. For example, they might process data from an early winter forecast or an upcoming regional holiday and coordinate a set of actions automatically: pricing agents adjust markdowns, while replenishment agents rebalance inventory across stores. What once took weeks of manual coordination can now happen within minutes.

This orchestration becomes tangible when seen in action. In a retail pilot, the workflow analyzed live demand patterns, social media trends, competitor pricing, inventory levels and customer segments to autonomously optimize prices and promotions across online and in-store channels. By continuously aligning these insights, the system generated pricing recommendations that could potentially boost sales by around 15% in simulation, illustrating how agentic systems turn intelligence into business outcomes.

Anticipating demand in real time

One of the most powerful uses of Agentic AI lies in smart inventory and replenishment. Traditionally, planning relied on historical data and manual forecasts, but real-world conditions such as festivals, social trends, logistics delays, rarely match historical patterns.

Agentic AI tracks real-time signals across stores and warehouses to identify Stock Keeping Unit (SKUs) running low or moving slower than expected. It initiates programmatic procurement by evaluating supplier contracts, negotiating landed prices and triggering replenishment. When restock velocity dropped at a regional hub, the system detected the slowdown and rerouted inventory from another location, keeping shelves stocked without human intervention.

Using these insights, it continuously balances inventory across the network, creating an always-available model that minimizes stockouts, lowers carrying costs and strengthens supply chain resilience.

Empowering teams to elevate customer experience

Every retailer knows the dilemma between operational precision and customer experience, a balance hard to maintain in fast-moving environments. Agentic AI shifts the dynamic by handling repetitive, data-heavy tasks like pricing, promotions and procurement. It allows associates to focus on higher-value work such as shopper engagement, merchandising and personalized service. Another application is dynamic bundling, which enables proactive recommendations rather than reactive discounts. Traditionally, bundles appear when a shopper searches for related products. Agentic AI reverses this logic; it identifies intent signals from social or conversational channels and delivers the right bundles before customers even begin their search.

Building explainability and responsible intelligence

As autonomous systems take on greater decision-making, explainability and governance become essential. Pricing and promotion decisions must be transparent, consistent and compliant.

A human-in-the-loop approach remains key. While AI recommends actions, final validation rests with category leads. Every decision is logged and traceable from how the system reached its conclusion, what data it used and which factors influenced it. This ensures accountability and builds trust in AI systems. Adopting responsible practices will determine which retailers ultimately win consumer confidence.

Redefining agility across the retail value chain

The next phase of transformation will come from Agent Commerce Protocols, connecting conversational, social and quick-commerce platforms into a unified ecosystem, blurring the lines between engagement and transaction.

We're entering an era where search-based commerce gives way to conversational commerce. Instead of typing "formal shoes," shoppers will describe their context: "I'm in sales and travel frequently." Agentic systems will interpret intent and curate solutions combining products, prices and availability in real time.

According to IDC's FutureScape: Worldwide Retail 2025 Predictions, by 2026, 70% of retailers will implement AI-driven loyalty apps, improving contextualized offers by 40% and boosting customer engagement to drive up to a 25% increase in retention rates. Agentic fulfillment agents now coordinate across warehouses to minimize split shipments, optimize carriers and speed up deliveries. As boundaries blur between manufacturer, retailer and consumer, agility and resilience become intrinsic.

A human-machine collaboration shaping the future of retail

Leading retailers are beginning to establish Agentic Centers of Excellence to unify data and decision workflows across supply chain, inventory, pricing, personalization and delivery. These hubs drive agentic-led transformation across retail operations, embedding intelligence into everyday processes and enabling real-time agility at scale. Retailers that pair this intelligence with human insight will define the next era of retail.

About Padmanabhan Venkatesan

Padmanabhan (Paddy) Venkatesan is Senior Vice President, leading the Global Consumer Tech vertical at Persistent, enabling AI-driven Digital Product Engineering for its clients across Retail, CPG, Travel and Logistics. He has led and managed several large industry verticals and P&Ls across Communications, Media, Hi-tech, Semiconductor, Retail and CPG, delivering digital transformation programs that enable product-to-platform evolution, SaaS-based business models and enterprise modernization. Paddy is a thought leader on various technology topics across data, machine learning, AI and cloud

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