Retail today is no longer shaped by scale alone; it is shaped by speed of decision-making. Customer expectations shift in real time, demand signals fragment across channels, and operational complexity grows with every new touchpoint. In this environment, even advanced analytics and traditional AI struggle to keep pace. Insight without action, or action without context, creates gaps that retailers can no longer afford.
This is where Agentic AI marks a fundamental shift. Rather than responding to predefined rules or static predictions, agentic systems are designed to sense, reason, and act autonomously within dynamic retail environments. They don’t just surface insights—they take outcome-driven actions and continuously learn from real-world signals to adapt their decisions as conditions change.
For retailers, this means moving beyond reactive problem-solving to a model where issues are anticipated, decisions are contextual, and experiences feel intuitive rather than engineered. From inventory and merchandising to pricing and forecasting, Agentic AI enables a more adaptive, self-optimizing retail ecosystem—one built for volatility, personalization, and scale.
Spotting Problems Early On
Agentic AI identifies potential problems before they become roadblocks and translate into lost sales. For example, product unavailability and issues with replenished shelves can lead to customer complaints. To address this, autonomous, agentic systems continuously monitor signals by integrating insights from inventory records, supply chain systems, point-of-sale data, and more. These insights are then used to identify anomalies early on and take corrective actions in real time. As a result, it can trigger the restocking of products that may be selling quickly or products that sell more during certain holidays or seasons.
The focus of Agentic AI is on fixing issues before they impact revenue. Furthermore, AI agents continuously learn in the retail environment from outcomes to improve their decision-making over time.
Improving Product Performance Proactively
An underperforming product doesn’t always indicate poor quality. Common reasons for product failures include pricing issues, poorly targeted ads, insufficient promotion, limited visibility, customer behavior, and seasonality. Agentic AI considers all relevant factors and improves conversions. For example, if a product is viewed frequently by customers but not purchased as much, the reason may be negative customer sentiment or poor digital content. Agentic AI analyses all data, identifies root causes, refines digital content, and optimises promotions to encourage purchases.
Making Smarter Merchandising and Pricing Decisions
In traditional retail environments, pricing decisions were made based on historical sales data. However, with Agentic AI, decision-making is no longer static or rule-based. Frequently changing customer expectations, along with demand volatility, call for intelligent, outcome-driven actions in real time. AI agents may analyse everything from customer preferences, cart composition, and footfall to weather, seasonality, and local events. The insights generated are used to optimize product shelfing, digital storefronts, and product assortment. For instance, if a product category performs well in select locations, the agentic system would suggest pushing products accordingly. On the other hand, a poorly performing product may be replaced or repositioned. Similarly, AI agents may suggest upsell and cross-sell opportunities to increase sales.
Agentic AI is especially useful for context-aware merchandising, enabling brands to personalize product displays and promotions across online and omnichannel channels. AI agents can help build hyper-personalized experiences for each shopper, highlighting relevant products and promotions based on past behavior, intent, and browsing patterns.
Forecasting Sales – Agentic AI helps retail businesses move beyond static predictions through continuous self-learning. This means AI agents continuously monitor shifts in demand and update forecasts accordingly. For example, they may predict demand based on seasonality, inventory levels, weather conditions, and regional trends. A great example would be a spike in demand driven by social media influence.
Note that agentic AI predicts and acts simultaneously. It continuously runs simulations to enable accurate forecasting, while minimizing bias and errors. Additionally, the merchandising, pricing, and supply teams are aligned and operate from a singular view to prevent errors.
How Tech Mahindra Steps in for Retailers
As retailers begin to agentify decision-making across their value chains, the challenge is no longer about adopting AI—it is about operationalizing autonomy responsibly and at scale. This requires more than isolated models or point solutions. It demands an ecosystem where data, intelligence, governance, and change management work in concert.
Tech Mahindra supports this shift through Orion, its Agentic AI platform designed to help enterprises deploy, orchestrate, and scale autonomous agents across complex business processes. Rather than treating agents as standalone tools, Orion enables organizations to embed intelligence directly into workflows—allowing systems to act, adapt, and improve continuously.
Through Orion, retailers can:
- Design and deploy custom AI agents aligned to specific business outcomes, integrating seamlessly with existing technology landscapes
- Establish a unified data foundation, consolidating signals across inventory, supply chain, customer, and operational systems
- Enable responsible autonomy through built-in governance, security, and operational safeguards
- Support enterprise adoption with skill enablement and change programs, ensuring humans and agents evolve together
- By combining deep retail domain expertise with scalable agentic frameworks, Tech Mahindra helps retailers move from experimentation to execution—transforming Agentic AI from a promising concept into a core operational capability.
Visit us and learn how TechM Orion can be a game-changer for retail businesses.
