Artificial intelligence has already changed how retailers create product descriptions, write marketing content, analyse customer feedback, and answer shopper queries.

But the next phase of retail AI will go beyond generating information.

It will take action.

This transition, from generative AI to agentic commerce, could fundamentally change how customers discover products, compare options, complete purchases, track deliveries, and interact with retailers after a sale.

For retailers, the shift is not simply about adding another chatbot to their website. It requires connected product information, accurate inventory, secure payments, reliable order management, and integrated POS software capable of sharing real-time business data across channels.

What Is Generative AI in Retail?

Generative AI creates new content based on prompts, existing data, and learned patterns.

Retailers currently use generative AI to:

• Write product descriptions
• Create marketing campaigns
• Generate social media content
• Summarise customer reviews
• Answer frequently asked questions
• Recommend products
• Assist employees with reporting and analysis

A generative AI assistant can tell a customer which running shoes may be suitable for daily training. However, it will generally wait for the customer to visit the website, select a size, enter an address, choose a payment method, and complete the order.

This is where agentic AI introduces a significant change.

What Is Agentic Commerce?

Agentic commerce uses AI agents that can work towards a defined goal with limited human supervision. Instead of only providing information, these agents can plan tasks, evaluate different options, make decisions, and perform authorised actions.

A customer could instruct an AI shopping agent:

“Find a pair of black running shoes in my size, under ₹5,000, from a trusted brand, and deliver them before Friday.”

The agent could potentially:

1. Search different retailers
2. Compare prices, reviews, delivery dates, and return policies
3. Check whether the required size is available
4. Apply eligible loyalty benefits
5. Select the best product
6. Complete the purchase after receiving the necessary authorisation
7. Track the delivery
8. Assist with a return or exchange when required

Agentic commerce therefore moves AI from being a shopping assistant to becoming a potential shopping participant.

Generative AI vs Agentic Commerce

The primary difference is the ability to act.

Generative AI	Agentic Commerce
Creates content and responses	Performs goal-oriented actions
Recommends products	Searches, compares, and may purchase products
Responds to individual prompts	Manages multi-step workflows
Requires regular human input	Operates with defined permissions
Supports a shopper’s decision	May execute the shopper’s decision
Works mainly through conversation	Connects with catalogues, payments, inventory, and fulfilment systems

Generative AI may help a customer decide what to purchase. Agentic commerce can help complete the purchasing journey.

Why Agentic Commerce Matters for Retailers

1. Product discovery is moving beyond traditional search

For years, retailers have optimised websites for search engines and marketplace algorithms. They may now also need to optimise their product information for AI agents.

Instead of searching for “best formal shoes,” customers may provide detailed instructions such as:

“Find comfortable brown formal shoes suitable for standing eight hours a day, available in size nine, with easy returns.”

An AI agent will compare products based on structured attributes, availability, delivery promises, pricing, policies, reviews, and relevance.

Retailers with incomplete product titles, inconsistent attributes, outdated prices, or unclear return policies may be excluded from the agent’s recommendations—even when they offer a suitable product.

2. Product data becomes a competitive advantage

AI agents need clean and structured information to understand what a retailer sells.

Retailers should maintain accurate data for:

• Product names and categories
• Sizes, colours, materials, and variants
• Compatibility and usage information
• Prices, discounts, and taxes
• Real-time stock availability
• Store locations
• Delivery timelines
• Warranty details
• Exchange and return policies
• Customer ratings and reviews

Google’s Universal Commerce Protocol, for example, is being developed to let shopping agents retrieve real-time product information such as pricing and inventory. It also supports identity linking so customers can receive relevant loyalty benefits during agent-assisted shopping.

This means product data will no longer serve only websites, marketplaces, or store employees. It must also be understandable and accessible to machines.

3. Inventory accuracy becomes essential

An AI agent may make a purchasing decision within seconds. If the retailer’s system shows that an item is available when it is actually out of stock, the transaction may fail.

That failure can damage customer trust and reduce the likelihood of the retailer being selected in future agent-driven searches.

Retailers need inventory information that remains synchronised across:

• Physical stores
• Warehouses
• Ecommerce websites
• Online marketplaces
• Social commerce channels
• Mobile applications
• AI-powered shopping platforms

This is where connected inventory management and reliable retail POS software become important. A modern POS system should update stock whenever a product is sold, returned, transferred, received, or adjusted.

Without real-time inventory visibility, agentic commerce cannot work effectively.

4. The checkout may no longer happen on the retailer’s website

Traditionally, retailers invest heavily in bringing shoppers to their websites or applications.

In agentic commerce, some customers may discover and purchase products without following the conventional website journey. The AI interface could become the primary shopping environment, while the retailer’s systems process the product, payment, order, and fulfilment information behind the scenes.

OpenAI and Stripe developed the Agentic Commerce Protocol as an open standard for programmatic transactions between buyers, AI agents, and businesses. The merchant continues to manage its existing systems, while the protocol helps connect the agent-led experience with checkout and payment processing.

Retailers must therefore prepare for a future in which their storefront is not limited to a physical shop, website, marketplace page, or mobile app.

Their products may also need to be accessible through AI agents.

5. Payments will require new forms of trust

Allowing an AI agent to initiate a payment creates important questions:

• Did the customer authorise the purchase?
• Is the agent legitimate?
• What spending limits apply?
• Which merchant can receive the payment?
• Can the agent change the order value?
• Who handles a disputed transaction?
• How will fraudulent agents be identified?

Payment networks and technology providers are developing systems to address these concerns. Visa Intelligent Commerce is designed to verify that an authorised agent can act on a consumer’s behalf, while Mastercard Agent Pay uses agent-specific technologies to support trusted AI-initiated transactions.

Retailers should not treat agentic payments like ordinary bot traffic. They will need systems that can differentiate authorised shopping agents from malicious automation.

6. Loyalty programmes must work across AI-led journeys

Customer loyalty remains valuable even when an AI agent manages the transaction.

An agent may consider:

• Available loyalty points
• Membership benefits
• Personalised discounts
• Free delivery eligibility
• Previous purchases
• Preferred brands
• Return history
• Subscription status

If loyalty information remains disconnected from ecommerce, store billing, or customer profiles, the agent may not recognise the retailer’s complete value proposition.

Connecting loyalty and CRM data with POS software can help retailers provide consistent benefits whether customers buy in a store, through a website, on a marketplace, or through an AI-powered platform.

7. Store operations will also become more agentic

Agentic commerce is not limited to customer-facing shopping agents.

Retailers may eventually use business agents to:

• Monitor stock levels
• Recommend purchase orders
• Identify slow-moving products
• Adjust promotions within approved limits
• Forecast demand
• Detect unusual sales activity
• Create staff schedules
• Respond to common support requests
• Reorder frequently sold products
• Generate store performance summaries

For example, an inventory agent could identify that a fast-selling product is likely to run out, check supplier lead times, prepare a purchase order, and send it to a manager for approval.

However, these actions depend on access to accurate sales and inventory data. Retailers using disconnected billing, inventory, ecommerce, loyalty, and reporting tools may struggle to implement useful AI agents.

The Role of POS Software in Agentic Commerce

A POS system is often viewed primarily as a billing tool. In an agentic retail environment, it becomes part of the retailer’s central operational data infrastructure.

Modern POS software can connect information related to:

• Product catalogues
• Prices and taxes
• Inventory availability
• Customer profiles
• Loyalty points
• Sales transactions
• Returns and exchanges
• Store locations
• Promotions
• Payments
• Employee activity
• Business reports

When this information is accurate and connected, AI agents can make better recommendations and perform safer actions.

For instance, an AI shopping agent cannot promise same-day pickup unless it can verify that the selected item is available at the customer’s preferred store. Similarly, a business agent cannot recommend restocking accurately without reliable sales and inventory data from the POS system.

Retailers evaluating retail POS software should therefore look beyond billing speed. They should consider whether the platform supports integrations, APIs, centralised product data, multi-store inventory, CRM, loyalty, real-time reporting, and omnichannel operations.

What Retailers Should Do Now

Retailers do not need to automate their entire business immediately. They should first build the right operational foundation.

Clean and structure the product catalogue

Standardise product titles, descriptions, categories, variants, sizes, colours, images, identifiers, and attributes.

Avoid storing important information only inside unstructured descriptions or images.

Improve real-time inventory visibility

Ensure stock levels update across stores, warehouses, ecommerce channels, and marketplaces.

Investigate frequent stock mismatches and establish clear processes for returns, transfers, damaged items, and purchase receipts.

Connect online and offline systems

Integrate ecommerce, payments, CRM, loyalty, inventory, and POS software so that every channel uses consistent business information.

Make policies machine-readable

Keep shipping, returns, warranties, cancellations, and exchange policies clear, structured, and updated. AI agents may use these policies when comparing retailers.

Prepare APIs and integrations

Retail systems should be capable of securely sharing authorised product, order, inventory, loyalty, and payment information with external platforms.

Retailers should review whether their current technology provider supports APIs and third-party integrations.

Establish clear approval rules

Define which decisions an AI agent can make independently and which require human approval.

Examples might include:

• Maximum transaction value
• Approved suppliers
• Permitted discount range
• Refund limits
• Reorder quantities
• Customer data access
• Payment permissions

Strengthen security and governance

Agentic systems introduce risks involving data privacy, incorrect actions, unauthorised payments, fraudulent agents, biased recommendations, and manipulated instructions.

Retailers should maintain activity logs, permission controls, transaction limits, human review mechanisms, and clear accountability.

Begin with controlled use cases

Start with low-risk, measurable applications such as:

• Product discovery
• Customer support
• Stock alerts
• Report generation
• Purchase-order recommendations
• Appointment assistance
• Delivery updates

Expand autonomy only after the system demonstrates reliable performance.

Will Agentic Commerce Replace Human Retail Experiences?

Agentic commerce will automate parts of the buying journey, but it will not eliminate the importance of human interaction.

Customers may still prefer employees for:

• High-value purchases
• Complex products
• Personal styling
• Product demonstrations
• Negotiations
• Sensitive complaints
• Special orders
• Expert consultation

The most successful retailers will combine AI-driven convenience with meaningful human service.

AI agents can manage repetitive research, comparisons, and routine transactions, while employees focus on relationships, expertise, problem-solving, and memorable in-store experiences.

The Future of Retail Is Connected, Not Just Automated

The biggest lesson for retailers is that agentic commerce is not simply an AI feature.

It is a connected-commerce model.

An AI agent is only as useful as the systems and information it can access. Poor product data, inaccurate inventory, disconnected loyalty programmes, unclear policies, and outdated billing systems will limit what retailers can achieve.

Retailers that invest in integrated inventory management, ecommerce connectivity, customer data, secure payments, and scalable POS software will be better positioned for this transition.

Generative AI helped businesses create and communicate faster.

Agentic commerce will help them decide and act faster.

The retailers that prepare now will not only become easier for customers to find. They will also become easier for the next generation of intelligent shopping agents to understand, evaluate, and choose.