Industry
Retail
Omnichannel CX and Intelligent Innovation – The Future of Retail
HIGHLIGHTS
- The next frontier in checkout will be powered by agentic AI and go.
- beyond convenience and speed, anchored by intelligence, context, and engagement.
- From inspiration to product discovery to making informed choices and checking out, agentic AI is poised to support every step along the shopping journey, across devices and channels, without any friction.
- Retailers who are quick to adopt agentic checkout are likely to improve top line growth as well as loyalty and customer satisfaction scores.
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The journey so far
Checkout in retail has always evolved with technology—from traditional manned counters to barcode scanners, from swiping cards to contactless payments, and from cashier-operated lanes to self-checkouts. Each evolution made checkout faster and more convenient. Now, agentic artificial intelligence (AI) brings the next major leap in this evolution, bringing human-like reasoning into the customer journey. We are entering a new phase in checkout—one that goes beyond convenience and speed, and focuses on intelligence, context, and engagement.
While most existing checkout systems wait for exact inputs, agentic checkout systems can anticipate, recommend and adapt, transforming rule-based workflows into a responsive and collaborative experience. The new checkout will be about understanding the customer’s intent, offering tailored recommendations, resolving exceptions in real time, elevating checkout from a mundane routine to a superlative, personalized experience.
The golden standard
Currently, in most checkout platforms, any disruption freezes the process. The moment the flow is disrupted due to, for instance, barcode not getting scanned or a promotion code not getting applied, the shopper is stuck waiting for the store associate to step in. Agentic AI changes that. If a barcode fails, agentic AI can instantly identify the product using alternative methods like image recognition, stock-matching, or camera-based detection. If a price seems off, it can cross-check against live inventory and promotional data, and address incongruencies before the customer detects it.
It’s not just faster; it’s smarter. If a shopper has added a bottle of wine and gourmet cheese to the cart, agentic AI-powered checkout can suggest a bundled offer. To a parent buying stationery, it can offer a discount on a backpack. It can flag promotions and suggest add-ons and loyalty rewards. Such contextual nudges not only enable rule-based upsells but also help establish loyalty.
This powerful shift is about making the checkout system more independent, orchestrating a set of actions together to complete multi-step processes like a customer journey autonomously.
Agentic AI can anticipate, recommend and adapt, transforming rule-based workflows into a responsive and collaborative experience.
Intelligent checkout in action
Let’s see how agentic AI is transforming checkout. Say Lisa visits a store to shop for a gift and settles for a handbag. As soon as she scans the handbag, the product agent steps in. It checks product availability, current stock level, and pricing. It notices that the handbag is the last item in stock and alerts the stock replenishment agent. While Liza continues shopping, the offer agent scans through active promotions and detects that the purchase qualifies for a discount on gift-wrapping. The customer context agent reviews Lisa’s past purchases and recognizes that she prefers eco-friendly packing. The checkout guide agent nudges Lisa with an offer she can’t resist: ‘This is the last piece in stock. Would you like to add an eco-friendly gift wrapping with a $3 discount?’ Lisa can’t resist the offer. When she is ready to pay, the payment agent checks for loyalty points, preferred payment methods, saved payment options, and redeemable points, and presents them. The checkout agent completes the whole journey, notifies Lisa about a digital receipt, and shares a personalized thank you message.
Throughout this customer journey, multiple agents are working in the background. They are collaborating, exchanging data, making autonomous decisions so that the experience feels seamless and non-invasive. Whether Lisa shops at a store, kiosk, on her mobile, or online, these group of agents, each focused on specific tasks, work together like a relay team to deliver a seamless and personalized experience.
This marks a transformative shift from today’s rule-based, linear systems. In most modern stores, checkout systems still struggle with real-world complexities such as a faulty barcode, overlapping discounts, or policy exceptions, necessitating human intervention. Shoppers must manually enter a promo code, and personalization is minimal. Friction points—like out-of-stock items or unclear return policies—often lead to frustrating interactions between customers and store associates. With its ability to turn contextual insights into meaningful actions independently, agentic AI ushers a new paradigm for checkout.
Pushing the boundaries of unified commerce
Agentic AI elevates unified commerce by infusing intelligence, autonomy, and real-time adaptability. Together, they go beyond system integration—agentic AI acts as a strategic data orchestrator, seamlessly connecting insights across ERP, CRM, supply chain, sales, and social media, transforming unified commerce into an intelligent, responsive, and self-optimizing growth engine. From hyper-personalized product discovery to dynamically adaptive checkout, agentic AI pivots unified commerce from a coordinated framework into a continuously learning ecosystem, unlocking previously untapped intelligence. For example, by leveraging memory layers, contextual frameworks and consent-based identity stitching, it can track interactions across channels and devices and build a profile of needs and intent, and not just behavior, to guide shopper’s decisions. Let’s see how.
Maya signs up for a charity trail hike. Later that evening, she explores the trail route through her generative AI (GenAI)-enabled smart glasses. She checks out the terrain and weather around the scheduled event and plans to buy the right gear for the hike. She instructs the GenAI-assistant to open the shopping app on her phone. By connecting the dots—understanding her intent, the trail route, weather forecast around the hike, her past shopping behavior, and what other hikers have purchased, agentic AI presents Maya a curated set: hiking shoes, waterproof jacket, energy bars, and a smart watch with altitude tracking. Instead of a regular home page, what Maya experiences is a personalized, immersive interface. Maya goes on to interact with the GenAI assistant, expressing her interest in all-weather hiking shoes. The GenAi assistant powered by agentic AI is quick to show product suggestions, user reviews, and a real-time comparison. The UI evolves with each query from Maya. Based on the context, it hides what she doesn’t need, highlights bundled offers, and shows a 3D version of the products. Parallelly, in the background, an enterprise knowledge graph connects product specifications, reviews, sustainability data, delivery times, and weather inputs—to guide Maya’s decisions without overwhelming her. On the checkout page, she is presented with two fulfilment options—next day delivery and a slower delivery that is carbon-neutral. She opts for the green option.
What stands out here is how smooth the journey felt. Maya starts her journey with her voice-enabled smart glasses and transitions to an immersive experience on her shopping app. From inspiration to product discovery to making informed choices and checking out, it all happens across devices, channels and moments, without any friction. That is the power of unified commerce, stitched together by agentic AI.
Challenges
It’s not easy to build what feels effortless. While agentic AI brings a lot of untapped opportunities, it also comes with the following set of challenges:
Agentic AI now help shoppers discover products, compare options, track prices, and even check out in one tap. Users can ask open-ended questions like ‘best black jackets for winters’ and get tailored results. They can digitally try on clothes using their own photo or avatar. Shopping becomes faster, smarter, and more convenient—all inside Google.
This changes the game. Platforms offering these capabilities might start owning more of the customer journey and may become the default storefront pushing retailers downstream. Retailers might lose access to purchase intent and attribution. They could lose control over the data, and they might have to depend on the ecosystem that the platforms with data own. To safeguard against this, retailers must double down on their own experiences. They need to invest in personalization, loyalty, and seamless omnichannel journeys. They should make checkout fast, promotions relevant, and service helpful within their own ecosystem.
And most importantly, retailers must build their own AI-powered touchpoints that make customers feel they are understood.
An agent can misunderstand the customer’s intent and can suggest wrong offers, resolutions, etc. Further, it’s quite possible that one agent fetches outdated pricing information, or an integration failed silently. In such cases, businesses must bring strong observability into their systems where agent’s decisions are logged in, timestamped, and reviewed. This can help dev teams in tracing back and identifying the root cause.
Currently, each agent might make multiple calls to external APIs and large language models. When this solution is scaled across hundreds and thousands of customers, this becomes even more expensive. By using lightweight models for simple tasks and more powerful models for complex reasoning, retailers can optimize budget allocation and investments.
With agents handling sensitive data, retailers must prioritize data sensitization, and ensure systems comply with local regulations.
Despite these challenges, agentic systems are robust and resilient as they learn from past behavior and scenarios and can adapt to new tools and environment, evolving alongside the business. What matters is not just the deployment but continuous training, supervising and improving them just like any valuable member of the team.
Realizing success with agentic checkout
Embracing agentic AI can seem overwhelming, but retailers or businesses do not need to build all the capabilities at once. The modular nature and approach make agentic systems flexible and compelling for retailers. They can always start with low risk and high impact workflows like an automated customer service, which can handle returns, or recommend similar items or alternatives in case of a stockout, reducing customer query time. Once these agents are stable and produce optimal results, more agents can be added overtime such as a discount agent or a customer profiling agent. These agents can be trained in controlled environments and tested in simulations before making them available. They can also check how well they are collaborating with other agents and the team can make changes based on the outputs. Once ready, it can be tested again at a separate counter before a full rollout to avoid any glitches.
Agentic AI has all the capabilities to turn checkout into a meaningful moment, one where small agentic actions add up to superior customer experiences. It is flexible and personal without being invasive. Retailers who will embrace this will see the difference not just in the top line but in loyalty and customer satisfaction scores. And that’s what makes agentic checkout the next chapter in customer experience.
