Back in 2023, AI in the retail market was valued at just over 7 billion dollars. In 2025, that number more than doubled. In 2030? Analysts predict another doubling in valuation, but with the rise of agentic AI, we might be in for an even higher ceiling.
Why do we think agentic ecommerce will be such a transformative force in the world of online retail? Well, if we take a look at the impact AI innovation has had on the industry, it becomes quite obvious that a fully automated ecosystem will be even more efficient.
That said, it’s hard to accurately predict the impact this new era of AI technology will have on the industry. However, what isn’t hard to predict is how these changes will manifest. And that’s exactly what we’ll do today.
What is Agentic Ecommerce?
Agentic ecommerce is a next-generation digital commerce model in which autonomous AI agents act on behalf of either brands or consumers to make decisions, complete complex tasks, and facilitate transactions, while requiring minimal or no human oversight.
These AI agents are goal-oriented, context-aware systems designed to independently plan, execute, and refine purchasing actions. Unlike rule-based automation or generative AI, agentic systems can reason, adapt, and coordinate across multiple systems in real time.
To understand these agentic workflows, it’s helpful to separate the perspective of the brand and the consumer:
| Brand Perspective | Consumer Perspective |
| Agents reduce friction in the purchase journey, leading to higher conversion rates with less drop-off | Agents compare products, filter options, and recommend the best choices so you don’t have to spend time researching |
| Brands can deliver hyper-personalized experiences at scale by integrating with agents that learn individual customer preferences | Routine purchases like groceries, refills, or gifts can be handled automatically, saving time and effort |
| Repeat purchases and subscriptions become more reliable as agents automate reordering and replenishment | Online shopping becomes more personalized since agents learn your preferences and adjust recommendations accordingly |
| Customer support costs decrease when agents handle routine service requests, order tracking, and returns | Agents track prices and deals across platforms, helping you spend less and avoid unnecessary purchases |
| Product discovery improves as agents surface relevant items based on real-time consumer intent, not guesswork | They can anticipate your needs and act before you notice, like reordering items when you’re running low |
| Brands can gather richer first-party data through agent interactions, improving targeting and product development | No need to switch between apps or sites since agents operate across multiple brands and storefronts |
| Lower acquisition costs over time, since agents reduce reliance on paid ads by creating direct, high-intent interactions | After you buy, agents can manage returns, customer service issues, and warranty claims for you |
How Will Agentic Ecommerce Change the Way Brands Operate?
Over the last few years, we’ve seen the progress that ecommerce brands can make by utilizing artificial intelligence and traditional automation. However, what marketing automation platforms did for brands is only a fraction of what an agentic AI system will allow them to do.
Driving Higher Conversion Through Autonomous Optimization
We know that brands already use AI to improve conversions by running automated A/B tests, personalizing site content, and recommending products based on user behavior. Companies like ASOS use sizing assistants and visual search to reduce uncertainty and lift engagement, and others, like Amazon, drive 35% of sales via tailored recommendations.
It’s evident that these AI tools help increase sales, grow email lists, or reduce cart abandonment, but they still rely on people to set them up, review results, and apply the changes. With agentic commerce, that loop gets closed.
Agents won’t just assist—they’ll take over the whole process. Instead of waiting for a marketer to pick the winning version of a landing page or an abandoned cart email, an autonomous agent will test, decide, and switch to it automatically. Over time, agents will learn what converts best for each customer (segment) and apply it across emails, ads, and product pages.
Scaling Hyper-Personalization Without Manual Oversight
AI tools are already great at analyzing customer data to segment audiences and tailor emails, discounts, and product recommendations. However, this process requires ongoing manual setup and fine-tuning by marketing teams. Its biggest problem? It’s time-consuming. You have to analyze the data, update rules, build segments, and adjust messaging as customer behavior changes, potentially causing bottlenecks as your brand grows.
Agentic AI tools promise to solve that issue because they’ll operate continuously and autonomously across massive, complex data sets without human intervention.
They will learn from real-time data streams and adapt instantly to customer needs. This means they can manage thousands of micro-segments, test countless offer variations simultaneously, and optimize personalization across multiple channels without any bottlenecks. The result? Infinitely more precise targeting at scale, faster response to trends, and reduced workload for marketing teams.
Delegating Customer Support to Intelligent Agents
Brands currently use an AI-powered chatbot or an AI assistant to handle common customer support tasks like answering FAQs, tracking orders, or basic troubleshooting. While this automation reduces response times, these systems often struggle with complex or multi-step issues and require human handoffs. This is bound to change.
Intelligent agents will now be able to remember past conversations, learn from interactions, and adapt over time to speed up problem resolution and deliver more accurate, consistent answers, and improve customer experience.
Also, they’ll be able to coordinate with each other behind the scenes. For example, a customer support agent will be able to talk with a shopping agent to get real-time updates.
Automating Inventory and Supply Chain Decisions in Real Time
Target’s Inventory Ledger system, which makes billions of weekly predictions to help reduce stockouts and overstock, offers brilliant insight into how intelligent agents can help with supply chain management.
Target’s Chief Information and Product Officer Prat Vemana said, “Combining traditional software with AI helps us make smarter, faster decisions about inventory management and keep our stores stocked more consistently.” With agentic ecommerce, this can be taken a step further.
One agent can monitor sales velocity, another can check supplier lead times, and another can predict inventory needs and demand fluctuations. These agents can talk to each other directly, making decisions based on live data to reduce overstocking, avoid shortages, or even allow supply chains to respond in real time to actual demand. Over time, this will lead to fewer delays, less waste, and better margins for ecommerce brands, regardless of the product they’re selling.
Implementing Dynamic Pricing Through Agent-Led Market Analysis
Dynamic pricing has been a thing in ecommerce for years. For instance, Amazon and Walmart adjust prices multiple times per day to stay competitive, but at the moment, most of these efforts are reserved for major players in the industry.
Moving forward, anyone will be able to have multiple AI agents work together to optimize dynamic pricing. One intelligent agent will track competitor prices, another one will sense seasonal trends, and a third will evaluate inventory levels in order to find the right price at any given moment. To have it all work seamlessly, an orchestration agent ties it all together.
That setup promises to beat current rule-based algorithms because decisions are continuous, synchronized, and context-aware. Agents anticipate Black Friday surges, coordinate pricing adjustments with inventory and promotions, and learn which changes drive conversions. Over time, this means smarter pricing, improved margins, and a fully automated dynamic pricing system.
Running Always-On, Self-Optimizing Marketing Campaigns
Most brands still run campaigns in cycles. Teams build a plan, push it out across channels, watch performance, and then adjust based on data. Now, this undoubtedly works, and AI tools are already a big part of it, but with AI agents coming along, we might see marketing campaigns that perform even better.
In the near future, we might have one agent manage email campaigns, another handle paid ads, and another track performance across all of it. Based on the data they pull, whether that’s sales, card abandonment, click and open rates, or even current market trends, agents will be able to adjust the marketing strategy in real time in order to drive more sales.
Enabling Agentic Transactions
Whether we’re talking about business to agent, agent to consumer, or agent to agent to consumer transactions, brands will need to update their transaction infrastructure to ensure smooth operations.
Thankfully, major companies in the payment processing industry have already launched their agentic transaction solutions, so if you want to adopt the new tech early, you can pick between:
How Can Brands Prepare for Agentic Ecommerce?
The harsh truth is, not all ecommerce brands are ready to take the next step. While agents promise to simplify daily operations and improve your sales, setting it all up will require some work beforehand, even with plug-and-play agentic solutions like Kibo’s platform.
To make sure you’re ready for the new era of digital commerce, do the following things:
Review Existing Data
To get the most from AI agents, start by reviewing your past data. You need a lot of it—think sales history, customer interactions, marketing performance, and inventory levels. Without a large, clean dataset, AI agents can’t learn and perform as intended.
Of course, you have to look beyond just having data. You also need to check if it covers all your key channels and details. For example, do you track website visits, mobile app use, email responses, open rates, and social media engagement? Do you have product-level sales and time-stamped events? The richer and more detailed your data, the smarter your AI will be.
Also, consider where your data lives. If it’s stuck in separate systems that don’t talk to each other, your AI won’t have the complete picture or up-to-date info. If that’s the case, it may be smart to centralize and connect your data sources.
Lastly, clean your data. Fix errors, remove duplicates, and ensure consistency. Poor data quality leads to wrong AI decisions, which could result in wasted money and missed opportunities.
Audit Your Infrastructure
Before you bring AI agents into your operations, take a hard look at your tech setup. Can your ecommerce platform, CRM, and marketing tools easily connect with AI systems? If not, expect delays and extra costs during integration.
You also need real-time data. AI agents work best when they get instant updates, so if your systems only update in batches, the AI won’t be able to do what it’s supposed to do.
Keep in mind that your infrastructure has to handle more traffic and data as AI agents run more transactions. You might face some downtime and lost sales if it can’t scale or stay reliable.
Develop a Strategy
You’ll need a clear, practical plan to succeed with AI agents. While their potential is massive, you can’t expect to integrate intelligent agents and have handle everything from that point on.
So, start by defining specific goals, whether that’s boosting personalization, automating campaigns, or optimizing inventory. This is crucial because knowing what you want helps focus your efforts and helps you map where AI fits into your business. For starters, identify processes AI can handle well, like bid adjustments or customer targeting, so you avoid automating areas that need human judgment.
Also, don’t forget to set boundaries for AI agents. Decide what they can do on their own, when to pause, and when humans must step in. This keeps control tight and reduces risk, at least early on, until you’re more comfortable with agentic systems running the show.
Make Your Catalog Agent-Readable
If you want AI agents to help sell your products, your catalog has to speak their language, so to speak. That means organizing product info clearly and consistently—think simple categories, accurate descriptions, prices, and stock levels.
When AI models can easily understand your products, it can recommend the right items faster and with fewer mistakes. More importantly, well-structured product data helps the shopping agents working for your customers to find exactly what they need. This means customers get relevant, accurate suggestions and avoid frustrating problems like out-of-stock items, incorrect colors or sizes, or outdated prices.
How Can Brands Benefit from Agentic Ecommerce?
Now, all this talk is fine and dandy, but if this doesn’t translate to real-world benefits, brands won’t invest the time or resources needed to make the shift. Therefore, let’s take a look at some benefits brands can expect if they join the agentic commerce train.
- Boosted conversions & revenue
- Reduced cart abandonment
- Reduced service costs
- Increased profits
- Reduced lost sales
1. Boosted Conversions & Revenue
If we look at the current data, brands that utilize AI in their marketing efforts are typically seeing better results than their competitors. For example, brands utilizing AI are seeing:
- 15–20% higher conversion rates
- 10–30% increase in average order value
- 10–40% increase in total revenue
Of course, none of these can be attributed to an agentic framework. These numbers reflect a joint effort between AI tools and marketers, even though, truth be told, most of the decisions are driven by AI-sourced data.
With that in mind, it shouldn’t be too hard to imagine how effective agent-led strategies will be in the future.
2. Reduced Cart Abandonment
We’ve seen that AI personalization can lower cart abandonment rates by 10–30%, which is quite impressive. We’ve also seen that timely cart abandonment emails can recover up to 16% of potentially lost sales. Also impressive.
Now, what if an AI agent armed with customer data handled personalization and recovery emails? Would we see better results? Quite likely. An agent could analyze the data and figure out which segments or even individual customers prefer discounts over vouchers and switch up the abandoned cart offer to get them back to the checkout phase.
3. Reduced Service Costs
Automating customer support with AI agents will allow ecommerce brands to cut service costs by approximately 30% (or more).
However, this cost efficiency won’t come at the expense of quality. AI language models are improving fast, enabling a 24/7, context-aware service capable of handling complex issues. Over time, these systems will only get better, potentially cutting service costs even further.
4. Increased Profits
AI agents can make real-time pricing decisions based on live data, such as demand shifts, competitor pricing, inventory levels, and shopper behavior. They automatically adjust product prices, prioritize high-margin items, and personalize what each customer sees, which can lead to significant gains. Current data shows that dynamic pricing strategies can increase profits by 15–25%, even without intelligent agents running the show.
5. Reduced Lost Sales
Since AI agents use real-time demand signals, historical data, and external variables like seasonality or trends to forecast product needs with far greater accuracy than traditional methods, they can reduce demand planning errors by 30–50%.This reduces the chances of overstocking, out-of-stock products, or missed opportunities during high-demand periods and prevents lost revenue. In fact, AI-powered forecasting may be able to cut lost sales by as much as 65%, according to McKinsey.
