AI-driven traffic to retail sites surged 693% year-over-year during the 2025 holiday season, according to Adobe Analytics data covering more than 1 trillion visits to U.S. retail sites. That is not a projection. It already happened. And the retailers who had autonomous agents in place when that traffic arrived converted it at rates 31% higher than other traffic sources.
During the 2025 holiday season, AI agents and generative AI tools influenced more than 20% of all online retail sales globally, according to Salesforce. AI-referred shoppers spent 45% more time on-site, viewed 13% more pages per visit, and generated 254% higher revenue per visit than non-AI traffic sources.
The question retail leaders are now asking is not whether AI agents work in commerce. The proof is in. The question is whether their organization will be positioned to capture the next wave or spend another cycle watching competitors do it first.
This article makes the business case for Agentforce Commerce using real data from real retailers. Not vendor projections. Named brands, hard numbers, and an honest look at where implementation complexity can stall ROI if the foundation isn’t right.
Salesforce Agentforce Commerce is the AI agent layer built natively on Data Cloud and Salesforce Commerce Cloud. Formerly known to many retailers as Salesforce Commerce Cloud AI, the platform has evolved well beyond its roots as a recommendation engine. The architecture matters here: this is not a chatbot bolted onto an existing platform. Agents operate directly on unified customer data, pulling from CRM records, purchase history, behavioral signals, and real-time inventory simultaneously, and act autonomously across the full commerce journey without waiting for a human to trigger each step.
That distinction separates Agentforce Commerce from the wave of AI chat tools retailers experimented with over the past two years. Standard chatbots follow scripts. Agentforce agents reason, make decisions, execute multi-step tasks, and escalate to human agents only when genuinely needed.
The platform’s core capabilities span the entire purchase lifecycle:
“Consumers are reviewing more content pre-purchase, making Guided Shopping and AI personalization key to closing sales.” — Nitin Mangtani, SVP/GM, Agentforce Commerce, Salesforce
The unified data foundation is the differentiator. Agents are only as effective as the data they act on. For retailers already running on Salesforce Commerce Cloud, that foundation is largely in place. For those who aren’t, it becomes the first implementation conversation. To understand what Salesforce Agentforce is at the platform level, including its architecture and agent builder, that context is worth establishing before evaluating the commerce-specific layer.
The most important thing to know about Agentforce for retail is that the ROI is no longer theoretical. Several retailers have published results, and the numbers are specific enough to anchor a business case.
| Brand | Use Case | Result |
|---|---|---|
| R.M. Williams | Guided Shopping + personalized recommendations | 34% revenue growth, 20% conversion rate lift |
| Pandora | Automated inquiry handling + AI recommendations | 10% NPS improvement |
| Wiley | AI agents across commerce and service workflows | 213% ROI |
| Shoe Carnival | Inbound call automation via AI agent | Projects 40% of call volume handled autonomously |
R.M. Williams is the clearest retail case study available. The Australian heritage footwear brand deployed Agentforce to power guided shopping experiences and personalized product recommendations. The outcome: 34% revenue growth and a 20% conversion rate increase. For a brand selling considered, premium products where the path to purchase involves real decision-making, those numbers reflect exactly what guided shopping agents are designed to do. They reduce friction at the moment of intent.
Pandora’s deployment focused on automating high-volume customer inquiries and deploying AI-powered recommendations at scale. The result was a 10% NPS lift, a customer satisfaction gain that directly correlates with repeat purchase rates and lifetime value in jewelry retail. As David Walmsley, Pandora’s Chief Digital and Technology Officer, put it: “We are redefining shopping with true dialogue via trusted AI and unified data.”
The brand-level results align with wider data from Salesforce’s State of Sales report. The productivity gap between AI-adopting and non-adopting teams is widening fast.
83% of sales teams using AI reported revenue growth in the past year, compared to 66% of teams without AI. Top-performing sellers are 1.7 times more likely to use AI agents than their lower-performing peers.
Nearly 90% of respondents plan to adopt AI agents by 2027. And 94% of sales leaders who have already adopted them agree they are essential to growth.
Wiley’s 213% ROI is the highest in the published data set. It also signals that the Agentforce model scales beyond pure retail into commerce-adjacent sectors, a useful data point for retailers with complex B2B or wholesale channels alongside their consumer business.
Understanding where Agentforce Commerce generates the most impact helps retailers prioritize deployment. The use cases below are ranked by the strength of available evidence, not by platform positioning.
When customers face real trade-offs, fit, compatibility, personalization, price, a guided shopping agent outperforms a static product page. It asks qualifying questions, narrows the product set, and surfaces the right recommendation with context.
This is the mechanism behind R.M. Williams’ 20% conversion lift. The agent reduces the cognitive load that causes abandonment on high-ticket items.
63% of Gen Z consumers say they are interested in using AI agents to help make purchases, according to Salesforce. That cohort is entering peak spending years. Retailers who build guided shopping capability now are building for the dominant consumer demographic of the next decade.
“Where is my order?” queries are the single highest-volume contact reason in most retail service operations. They are also entirely automatable. According to Salesforce, a single WISMO request costs a business around $12 to resolve manually. Agentforce post-purchase agents handle these queries, returns initiation, and order modification requests without human involvement, freeing service teams for genuinely complex cases.
Shoe Carnival projects its Agentforce agent will handle 40% of inbound call volume autonomously. That is not a marginal efficiency gain. At scale, it represents a structural reduction in service cost that compounds as order volume grows.
During the 2025 holiday season, AI agents and generative AI tools influenced more than 20% of all online retail sales globally, according to Salesforce. The scale reflects what happens when recommendation logic runs on unified customer data rather than session-level signals. Agents that know a customer’s full purchase history, browsing behavior, and stated preferences produce recommendations that convert. Agents working from anonymous session data produce noise.
Agentforce agents can deploy personalized promotions in real time based on behavioral triggers: cart abandonment, browsing patterns, loyalty tier, and predicted churn risk. This moves promotional strategy from batch campaigns to individual-level intervention, which is where how AI drives commerce growth at the margin level becomes most tangible.
The ROI data is compelling. The implementation complexity is real. Retailers who go in expecting a plug-and-play deployment tend to underestimate the data work required before agents can perform at the level the case studies describe.
The four most common friction points, based on Salesforce’s own research and published case study data:
“Traffic from tools like ChatGPT, Gemini, and Perplexity is growing rapidly. More importantly, it’s high-quality traffic that converts. For retailers and brands, this signals a lasting shift in how consumers discover and evaluate products.” — Vivek Pandya, Lead Analyst, Adobe Digital Insights
None of these challenges are blockers. They are sequencing decisions. Retailers who address the data foundation first deploy agents that perform closer to the published benchmarks from day one.
The adoption curve for AI agents in commerce is not moving on a five-year timeline. It is moving on an 18-month one.
54% of sellers are already using AI agents. 90% plan to by 2027. Agentforce’s ARR grew 114% year-over-year to $1.4 billion in Q3 FY2026, with account growth of 70% quarter-over-quarter. 76 of North America’s top 2,000 retailers already run on Salesforce Commerce Cloud, meaning the integration path to Agentforce Commerce exists for a substantial portion of the market right now.
The 2025 holiday season generated $257.8 billion in online sales, with AI-referred traffic converting at rates 31% higher than other sources. The 2026 season will be larger. Retailers who have agents in place, trained on real customer data, and integrated across their commerce stack before that window opens will capture a disproportionate share of it. Those deploying in Q4 will be optimizing in real time against competitors who started a year earlier.
Three steps retailers should take now:
The ROI case for Agentforce Commerce is no longer a forward-looking argument. It is a present-tense one. The retailers in the case studies above acted. Working with an experienced Salesforce implementation partner is the fastest way to close the gap between where your data foundation is today and where it needs to be before peak season.
Agentforce Commerce is Salesforce’s AI agent layer built natively on Data Cloud and Commerce Cloud. Unlike chatbots, it operates autonomously across the full commerce journey, from guided shopping and personalized recommendations to order management and post-purchase service, using unified customer data to act in real time.
Early results are strong. R.M. Williams saw 34% revenue growth and a 20% conversion rate lift. Pandora achieved a 10% NPS improvement. Wiley reported 213% ROI. Across the board, 83% of sales teams using AI reported revenue growth, compared to 66% of teams without AI.
Agentforce Commerce agents act autonomously on unified customer data, pulling from CRM, purchase history, and behavioral signals simultaneously. Standard chatbots follow scripts. Agentforce agents reason, make decisions, execute multi-step tasks, and hand off to human agents only when genuinely needed.
The highest-impact use cases are guided shopping for high-consideration purchases, post-purchase and WISMO query automation, personalized product recommendations at scale, and AI-driven promotions. Shoe Carnival, for example, projects its Agentforce agent will handle 40% of inbound call volume.
Disconnected retail data is the primary blocker. Agents are only as effective as the data they act on. Only 27% of enterprise applications are currently integrated, and 50% of deployed agents operate in isolation. Retailers need a unified data foundation before agents