Agentforce for Commerce: How AI Helps in Improving Sales

If you’re still relying on basic chatbots and manual merchandising tasks, you’re leaving money on the table. Salesforce launched Agentforce for Commerce in October 2024, and it’s not just another AI feature—it’s autonomous agents that analyze data, make decisions, and take action without constant human supervision.

The numbers prove it: Online traffic volumes driven by AI assistants grew 119% year-over-year in the first half of 2025, with intelligent agents now projected to influence 22% of global orders during Cyber Week.

Why Salesforce Launched Agentforce for Commerce?

Let’s address the elephant in the room. Why did Salesforce build Agentforce when they already had Einstein Copilot?

The Autonomy Gap

The answer comes down to autonomy. Copilots and chatbots rely on human requests and struggle with complex or multi-step tasks, while Agentforce offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention.

Think of it this way: copilots are like having a very smart assistant who waits for your instructions. Agentforce agents are like hiring specialized team members who know their job and get it done.

Market Demand Drove Innovation

The market demanded this evolution. Key indicators include:

• Gartner expects AI agents to command $15 trillion in B2B purchases by 2028
• Organizations using AI agents for 80% of customer-facing processes will outperform competitors
• Businesses needed systems that could handle real-time decision-making at scale
• The AI agents market reached $5.9 billion in 2024 and is expected to hit $105.6 billion by 2034

Michael Affronti, SVP and General Manager of Commerce Cloud, explained that “traditional point solutions often result in ‘commerce islands,’ where different parts of the business operate in isolation, leading to fragmented customer journeys and missed revenue opportunities”. Salesforce built Agentforce to solve this fragmentation problem.

Unified Commerce Platform Architecture

The platform unifies B2C, DTC, and B2B Commerce, Order Management, and Payments on a single integrated platform powered by Data Cloud and the Einstein Trust Layer. This means your product catalogs, customer data, inventory levels, and pricing strategies all work together instead of fighting each other.

Rapid Development and Deployment

Here’s what changed with the launch. At Dreamforce 2024, Salesforce customers built more than 10,000 autonomous agents designed to tackle specific business challenges, with some customers noting it took only three minutes to create functioning prototypes. That’s deployment speed traditional commerce platforms can’t match.

The reasoning engine behind these agents—Atlas—delivers results that matter. Agents built on Atlas achieved state-of-the-art results, with one-third more accurate and two times more relevant results based on Fortune 500 customer benchmarks.

Which Key Challenges Does Agentforce for Commerce Solve?

E-commerce teams face the same problems over and over: too many manual tasks, disconnected systems, and customers who expect Amazon-level experiences regardless of where they shop.

Let’s break down what Agentforce actually fixes.

Manual Operations Eating Your Margins

Your merchandisers spend hours every week updating product descriptions, setting up promotions, and adjusting site layouts. Traditional workflows create bottlenecks:

• Configuring product boosting rules requires 15 different clicks and understanding rule syntax
• Product description updates happen one SKU at a time
• Promotional campaigns need manual setup across multiple channels
• Site layout changes require technical knowledge and testing cycles

With Agentforce Merchant, that same boosting task becomes a simple natural language prompt: “boost new arrivals.” The agent handles the implementation.

The Data Silo Problem

When your inventory system doesn’t talk to your e-commerce platform, customers see products that aren’t in stock. When your CRM doesn’t connect to your marketing automation, you can’t personalize experiences. Commerce Cloud embodies unified commerce—giving businesses a single, integrated platform that brings every part of the commerce journey together with Agentforce agents to unlock new revenue opportunities and AI-powered efficiency.

Scale Without Adding Headcount

Here’s the reality: An estimated 41% of employee time is spent on repetitive, low-impact work, and 65% of desk workers believe generative AI will allow them to be more strategic. Your team shouldn’t be manually responding to ‘where’s my order’ inquiries when Agentforce customer service agents can handle those instantly.

Inconsistent Customer Experiences

Providing consistent experiences across web, mobile, store, and social commerce requires coordination that breaks down as you scale. Agentforce operates across every customer touchpoint, maintaining context and personalization whether someone’s browsing on their phone, chatting with an agent, or talking to a store associate.

Static Systems Can’t Keep Pace

Markets move fast. Competitors change prices. Inventory levels shift. Customer preferences evolve. Static systems can’t keep up. Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organization’s customized guardrails, ensuring every customer interaction is informed, relevant, and valuable.

The challenge isn’t just implementing AI—it’s implementing AI that actually works with your existing commerce infrastructure. That’s where the Commerce Cloud native integration makes a difference. For retailers already on the platform, agents access real-time inventory, orders, and customer profiles without complex API development.

Agentforce for Commerce: Use Cases

Let’s get practical. Here are six ways Agentforce transforms day-to-day commerce operations, backed by real data.

Agentforce Use Case #1: AI-Powered Product Recommendations

Generic product recommendations don’t cut it anymore. Customers expect Amazon-level personalization everywhere they shop.

The numbers are compelling. Product recommendations drive up to 31% of e-commerce site revenues. Let that sink in—nearly one-third of your revenue comes from helping customers discover the right products at the right time.

How Agentforce Recommendations Work Differently

But here’s what makes Agentforce different from basic recommendation engines. Instead of just analyzing past purchase history, the Personal Shopper agent understands natural language queries like “I need a gift for my sister who loves hiking” and generates contextually relevant suggestions based on inventory, pricing, customer segment, and real-time behavior.

Personalization programs can yield up to 20% higher customer satisfaction and a 10-15% boost in conversion rates if successful. The key word is “if successful”—most personalization attempts fail because they’re based on incomplete data or rigid rules.

The Data Cloud Advantage

Agentforce avoids this by connecting to Data Cloud, which unifies customer data across every touchpoint. When someone browses your mobile app, abandons a cart on your website, and then walks into your store, the agent knows the full journey and adjusts recommendations accordingly.

The impact extends beyond immediate sales. Personalized product recommendations can make 28% of customers more likely to buy a product they didn’t intend to buy initially. You’re not just fulfilling existing demand—you’re creating new demand through intelligent discovery.

For B2B commerce, this becomes even more critical. Business buyers need specific SKUs, bulk pricing, and contract-compliant purchasing. The Buyer agent handles these complex requirements while maintaining the conversational simplicity that B2C customers expect.

Integrating these capabilities with Marketing Cloud automation and Agentforce for marketing creates personalized journeys that nurture leads based on browsing behavior and recommendation interactions.

Agentforce Use Case #2: Real-Time Dynamic Pricing to Maximize Revenue

Pricing is no longer a “set it and forget it” decision. Your competitors are adjusting prices based on demand, inventory levels, and market conditions—sometimes multiple times per day.

Here’s the business case: E-commerce platforms employing AI-driven dynamic pricing strategies have seen profit margins rise by 25%, as AI adjusts prices in real-time based on market trends and consumer behavior.

Practical Applications of Dynamic Pricing

Think about what that means practically:

• When demand spikes for a specific product category, prices adjust upward to maximize margin
• When inventory sits too long, prices drop strategically to move stock before it becomes obsolete
• When a competitor runs a promotion, your pricing responds intelligently without manual intervention
• When high-value customers browse, pricing can reflect their loyalty tier and purchase history

Amazon’s big data analytics, which updates prices every 10 minutes, has boosted annual profits by an average of 143%, highlighting the power of dynamic pricing. You don’t need Amazon’s resources to implement similar strategies—that’s exactly what Agentforce enables.

How the Merchandising Agent Optimizes Pricing

The Merchandising Actions agent analyzes multiple factors simultaneously: competitor pricing, customer segments, inventory turnover rates, seasonal trends, and historical purchase patterns. It then recommends or executes pricing changes that balance revenue maximization with customer perception.

One often-overlooked benefit: dynamic pricing reduces the need for blanket promotions. Instead of offering site-wide discounts that erode margin across your entire catalog, you can target price adjustments to specific products, customer segments, or time windows.

Even a 1% improvement in pricing can lead to a 12.5% increase in profits. That’s not a typo—small pricing optimizations compound into significant profit improvements because they affect every transaction.

For businesses worried about customer perception, Agentforce operates within guardrails you define. You set minimum margins, maximum discount percentages, and pricing rules that reflect your brand positioning. The agent optimizes within those boundaries. Proper Commerce Cloud optimization ensures these guardrails align with your business strategy while maximizing revenue potential.

Agentforce Use Case #3: Instant Personalized Discounts

Blanket promotions are expensive and ineffective. You’re giving discounts to customers who would have purchased at full price while missing customers who need different incentives.

Agentforce changes this equation through real-time personalization at the individual customer level.

The Revenue Impact of Smart Discounting

Personalization leaders achieve revenue growth rates approximately 10 percentage points higher than laggards annually. The difference isn’t just offering discounts—it’s offering the right discount to the right customer at the right moment.

Consider how this works in practice. A first-time visitor browsing premium products might receive free shipping to reduce friction. A returning customer who abandoned their cart gets a time-limited discount on those specific items. A loyal customer who typically buys during promotions sees an early-access offer before the public sale begins.

Machine Learning Optimizes Every Offer

The Personal Shopper agent analyzes customer behavior in real-time and determines the minimum incentive needed to convert. Personalized recommendations also directly enhance e-commerce income, with an estimated 35% of Amazon’s revenue coming from its AI-based product recommendations.

This approach protects margin while improving conversion rates. You’re not over-discounting to customers who would have paid full price, and you’re not under-incentivizing customers who need a push to complete their purchase.

The system also learns over time. If certain customer segments consistently respond to specific incentive types (free shipping vs. percentage off vs. dollar amount off), the agent adapts future offers accordingly.

For B2B contexts, personalized discounts become even more sophisticated. The agent can apply contract pricing, volume discounts, and customer-specific terms automatically while ensuring compliance with negotiated agreements. Organizations managing complex B2B commerce relationships benefit from agents that understand multi-tier pricing structures and approval workflows.

Starbucks’ adoption of AI personalization led to a 30% increase in ROI on marketing initiatives and a 15% rise in customer engagement levels compared to previous approaches. The key was moving from batch-and-blast promotions to individual-level personalization.

Agentforce Use Case #4: Live Inventory Updates Reducing Stockouts

Nothing frustrates customers more than ordering a product that’s actually out of stock. The traditional approach of batch inventory updates every few hours doesn’t work in fast-moving categories.

Real-Time Visibility Across Your Network

The Order Routing agent solves this through real-time inventory visibility across your entire fulfillment network. When a customer adds an item to their cart, they see actual availability. When they complete checkout, the system routes the order to the optimal fulfillment location based on current stock levels, shipping costs, and delivery speed.

Here’s why this matters financially: AI-powered forecasting can reduce errors by 30 to 50% in supply chain networks, leading to a 65% reduction in lost sales due to inventory out-of-stock situations.

Think about the revenue impact. If you’re losing 5% of potential sales to stockouts, and your annual revenue is $10 million, that’s $500,000 left on the table. A 65% reduction in those stockouts recovers $325,000 annually.

Beyond Simple Availability

The agent goes beyond simple availability checks. It predicts demand patterns and adjusts safety stock levels accordingly:

• During promotional periods or seasonal spikes, it automatically increases buffer inventory for fast-moving items
• During slower periods, it reduces safety stock to minimize holding costs
• For omnichannel retailers, it fulfills online orders from store inventory when distribution centers run low
• It handles complex scenarios like pre-orders, backorders, and partial fulfillment intelligently

Warehousing costs decrease around 10 to 40% through improved inventory optimization. You’re not just avoiding lost sales—you’re reducing the capital tied up in excess inventory.

Agentforce Use Case #5: Automated Cart Abandonment Recovery

Here’s a sobering statistic: The average shopping cart abandonment rate across industries is 70.19% in 2025. Seven out of ten customers add items to their cart and leave without purchasing.

That’s not just a conversion problem—it’s a revenue problem. E-commerce brands lose $18 billion in sales revenue each year because of cart abandonment.

Understanding Why Customers Abandon

Traditional cart abandonment tactics—sending a generic email 24 hours later—barely scratch the surface. The Personal Shopper agent takes a more sophisticated approach.

First, it identifies why the abandonment happened. Did the customer leave because of shipping costs? Lack of payment options? Uncertainty about the product? Complex checkout flow? 55 percent of shoppers will abandon if they encounter extra costs like shipping, taxes, and fees that weren’t disclosed upfront.

Customized Recovery Strategies

Based on the reason, the agent customizes the recovery approach. If shipping costs caused the abandonment, offer free shipping with a time limit. If the customer seemed uncertain about sizing, provide detailed product information and reviews. If they compared multiple similar products, highlight the differences that matter most.

The results speak for themselves. Automated abandoned cart emails drive significant results, with one in three people who clicked on an automated abandoned cart message making a purchase, representing a 42.02% click-to-conversion rate.

Multi-Channel Recovery Approach

But email is just one channel. Agentforce can trigger recovery sequences across SMS, push notifications, retargeting ads, and even direct outreach from sales teams for high-value B2B carts.

Abandoned cart flows drive the highest average revenue per recipient ($3.65) and the highest average placed order rate, or conversion rate (3.33%), of all flows. No other automated workflow comes close to these metrics.

The agent also knows when to give up. If someone abandoned their cart because they’re just browsing or comparing options, continuing to bombard them with recovery messages hurts more than it helps. 43% of US online shoppers have abandoned a cart because “I was just browsing / not ready to buy”.

For B2B commerce, cart abandonment recovery becomes more nuanced. Business buyers often need internal approvals, budget confirmation, or contract review before completing large purchases. Organizations can also leverage Agentforce for HR service to streamline internal procurement approval workflows.

The Buyer agent understands these workflows and adjusts follow-up timing and messaging accordingly. Maintaining these complex workflows requires ongoing Commerce Cloud support to ensure agents adapt as your business processes evolve.

Employing predictive AI can reduce cart abandonment rate by 18%. The key is identifying abandonment signals before the customer leaves and intervening proactively with relevant incentives or information.

Agentforce Use Case #6: Predictive Demand Forecasting Cutting Overstock Costs

Inventory is expensive. Too much inventory ties up capital and increases carrying costs. Too little inventory means lost sales and disappointed customers.

Getting this balance right manually is nearly impossible at scale. That’s where predictive demand forecasting transforms operations.

Proven Cost Reduction Results

Kumar et al. (2024) documented deployment experiences across 12 retail organizations, reporting average inventory holding cost reductions of 23.7% and stockout frequency decreases of 31.2% through AI-driven forecasting platforms.

Here’s how Agentforce approaches demand forecasting differently. Traditional methods analyze historical sales data and extrapolate forward. Agentforce incorporates dozens of additional factors: weather patterns, economic indicators, social media trends, competitor activity, marketing campaign performance, and seasonal variations.

From Prediction to Action

The Merchandising Actions agent doesn’t just predict demand—it adjusts ordering patterns, promotional calendars, and pricing strategies based on those predictions. If the system forecasts higher-than-expected demand for a product category, it increases purchase orders and plans promotional activities to capitalize on the trend.

Zara found that implementing an AI-powered demand forecasting system resulted in a 10% reduction in stockouts and a 15% decrease in excess inventory. For a retailer operating on thin margins, these improvements directly impact profitability.

Strategic Planning Benefits

The forecasting also helps with longer-term strategic decisions. Which product lines should you expand? Which categories are declining? Where should you invest in new inventory? The estimated impact of AI in the supply chain is between $1.2 trillion and $2 trillion in manufacturing and supply chain planning.

For seasonal businesses, accurate demand forecasting becomes even more critical. Walmart found that using machine learning algorithms to predict demand resulted in a 25% reduction in inventory costs. The system learns seasonal patterns and adjusts purchasing ahead of demand spikes.

One often-overlooked benefit: better cash flow management. When you order inventory closer to when you’ll actually sell it, you reduce the cash conversion cycle and free up working capital for other investments.

According to Gartner, demand forecasting is the most widely used AI application in supply chain planning, with 45% of companies currently using it and 43% planning to adopt AI-powered demand forecasting within two years. The competitive advantage goes to companies that implement these capabilities first and connect them to Sales Cloud forecasting tools for unified revenue planning across channels.

How Folio3 Can Be Your AI Strategic Partner?

Implementing Agentforce isn’t just a technical project—it’s a business transformation. You need partners who understand both the technology and the commerce context.

Why Choose Folio3?

As a trusted Salesforce Agentforce consulting partner, Folio3 brings 15+ years of Salesforce expertise with over 100 certifications across the platform. We’ve delivered 1,000+ successful implementations for companies ranging from mid-market retailers to enterprise organizations.

Here’s what makes our approach different. We don’t start with technology—we start with your business problems. What are your biggest revenue leaks? Where do customers drop off? Which processes consume too much manual effort? Then we map Agentforce capabilities to those specific challenges.

Our Agentforce Implementation Approach

Our team includes certified Agentforce specialists who work together to deliver seamless implementations. We understand how Data Cloud feeds agents, how Einstein Trust Layer maintains security, and how to customize agent behaviors for your specific use cases.

For businesses already on Commerce Cloud, we accelerate deployment by leveraging native integrations. For companies on other platforms, we handle the Salesforce migration and modernization required to take full advantage of autonomous agents.

Think of it this way: copilots are like having a very smart assistant who waits for your instructions. Agentforce agents are like hiring specialized team members who know their job and get it done.

Our managed services team stays with you after go-live, monitoring agent performance and continuously optimizing based on real-world results.

For businesses operating B2B commerce, we specialize in complex configurations like custom pricing, contract management, and approval workflows. We’ve also helped clients with Marketing Cloud integration and Sales Cloud implementation to ensure agents work seamlessly across your entire CRM ecosystem.

FAQs

What is Agentforce for Commerce?

Agentforce for Commerce is Salesforce’s suite of autonomous AI agents designed specifically for e-commerce operations. Unlike traditional chatbots that require constant human guidance, these agents autonomously handle tasks like product recommendations, order routing, merchandising actions, and customer service across web, mobile, and in-store channels.

How does Agentforce differ from Einstein Copilot?

Einstein Copilot is an AI assistant that waits for user commands and provides suggestions. Agentforce agents operate autonomously—they analyze data, make decisions, and execute tasks without requiring human intervention for every action. Agentforce is built for continuous operation while Copilot is built for on-demand assistance.

Which industries benefit most from Agentforce for Commerce?

Retail, fashion, consumer goods, electronics, automotive, and B2B distributors see the strongest ROI. Any business selling products online that deals with large catalogs, complex pricing, or high transaction volumes benefits from autonomous commerce agents.

Can Agentforce integrate with existing e-commerce platforms?

Agentforce works natively with Salesforce Commerce Cloud. For businesses on other platforms like Shopify, Magento, or custom solutions, integration is possible but requires API development and data synchronization through middleware or Data Cloud connections.

Companies migrating to Commerce Cloud work with Salesforce migration specialists to handle data transfer, API integrations, and ensure seamless transition to Agentforce-powered operations.

How long does Agentforce implementation take?

Implementation timelines vary based on complexity. Simple deployments with pre-built agents on existing Commerce Cloud instances can launch in 4-6 weeks. Complex implementations requiring custom agent behaviors, extensive integrations, and data migration may take 3-6 months.

Organizations work with an experienced Salesforce implementation service provider to reduce deployment time and ensure proper configuration from the start.

What data does Agentforce need to function effectively?

Agentforce requires customer data (purchase history, browsing behavior, preferences), product catalog data (descriptions, pricing, inventory), order data (fulfillment, shipping, returns), and external data (weather, economic indicators, competitor pricing). Data Cloud unifies these sources.

Does Agentforce replace human employees?

No. Agentforce handles repetitive tasks so human employees can focus on complex problems, strategic decisions, and high-value customer interactions. It augments teams rather than replacing them. Companies report employees become more strategic after implementing autonomous agents.

What security measures protect customer data in Agentforce?

Agentforce operates within Salesforce’s Einstein Trust Layer, which provides data governance, access controls, audit trails, and compliance frameworks. Customer data remains encrypted, and agents only access information necessary for specific tasks based on role-based permissions.

How much does Agentforce cost?

Salesforce prices Agentforce starting at $2 per conversation for customer-facing agents. Enterprise pricing varies based on deployment scale, number of agents, and transaction volume. The ROI typically comes from increased conversion rates, reduced operational costs, and improved customer lifetime value.

Can Agentforce handle multiple languages?

Yes. Agentforce supports conversational commerce in seven languages at launch, with additional language support expanding over time. This enables global retailers to deploy consistent experiences across regions without building separate systems for each market.