AI is changing how eCommerce businesses sell and serve customers. The shift is visible in the customer’s journey. According to Salesforce’s 2026 State of Commerce report, agentic search is becoming an important step in purchase journeys with usage growing 200% year over year.
But the opportunity extends beyond product discovery. AI works across commerce workflows, using business data and connected systems to perform tasks. This is where the distinction between Generative AI and Agentic AI matters. Generative AI creates content and responses, while Agentic AI can plan and execute tasks. For commerce leaders, understanding this difference helps determine where greater AI autonomy can deliver business value.
What is Agentic AI?
Agentic AI refers to AI systems that can understand a goal, plan the steps needed to achieve it, use tools or systems, and take actions with little human intervention. Instead of stopping after providing an answer, an AI agent interacts with business systems, executes multiple steps, evaluate results, and continues until the task is completed.
Key features:
- Works toward defined goals
- Breaks goals into multiple steps
- Uses APIs, tools, and business systems
- Makes decisions based on data and rules
- Executes tasks and workflows
- Evaluates results and adjusts its actions
- Operates with defined permissions and human oversight
“We’re defining agentic AI as systems that can plan autonomously and take actions to meet goals.” Tom Coshow, Senior Director Analyst at Gartner
What is Generative AI?
Generative AI refers to AI systems that create new content or responses based on user inputs and available context. It understands natural-language requests and generates text, recommendations, summaries, images, code, and other outputs. In commerce, it can help customers discover products, answer questions, compare options, and generate personalized recommendations.
Key features:
- Generates content and responses
- Understands natural-language queries
- Summarizes and synthesizes information
- Provides recommendations
- Personalizes responses using available context
- Responds to user prompts
“Generative AI has acted as an immensely effective catalyst for the adoption of AI into our lives, work and businesses.” Bernard Marr, Futurist
Agentic AI vs. Generative AI in eCommerce
The table below shows a comparison between Agentic AI and Generative AI with specific use cases in eCommerce.
| Agentic AI | Generative AI | |
| Role | Executes goals through multiple actions. | Generates, content, answers, and recommendations. |
| Customer interaction | Finds products, checks availability, and initiates transactions. | Answers questions and provides recommendations. |
| Decision-making | Makes decisions within defined rules and permissions. | Supports decisions by generating or summarizing information. |
| System access | Uses APIs and systems to execute workflows. | Retrieves information from connected systems. |
| Impact | Automates processes and influences conversion, fulfillment, service costs, and retention. | Improves discovery, engagement, and productivity. |
When Should Commerce Leaders use Generative AI vs. Agentic AI?
Commerce leaders should choose between Generative AI and Agentic AI based on the outcome they want to achieve. Generative AI is best suited to understanding, creating, and assisting, while Agentic AI is better suited to deciding and executing multi-step workflows.
Use Generative AI when your goal is to:
- Understand customer intent and respond to questions.
- Generate personalized product recommendations.
- Summarize product information or compare options.
- Help customers discover products and make purchase decisions.
- Assist employees with content, insights, or information.
For example, a Generative AI assistant can understand a customer’s requirements and recommend the most relevant products.
Use Agentic AI when your goal is to:
- Execute multi-step commerce workflows.
- Make decisions based on business rules, customer context, and real-time data.
- Coordinate actions across multiple commerce systems.
- Complete tasks with limited human intervention.
For example, an Agentic AI system can take a product recommendation further by checking inventory, validating pricing and promotions, creating the order, and triggering fulfillment steps within its permissions.
This does not mean Agentic AI should replace Generative AI. Generative AI can provide the intelligence and interaction layer, while Agentic AI can use that intelligence to execute defined workflows. The right approach depends on the business process, available data, integration maturity, and level of autonomy the business is willing to allow.
Conclusion
Generative AI can improve how customers discover products, get answers, and make decisions. Agentic AI can take this further by executing defined tasks across commerce workflows.
For commerce leaders, the priority should be to identify where autonomous actions can create measurable value. This starts with well-defined workflows, reliable data, connected systems, and clear controls.
At Ignitiv, we help commerce leaders embrace AI by rethinking how commerce platforms, data, and operational systems work together, creating a foundation for intelligent commerce operations.
FAQs
Agentic AI can automate workflows such as product discovery, order tracking, return processing, replenishment, and customer service. It can check data, apply business rules, take approved actions, and update connected systems.
An agent needs access to product, customer, inventory, pricing, order, and fulfillment data. It may connect with platforms such as commerce systems, PIM, CRM, ERP, and OMS through APIs.
Prioritize workflows that are high-volume, repetitive, rule-based, data-rich, and measurable, for example order management, customer service, product discovery, returns, and inventory exceptions.
Track metrics such as revenue influenced, conversion, AOV, cost per interaction, processing time, automation rate, error rate, and human intervention.
For revenue, track agent-assisted conversions, recommendation-driven purchases, AOV, repeat purchases, and revenue from completed workflows. For savings, compare labor hours, service costs, processing time, and exception-handling costs before and after deployment.
