What is an AI agent and an AI copilot?
Artificial intelligence is transforming B2B commerce: AI copilots and agents turn manual, time-consuming processes into proactive, intelligent interactions – driving efficiency, improving customer experience, and accelerating revenue. This glossary article explains both concepts, highlights the key differences, and shows how B2B companies can deploy them strategically in e-commerce.
What is an AI agent?
An AI agent is a software-based system that autonomously makes decisions and executes actions to achieve a defined goal – without requiring continuous human input. Key characteristics include a knowledge base, automated decision-making, and the ability to orchestrate multiple steps and tools: researching information, calling external systems, or triggering orders.
In B2B e-commerce, AI agents can check product availability, compare prices, apply procurement policies, and initiate purchase orders in the background. Modern commerce platforms connect agents with existing ERP, PIM, and CRM systems to automate entire procurement or service workflows.
What is an AI copilot?
An AI copilot is an AI-powered assistant embedded directly into applications. It provides context-relevant information, makes suggestions, and guides users through complex tasks – while keeping the final decision in human hands.
In B2B e-commerce, copilots support buyers during product search, summarize relevant information, and recommend suitable solutions. Actions such as placing an order remain with the user.
What is the difference between an AI agent and an AI copilot?
- Copilots: assistive and collaborative – they support users, provide recommendations, and operate within a visible user interface.
- Agents: more autonomous – they pursue defined goals and execute multi-step processes in the background, often without continuous user input.
In B2B commerce, both complement each other: copilots enhance the human-system interaction, while agents handle operational tasks behind the scenes.
Benefits of AI agents & AI copilots in B2B e-commerce
1. Faster and more accurate product discovery
Copilots can search product catalogs, technical datasheets, and availability in real time, suggesting the right products in natural language. This dramatically reduces the time buyers spend searching – especially in complex assortments with thousands of SKUs.
2. Relieving sales and support teams
AI copilots handle recurring questions, instantly surface context from order histories and documents, and prepare information for sales and service staff. Teams can focus on high-value customer interactions while standard queries are resolved in self-service.
3. Automating complex procurement processes
AI agents orchestrate multiple steps – from demand identification through price and supplier comparison to order placement – while applying predefined rules and policies. In agentic commerce scenarios, agents can check availability, negotiate volume discounts, and compare delivery options autonomously.
4. Higher conversion in the online shop
Combined copilot and agent approaches shorten the path from problem statement to the right solution. Generative AI assistants can increase conversion rates by guiding customers to relevant products faster and enabling transactions directly within the dialogue.
5. Better data utilization across system boundaries
By integrating with ERP, CRM, PIM, and other systems, copilots and agents can access, enrich, and combine existing data in real time. Composable AI architectures allow different AI services and agents to be orchestrated to map end-to-end commerce processes.
Practical challenges & solutions for AI deployment in B2B
Challenge 1: Unclear objectives and use cases
Many B2B companies start with AI without defining concrete goals or prioritizing use cases. This leads to pilot projects with no measurable business impact and makes internal adoption difficult.
- Solution: A B2B-specific AI roadmap with clear commerce goals
A structured approach starts with identifying the most valuable pain points in commerce, sales, and service – e.g., product search, quoting processes, after-sales service. Intershop focuses its copilots and agents explicitly on commerce-adjacent tasks such as product recommendations, order processes, and self-service, so that use cases can be tied directly to KPIs like conversion rate, order effort, and support volume.
Challenge 2: Fragmented tech stack and data silos
Without consistent product, pricing, and customer data, copilots and agents cannot perform at their best. Data silos lead to incorrect recommendations, incomplete answers, and user distrust.
- Solution: Composable commerce platform with integrated AI orchestration
A platform that consolidates product data, customer data, and transaction history and integrates AI services via open APIs forms the foundation. Intershop relies on a composable architecture in which copilots and agents orchestrate various internal and external systems, from search and pricing engines to analytics and BI.
Challenge 3: Skepticism toward autonomous decisions
Especially in regulated industries and for high-value orders, there are reservations about delegating purchasing or pricing decisions to an AI system. The perceived risk of errors is high.
- Solution: A staged model from copilot to agent
A pragmatic approach is to start with assistive copilots that make suggestions confirmed by humans. Only once processes and data quality are proven reliable are individual steps handed over to agents. This allows trust to be built in a controlled, incremental way.
Challenge 4: Lack of internal AI expertise
Many B2B companies have neither in-house data science teams nor experience with LLMs, RAG, or agent orchestration. This makes it difficult to go beyond simple chatbots.
- Solution: Ready-made commerce-specific copilots and agents
Instead of developing everything from scratch, companies can turn to ready-made e-commerce copilots and agents. Intershop offers the “Copilot for Buyers” as a ready-to-use, AI-powered sales and service assistant.
Intershop continuously expands its AI agent portfolio with each release – including agents for localization and business intelligence – positioning itself as the B2B commerce platform that integrates copilots and agents seamlessly, enabling a practical path toward agentic commerce.
B2B use cases for AI agents & AI copilots
Use case 1: Technical wholesaler – procurement with Copilot for Buyers
A purchasing association or technical wholesaler with a large assortment deploys a Copilot for Buyers to guide procurement managers through the catalog. The copilot understands problem-based queries (“I need a corrosion-resistant bolt for outdoor use”) and suggests suitable products while factoring in framework agreements, availability, and alternatives.
Intershop customers already use the Intershop Commerce Platform as the foundation to optimize their digital procurement and sales processes – creating the ideal base for deploying AI copilots and agents for product search, advisory, and ordering.
Use case 2: Mechanical engineering manufacturer – after-sales service with copilot & agents
A machinery manufacturer operates a B2B service portal with spare parts, documents, and service requests. An AI copilot helps service technicians and customers find the right spare parts based on serial numbers, fault descriptions, and maintenance history. Autonomous agents trigger background workflows – such as creating tickets, reserving parts in inventory, or booking a technician appointment.
Use case 3: B2B distributor for construction & trade supplies – personalized advice in the webshop
A B2B distributor serving diverse customer groups – from sole traders to large enterprises – uses a copilot in the webshop to answer questions about applications, standards, and material properties. Depending on the customer segment and order volume, the copilot recommends suitable products, accessories, and consumables. An agent handles pricing tiers, stock levels, and delivery options in the background and feeds them into the dialogue.
Use case 4: Global manufacturer – localization & AI agents
A global manufacturer operates multiple country stores with different languages, assortments, and regulatory requirements. Localization agents analyze content, pricing, and assortments, suggest adaptations, and can – within defined guardrails – automatically handle translations, currency conversions, or product selection for specific regions. Intershop continuously develops its AI agent capabilities and delivers new features with each release that cover exactly these commerce tasks, including localization and business intelligence.
Technology and tools:
How AI agents and copilots work in B2B commerce
Large language models (LLMs) & generative AI
Modern AI agents and copilots are powered by large language models that understand, contextualize, and respond to natural language input. They enable complex queries – such as technical product specifications or delivery conditions – to be processed and answered in real time. Leading providers such as OpenAI, Google, and Anthropic offer these models as API-based services that can be integrated into commerce platforms.
Retrieval-augmented generation (RAG) & knowledge integration
Rather than generating answers solely from trained model knowledge, modern copilots and agents use RAG approaches to access company-specific data, such as product catalogs, price lists, documents, or order histories. The result is context-accurate, up-to-date answers based on real business data rather than generic outputs. For B2B companies with complex assortments and individual customer pricing, this is a decisive advantage.
Agent orchestration & workflow engines
AI agents coordinate multiple tools, APIs, and systems simultaneously – for search, pricing, availability, or reporting. Orchestration frameworks such as LangChain, AutoGen, or proprietary solutions enable multiple agents to collaborate within defined workflows. This creates multi-step, automated processes that go far beyond a single dialogue exchange.
Composable AI architecture in commerce platforms
Modern B2B commerce platforms adopt composable architectures in which AI capabilities are integrated as independent, interchangeable modules. Copilots and agents can be activated flexibly and connected to existing ERP, PIM, or CRM systems without rebuilding the entire platform. This modular approach enables a phased entry and significantly reduces implementation risk.
The technology foundation is critical – but the right strategy for getting started is equally important. The most common questions on this topic are answered in the FAQ section below.
FAQs about AI agents & AI copilots
Are AI copilots and AI agents the same thing?
No. AI copilots are primarily assistive tools embedded in the user’s interface, supporting them in their work. AI agents are autonomous systems that pursue defined goals and execute processes automatically – often interacting with other agents and systems without continuous human input.
Why are AI agents and copilots relevant for B2B companies?
Because B2B processes are often complex, data-intensive, and repetitive. Copilots accelerate knowledge work, quoting, and information processes, while agents largely automate routine tasks in procurement, sales, and service. This reduces process costs, increases responsiveness, and enables customers to receive more relevant offers in self-service.
How do I tell a simple chatbot apart from an AI agent?
Classic chatbots follow predefined dialogue paths and can execute only limited actions. AI agents understand more complex goals, make independent decisions, orchestrate multiple tools, and execute multi-step workflows – for example, research, analysis, and triggering a transaction all within a single process.
Will AI agents and copilots replace my sales staff?
No. In practice, copilots and agents support sales and service by handling routine tasks and delivering information faster. Strategic negotiations, complex advisory, and relationship management remain with humans – AI handles the preparation and background workload.
What are good first use cases for AI agents in B2B?
Typical starting points include automated product recommendations, support for quote creation, order automation for C-parts, ticket classification in service, or localization tasks in international shops. The key is to start with a clearly scoped use case and measure the impact against concrete KPIs.
Start making it real
AI agents and copilots are no longer a future scenario. Discover how Intershop helps B2B companies integrate smart AI solutions directly into their commerce processes – practical, scalable, and tailored to your requirements.
