2026 – New year, familiar trends!? Automation with AI in B2B commerce

Current projects that communicode is implementing for B2B companies clearly show that traditional process models are increasingly reaching their limits. At the same time, demands for efficiency, scalability, and transparency are rising, while human resources remain limited. Today, digital commerce processes in B2B must not only function, but also adapt dynamically to market conditions, customer behavior, and organizational structures.

How companies intelligently optimize their commerce processes

Holistic AI-supported process automation offers a response to these challenges. It combines traditional automation approaches with the capabilities of artificial intelligence, opening up new possibilities for optimizing complex commerce processes in the B2B environment.

In this article, we answer the key questions:

• What does process automation with AI mean in B2B commerce?
• What are the benefits in practice?
• How does AI-supported automation work along the commerce value chain?
• What trends and success factors should companies be aware of?

What does process automation with AI mean in B2B commerce?

Process automation with AI refers to the use of artificial intelligence for the automatic execution, control, and continuous optimization of business processes. Unlike purely rule-based automation, AI systems are able to learn from data, recognize patterns, and make decisions even under uncertain or changing conditions.

In communicode's consulting practice, this means not introducing AI as an isolated technology, but integrating it into (new) business processes and system landscapes. Applied to B2B commerce, this means, among other things:

• the intelligent processing of customer inquiries across various channels,
• the automated creation and review of offers,
• the dynamic prioritization of orders, and
• the adaptive control of processes across multiple systems (e.g., ERP, PIM, CRM, and commerce platform).

Modern, API-based commerce architectures form the technical basis for this. In composable setups, such as those implemented by communicode together with platform partners such as Emporix, predefined AI agents are provided to identify potential fraud cases, handle support requests, or generate translations, among other things.

AI functions can thus be integrated into individual process steps in a targeted manner without creating monolithic dependencies or having to replace entire process chains.

Advantages of AI-supported process automation in B2B commerce

The benefits of AI do not come from individual features, but from measurable effects along the entire value chain.

Efficiency & Scalability
AI systems reduce throughput times by automating manual checks, queries, and transfers. Quotation and ordering processes can be significantly accelerated, especially when there is a high volume of inquiries and many variants.

Quality & Consistency
AI-supported decision-making logic ensures consistent pricing, availability, and condition models across all channels.

Reduced workload for employees
Routine tasks such as data validation, classification, and prioritization are automated. Employees gain time for consulting, strategic, and customer-oriented activities.

Improved Customer Experience
Business customers benefit from faster response times, more transparent processes, and consistent information – trust as a success factor.

AI-supported automation along commerce processes

Data Collection & System Integration
AI systems access data from ERP, commerce, CRM, and PIM systems. In addition to structured data, unstructured content such as emails, documents, or free text can also be processed. This requires a clean integration architecture with clearly defined interfaces.

Analysis & Pattern Recognition
The AI analyzes incoming information using methods such as natural language processing (NLP), predictive analytics, and machine learning. Typical use cases in B2B commerce include the classification of inquiries, the recognition of purchasing patterns, and the forecasting of demand.

Decision & Execution
Based on the analysis, the system makes decisions, such as prioritizing an order or determining the need for manual approval. Implementation is carried out via APIs or automation workflows that are connected to existing systems. This allows, for example, intelligent predictions to be made about actual deliverability, especially for large-volume orders, and, if necessary, adequate complementary products to be offered automatically.

Learning & Optimization
Feedback from practice – such as manual corrections or deviating decisions – is fed back into the models. This allows processes to continuously improve and adapt to changing conditions.

Typical application examples in B2B commerce

The following areas of application have emerged from project experience:

• Quotation processes: Automated prequalification, price proposals, and approvals
• Order management: Intelligent control of orders according to priority, margin, or delivery capability
• Self-service portals: Support for product selection and reordering
• Process analysis: Identification of bottlenecks and media breaks using process mining

These use cases can be combined in a modular fashion and expanded step by step.

Where is automation in B2B commerce headed?

In a highly dynamic environment, conditions change very quickly. From communicode's perspective, the following trends are particularly evident for 2026:

• Agent-based end-to-end automation
• Hyperautomation in B2B (AI, RPA, iPaaS) the combination of AI, RPA, and integration platforms in the sense of hyperautomation, as well as
• The increased use of process intelligence for data-driven optimization
• Low-code/no-code automation for faster implementation, such as n8n or make, enabling automation without programming knowledge

These developments require commerce platforms to be open, modular, and AI-enabled. iPaaS (Integration Platform as a Service) enables precisely this: the integration of applications, data sources, and systems. AI orchestration layers such as MCP (Model Context Protocol) enable standardized and secure exchange between AI models.

Platforms such as Emporix are also increasingly offering their own LLM providers such as OpenAI or enabling the easy integration of customer-hosted models.

Humans and AI – working together, not against each other!

In addition to all the technical, procedural, organizational, legal, and other issues, the key to success lies in a simple realization: AI agents will become new colleagues in the company. The better company managers succeed in connecting AI agents and humans with each other and orchestrating tasks in a meaningful way, the more successful they will be in the market. Transparent communication and early involvement of teams, supported by relevant training and internal standards, is essential.

communicode supports companies in orchestrating humans and AI in a meaningful way and creating acceptance through clear processes, training concepts, and organizational guidelines.

Conclusion: AI automation as a strategic lever

AI-supported process automation in B2B commerce is not an isolated technology project, but rather a strategic lever for increasing efficiency, quality, and scalability. Companies that intelligently automate their commerce processes today are laying the foundation for future-proof business models and a consistent customer experience across all channels.

The key to success is not AI alone, but the interplay of processes, data, organization, and a suitable commerce architecture. Only when these elements are orchestrated in a meaningful way can AI unleash its full potential in B2B commerce.

communicode combines the necessary competencies:

• Deep understanding of B2B commerce processes
• Expertise in composable commerce architectures
• Integration expertise across complex system landscapes
• Practical implementation of AI-supported automation

The result is not an isolated AI project, but a scalable, sustainable AI commerce strategy that creates measurable added value and makes companies competitive in the long term.