This is business-critical for B2B companies. If information is processed systemically, decisions are prepared on the basis of data and procurement processes are increasingly shaped by agents, operational connectivity becomes a competitive factor. Agentic commerce is often described as a development along an automation curve in which more and more parts of the commerce journey are delegated to systems.
Operational AI is not simply the next stage of automation. It changes how companies become visible, capable of making decisions and connected to agentic procurement processes.
Many companies use AI - but only a few make it operationally effective
AI is already visible in many companies: in content creation, research, analysis, translation or the automation of individual tasks. This often generates rapid productivity gains. But productivity alone is not an operational transformation. The real leverage here lies not in the isolated use of individual tools, but in the reorganisation of the operating model. McKinsey comes to a similar conclusion and names six key dimensions for scaled AI value creation in its current ["The State of AI" report]*: strategy, talent, operating model, technology, data, adoption and scaling.
This difference is particularly clear in B2B. Complex approvals, individual price and role models, evolved system landscapes and high data quality requirements make it unlikely that isolated AI experiments alone will generate a sustainable impact. Shopware therefore describes stable foundations in data, system landscape, governance and operating model as a prerequisite for scaling, AI and future intelligence in commerce.
*[Editorial source: McKinsey, "The State of AI: Global Survey 2025";]
The state of AI in 2025: Agents, innovation, and transformation
What distinguishes operational AI from selective AI use cases
Operational AI does not simply mean using more AI in the company. It refers to a level of maturity at which AI reliably supports recurring decisions, is embedded in existing workflows, works on reliable data and operates within clear rules. The above-mentioned study describes this form of value creation not as an individual measure, but as the result of an interplaying management system comprising strategy, operating model, data, technology, talent, adoption and scaling.
In the B2B context, this means that AI must be compatible with product data, availability, pricing logic, role models, approvals, content structures and governance. As soon as it is reliably linked to day-to-day business in this sense, a use case becomes part of the operating model. This is precisely the aim of Shopware's argument for B2B Commerce 2026.
Why isolated AI experiments in B2B reach their limits
The typical limit is not a lack of interest, but a lack of integration. One team uses AI for content, another for analyses, a third for support or product data. However, if these applications access different data sets, are not connected to core processes and do not have a common governance framework, the overall effect remains limited. This is precisely why Shopware is placing the operating model so strongly at the centre of the current B2B debate.
With regard to agentic commerce, the pressure is also increasing. Commerce is gradually developing in the direction of delegated, partially automated and autonomous interactions. This means that information, processes and offer logics must be structured in such a way that systems can process them reliably. What seems like data hygiene today will therefore become a business-critical prerequisite for future procurement processes. This is derived from the description of the automation curve and the growing role of agent-based interactions in commerce.
The CTO perspective: Operational AI needs integration logic, data and governance
From the CTO's point of view, operational effectiveness does not come from additional tools, but from reliable links. AI must be integrated into system landscapes, work with consistent data and operate within clear responsibilities. Only then will results be repeatable, traceable and scalable. Organisations that derive more value from AI work more systematically along defined management practices across operating model, data, technology and scaling.
Simon Neuberger,
CTO at elio GmbH
For B2B companies, this means in practice: clean data models, integrated process logic, clear human-in-the-loop rules and an architecture that not only generates short-term efficiency gains, but also operational resilience. This is precisely why the operating model becomes the real question of maturity. This point is emphasised by the demand for an AI-enhanced operating model that brings together people, AI, roles and governance.
The marketing perspective: Operational AI changes go-to-market, communication and prioritisation
Operational AI is not just a topic for IT or operations. It also changes how companies prioritise topics, structure content, develop demand and build trust. In the marketing context, this means that the real AI leverage lies not in isolated tools, but in the reinvention of the marketing operating model. Anyone who only sees AI as a productivity tool is falling short.
Data quality, message consistency, clear service descriptions and structured content will become operational requirements in marketing in the future. If you want to be understandable, credible and connectable in the market, you not only have to communicate for people, but also become interpretable for systems, search models and, in the long term, agents. In the environment of agentic commerce, visibility is therefore shifting from pure presence to machine-readable relevance. As a result, marketing under AI is moving more towards responsibility for decision-making, orchestration and operating models.
Volker Riedel,
Head of Marketing at elio GmbH
As a result, marketing is becoming more part of the operating model: not just as a sender of communication, but as a function that helps build relevance, comprehensibility and trust signals in an AI-influenced market logic. This is also shown in [the current article]* by the Boston Consulting Group (BCG)
*[Editor's note: Boston Consulting Group (BCG), "AI Transformation Is a Workforce Transformation"]
From Campaigns to Business Value: How AI Will Transform Marketing
From experiment to operating model: how companies recognise maturity
The transition to operational AI cannot be recognised by the number of tools used. It can be recognised by whether AI has a recurring effect in core processes. The first level of maturity is reached when individual tests become stable sub-processes. The next is when these sub-processes are connected to shared data, roles and governance. Operational AI only becomes fully effective when it improves decisions, quality and speed in several areas of the company simultaneously without creating new friction. This categorisation is a conclusion for scaled AI value creation.
You can therefore recognise a company with a high degree of maturity not by the fact that it talks a lot about AI. Instead, it is recognised by the fact that AI is reliably embedded in processes, works on the basis of reliable information, is controlled in a comprehensible manner and generates a consistent impact from the market side to the system side. Operational AI is therefore not a communication label, but an expression of operational discipline.
Conclusion: Operational AI starts with operational clarity
Operational AI is not a buzzword or an additional technology level. It is a level of maturity. Companies do not achieve it by using as many AI tools as possible, but by structuring data, processes, systems and decision-making logic in such a way that they have a stable, repeatable effect.
In B2B in particular, this operational anchoring determines whether AI only accelerates individual tasks or actually changes the performance of a company. When information is structured consistently, processes are integrated and decisions are prepared on the basis of data, an infrastructure is created that simultaneously improves efficiency, speed and market access.
If you would like to check how far your company is on this path, it is worth taking a look at our services as elio's AI agency. We support companies in developing concrete AI projects from initial ideas - from analysis and implementation to integration into existing processes, with the aim of achieving a measurable impact in operational business.
*Quelle: McKinsey, „The State of AI: Global Survey 2025“
**Quelle: Boston Consulting Group (BCG), „AI Transformation Is a Workforce Transformation“
