Why this is a B2B issue
In B2B, visibility in the traditional web sense has never determined success or failure. Internal processes, contracts, supplier relationships, and the complexity of products and services have always been more decisive.
This is precisely why current developments in AI are so relevant for B2B. What is changing is not primarily the channel, but the way decisions are prepared.
The central question is: If systems are getting better at selecting, what will determine who gets selected in the future?
A quick recap from issue 1
In the first issue, we analyzed what open standards like the Universal Commerce Protocol can achieve and where their limits lie. The key takeaway was: standards create interoperability, but they do not replace governance.
Before AI can act, it must be clear what it is allowed to understand, compare, and recommend. Building on this, we are now looking at the next level: decision preparation.
B2C as a benchmark: Selection and action are already technically possible
In B2C, this development is already well advanced. In many search and shopping contexts, about every second query is now answered without the need for a further click, as AI responses provide information, comparisons, and concrete recommendations directly.
With initiatives like "Buy it in ChatGPT," it is becoming clear that AI systems can already map research, selection, and transactions into a seamless flow. Users compare options, make decisions, and complete purchases within an AI dialogue.
This is not a vision, but a functional level of maturity. For B2B, this is less of a direct target model, but a clear indication of how advanced the technology already is.
What connects and clearly separates "Buy it in ChatGPT" and UCP
The comparison between "Buy it in ChatGPT" and UCP is frequently made, but it only holds up if you clearly distinguish between the different layers.
"Buy it in ChatGPT" demonstrates very concretely that AI-supported commerce up to the point of transaction already works today when a provider controls the entire process, the data, and the responsibility.
UCP takes a different approach. It is not a purchasing flow or a product, but an attempt to define a common structural foundation so that such agentic flows can become possible across organizational and system boundaries.
In this sense, both answer the same question from different directions. ChatGPT shows what is technically possible. UCP addresses what must be solved organizationally, systemically, and responsibly to achieve it.
How AI prepares decisions today
In B2B, too, AI-supported decision preparation has long been a reality—often less visible, but operationally effective.
AI systems are currently being used to:
● understand unstructured requirements
● Semantically interpreting requirements
● Comparing relevant options
● And pre-structuring decision options
The crucial point here is not autonomy, but pre-structuring. AI does not make decisions, but it increasingly determines which options are even available for consideration.
Practical example: Email-to-Order
A very common B2B use case is Email-to-Order.
Customers send order or quote requests via email, often unstructured, with references to previous orders, individual terms, or technical details. AI-powered systems read these requests semantically, identify products, quantities, and contexts, map them to internal items, customer accounts, or contracts, and check them against pricing and authorization rules.
The result is not an autonomous order, but a structured proposal in the ERP or order system, including flagged discrepancies or items requiring clarification.
At Reply, we have implemented such Email-to-Order flows for clients multiple times. In practice, it becomes very clear: the greatest leverage lies not in automation itself, but in the quality of decision preparation. This is where measurable effects such as shorter lead times, fewer errors, and more consistent decisions are generated.
Email-to-Order is strategically relevant because it applies the exact same logic as agentic commerce, but with a clearly defined control boundary.
What is really shifting
The central shift is not that AI provides better recommendations. It is that selection itself is becoming a system process.
In a world where systems pre-select, compare, and prioritize, competition is no longer determined solely by visibility or price, but by a new question:
How well is a company prepared for selection?
This concerns not only data, but also:
● How clearly rules are formulated
● How consistently decisions are made
● How compatible information is for other systems
Email-to-Order, AI-supported sourcing shortlists, or prepared order suggestions are not peripheral phenomena. They are early indicators of how selection will be organized in the future.
Those who invest here are not investing in automation, but in decision-making capability.
Final thoughts
When choices are increasingly prepared by systems, the decisive question is no longer:
How visible are we?
But rather:
How well can we be explained, compared, and aligned with rules for machine-based selection?
Which of your current decisions are already described clearly enough that a system could reliably prepare them for you?
And where would you notice that it is not technology that is lacking, but rather clarity regarding rules, data, and responsibility?