The introduction of AI into search algorithms and platforms is fundamentally changing the way decisions are prepared: customers no longer approach a company step by step and across all channels. Instead, they receive structured and personalized decision templates on upstream platforms. This is because AI systems bundle information, compare options and make a pre-selection. As a result, the customer comes into direct contact with the company much later in the decision-making process. The company's marketing and sales departments therefore have noticeably less influence on the decision-making process. This leads to the central question:
How can companies continue to effectively influence the customer selection process under these conditions?
When optimizing the customer journey is no longer effective
It is precisely in phases of more difficult sales conditions that companies invest specifically in optimizing their own influence along the customer journey, for example by expanding digital touchpoints or intensifying sales channels and sales campaigns. The assumption: better control leads to more demand.
However, this is precisely where a break occurs. Customers are using AI-based search systems, platforms and increasingly AI applications to make a pre-selection without switching channels. This shifts the pre-selection of leads to these systems: AI not only pre-qualifies leads, but also ensures that leads meet the company in a later decision-making phase. Sales discussions only begin once key decisions have already been made.
Analyses of B2B purchasing behavior, including those by Gartner, confirm that a large part of the decision-making process takes place before direct contact. At the same time, developments at Google and Zero-Click-Analyses illustrate that decision-relevant information is increasingly available directly in upstream systems. This means that companies can no longer influence which providers are selected, but only how potential customers perceive them within this selection.
Companies must rethink the customer relationship
As an interim conclusion, it can be said that the customer journey remains relevant: The customer journey remains relevant, but is losing its role as the primary control framework. And this is particularly noticeable in phases of difficult sales conditions. The decisive factor is no longer just the optimization of touchpoints, but the question of how companies are visible and taken into account in upstream, AI-driven decision-making processes.
Step 1: Create transparency about the upstream decision-making phase.
Companies must first understand where and how customers orient themselves. Companies need to understand the contexts in which customers prepare decisions. Only then will it become clear where and how they are taken into account in these processes. Based on this, companies must align their measures in such a way that they can provide customers with relevant information and guide them through their decision-making process in a targeted manner. Analyzing the relevant information contexts is key:
- What questions do customers ask?
- What criteria are decisive?
- Where do initial preselections occur?
In practice, this perspective is often missing. Companies optimize their touchpoints without taking into account what happens beforehand. Under the influence of AI, this deficit becomes a structural disadvantage.
Step 2: Realign your content strategy.
Content must deliver decision-relevant information - not just generate attention. To achieve this, content must be structured in such a way that it can be processed by systems, integrated into different decision-making contexts and presented in a way that customers can understand (Generative Engine Optimization, or GEO for short). This requires clear structures, comparable features and direct answers to key questions. At the same time, content must cover different customer decision-making situations: from orientation to after-sales. The focus is shifting from channel-specific campaigns and rankings to permanently available, structured information offerings.
Step 3: Combine content strategy with data strategy.
The quality of content is directly dependent on the quality of the underlying data. Only structured, consistent and up-to-date information is taken into account in decision-making processes. Product data, service descriptions and use cases must be prepared in such a way that they can be processed across systems and played out in context.
In many companies, this information is fragmented. Inconsistency and incomplete answers to customers' questions are the result. In the worst case scenario, the AI recognizes these deficits and sorts out the information - the company is not taken into account in the decision-making process. It follows from this: Data strategy and system architecture must be aligned in such a way that they can provide consistent, stringent and usable information.
Step 4: Interlink marketing, sales and service more closely.
The previous steps show: The requirements cannot be solved within individual functions. Marketing, sales and service often work separately - with different data and perspectives. As a result, the external presentation lacks content-related links between brand, product and service, leaving customers with unanswered questions. Often intended as a strategy to persuade customers to make contact, this can become a problem in the AI decision space.
It is only through the cross-functional connection of the different perspectives on the customer that a coherent overall picture of the company's brand value, product structure and service is created. Without this interlinking, information remains fragmented and loses its impact in upstream decision-making logic.
Step 5: Realign measurability.
As significant parts of decision-making take place outside of your own sphere of influence, established KPIs fall short. Conversion rates or funnel analyses only reflect the part of the decision-making process that takes place within your own systems - but not the upstream selection process. In order to manage their own market effectiveness under these conditions, companies need to expand their measurement logic. The key question here is whether and how a company is taken into account at all in upstream decision-making processes.
In concrete terms, this means that Companies must be able to use AI Visibility Monitoring (continuous tracking of AI responses) to understand
- in which contexts and systems they appear,
- with what information they are presented and
- on what basis they are compared with competitors.
Only when this transparency is given can it be specifically influenced that content is taken into account in AI-supported selection and recommendation systems. This makes measurability a prerequisite for understanding the upstream decision-making phase - thus closing the circle to the first step.
Starting points for companies
On the one hand, the decisive change lies in the fact that customers only enter the customer journey defined by companies at a later stage due to upstream decision-making processes. On the other hand, companies need to check whether their existing communication and sales processes are compatible with the upstream decision-making processes. Later contact with leads and existing customers changes the requirements for content, interaction and organization.
In the short term, the focus is on remaining capable of acting under AI conditions. This means in particular:
- Aligning content with upstream decision-making processes, so that they gain relevance
- Consider later entry points and preconceived opinions in the design of the customer journey
- Marketing, Sales and service in order to ensure a consistent and connected customer experience
In the long term, companies must create the strategic and structural conditions so that they can sustainably take into account the growing use of AI in customer decision-making processes.
- Build a cross-functional and rolling content and data strategy to optimize and adapt the connectivity of content
- Align the system architecture with a cross-functional and communicative database, so that context-relevant information can be played out
- Create clear responsibilities and processes for content and data
Conclusion: Rethinking market effectiveness
Customer journeys remain relevant. However, they are no longer the starting point for decision-making. A growing proportion of customers are making decisions outside of their own organization. Internal optimization of campaigns, touchpoints and channels alone is no longer sufficient, especially in phases of difficult sales conditions. The decisive lever lies in the ability to connect to upstream decision-making processes - in the short term through the closer integration of functions and an understanding of how customers make their decisions, and in the long term through a consistent data and system basis.
The central question is no longer how companies design their touchpoints - but how they remain visible at all in the AI-driven decision-making process via a content and data strategy.
