Generative AI has arrived in the everyday sales work of many mid-sized companies, but often without a clear structure. What matters is not the technology, but the question of where in the process AI should act and where human responsibility is deliberately retained.
Generative AI has arrived in the everyday sales work of many mid-sized B2B companies. Tools like ChatGPT are used to write copy, condense information or follow up after conversations. In practice, this often happens informally, without clear rules and without organizational embedding into existing workflows.
The central problem lies less in the technology itself than in the missing structure. AI is deployed without clearly settling where in the sales process it should act, which tasks it takes over and where human responsibility is deliberately retained. In an increasingly volatile competitive environment, this is no longer an innovation topic, but a question of operational capability.
AI Amplifies What Is Already There
AI does not replace processes, it amplifies what is already there. Functioning workflows become more efficient, while messy or unclear structures become more visible and more problematic. Anyone who wants to integrate generative AI sensibly therefore benefits first from an analysis of the actual sales work. Sales does not consist of one uniform block of activity, but of many different tasks: customer conversations, condensing information, creating proposals, internal coordination or maintaining CRM systems. AI unfolds its value not at the level of sales, but at the level of these individual activities, especially where work is language-driven, repeatable or heavily formalized.
Concrete Goals Instead of Abstract AI Use
What matters is clarifying concrete objectives. The goal of using AI stays abstract and not very helpful. More meaningful are goals directly tied to operational workflows, such as reducing the time spent on proposal drafts, consistent documentation of conversations or more reliable follow-ups after customer meetings.
Assign Responsibility Clearly
The researcher Ethan Mollick distinguishes four categories: activities that deliberately stay with humans (human only), tasks in which AI serves as a cognitive sparring partner, delegated activities in the sense of a human-in-the-loop approach, and fully automatable tasks. Certain activities such as leading central customer conversations, prioritizing accounts or strategic decisions deliberately remain in human hands. This is not a statement about the technical capabilities of AI, but a normative decision about where companies want to locate responsibility and final decisions.
In addition, there are tasks in which AI produces preliminary work that must be reviewed and taken responsibility for, such as conversation summaries, proposal wording or follow-up texts. Other activities can be fully automated, provided they are clearly standardized and rule-based, for example filling predefined CRM fields.
Processes First, Then Tools
Only when tasks, responsibilities and control points are clearly defined can robust use cases be developed. The selection of suitable tools comes second. In practice, this order is often reversed: applications are introduced before their place in the workflow is defined. What is decisive is not the abundance of features in a system, but its fit to clearly described workflows.
Large language models such as ChatGPT or Google Gemini are strong at processing and generating language. They are suitable where content is to be structured, condensed or varied. They are unsuitable where they are misunderstood as a reliable source of knowledge or a decision-making authority. They produce plausible answers based on statistical patterns, but they have no understanding of truth of their own.
Enabling the Workforce
What is decisive when introducing AI is enabling the workforce, for example in working with prompts. Good prompting is often misunderstood as specialized technical knowledge, but it is less about polished wording than about structured thinking. Anyone who uses AI has to describe tasks precisely: what role should the AI take on? What goal should be achieved? What information is available? Vague prompts inevitably lead to vague results.
The success of generative AI in B2B sales is not decided by the performance of the models, but by their organizational embedding. Those who systematically reorganize work, distribute responsibility clearly and involve leadership create the basis for lasting productivity gains. The next part of the series addresses the question of how companies can use generative AI responsibly: legally secured, organizationally controlled and without endangering the integrity of their data.
