{"id":991572,"date":"2026-04-16T14:30:28","date_gmt":"2026-04-16T14:30:28","guid":{"rendered":"https:\/\/customersx.ch\/ai-in-sales\/"},"modified":"2026-06-14T22:54:02","modified_gmt":"2026-06-14T22:54:02","slug":"ai-in-sales","status":"publish","type":"post","link":"https:\/\/customersx.ch\/en\/ai-in-sales\/","title":{"rendered":"AI in sales"},"content":{"rendered":"\n

AI, or artificial intelligence, is said to have great potential in companies. So far, compared with other departments, sales has been little digitalised. That’s why the question arises: what does the potential of AI in sales look like?<\/p>\n\n

Fundamentally, two types of AI must be distinguished: generative AI and predictive AI. Generative AI is used to create new content: text, videos, music, etc. Predictive AI analyses data and enables predictions about future developments. Although generative AI is getting a lot of attention at the moment, we see predictive AI in particular as a major lever for sales. Besides the different types of AI, the place of use must be distinguished: front end and back end. Why is this distinction so important? Because digitalisation in sales has so far focused too strongly on the front end. Front end refers to customer interactions. The classic example is the chatbot. Customers can submit enquiries to the company, and AI-supported chatbots are getting better and better at answering them. Sounds very good \u2014 if only an average of 5% of customers in Switzerland actually used a chatbot. Not to mention satisfaction with the tools. The back end, on the other hand, focuses primarily on data. This is where the greatest potential lies, but also the greatest challenge for companies to use AI successfully in sales. For example, the current data quality in most companies is too low to use AI. The systems are outdated, often not yet in the cloud, and disconnected. Thus the IT ecosystem is not yet AI-ready. In the following, the different use cases are presented and an assessment is made based on our AI consulting<\/a>.<\/p>\n\n

How can AI be used in sales?<\/h2>\n\n

At the moment, we see that AI in sales focuses mainly on the front-end case of the chatbot. There are numerous providers that try to convince even in Swiss German. Drift is one example. In principle, there is nothing wrong with this kind of AI application. But it should be critically examined whether this is currently the optimal entry point into AI. Because it ties up resources, many discussions are held on the basis of uncertainties, and few customers will use this tool. That’s why we recommend entering AI in sales at the back end.<\/p>\n\n

Use cases for AI in the back end are: <\/strong><\/p>\n\n

    \n
  1. Automation of routine tasks<\/li>\n\n\n\n
  2. Process automation<\/li>\n\n\n\n
  3. Data analysis<\/li>\n\n\n\n
  4. Sales enablement<\/li>\n<\/ol>\n\n

    (1) Numerous providers advertise support with automating routine tasks, such as data entry, email communication and order management. AI now offers a certain efficiency-gain potential here. For example, email wording is optimised and sending at the optimal time is enabled. Apollo is one example. A survey of 1,400 people found that this use case is seen as the most important. Such an application supports employees but also holds limited profit-increase potential. In addition, it must be ensured that the content towards customers still retains a personal touch. So it is, after all, surprising that this use case is rated so highly.<\/p>\n\n

    (2) Process automation is another area of application. AI can suggest improvements to existing processes. This presupposes that the processes are digitally documented and that the individual process stages are also checked via KPIs. Usually, in connection with the chatbot, the focus is ‘only’ on better customer interactions. In companies, this is an optimisation opportunity due to missing responsibilities and other reasons. AI can help here. The truly deep integration of AI into sales processes presupposes (except in e-commerce) that every employee provides the company with very precise information about individual process steps. Here we see a major challenge in getting salespeople to perform this activity. A feeling of being monitored quickly arises. So it is a possibility in principle, but in our view not the starting point for AI in sales.<\/p>\n\n

    (3) Data analysis can be carried out by AI. Several points must be considered, however:<\/p>\n\n

      \n
    1. The relevant systems are connected in the cloud<\/li>\n\n\n\n
    2. Valuable attitudinal data is also collected<\/li>\n\n\n\n
    3. Sales has data collection as a bonus component in their remuneration<\/li>\n\n\n\n
    4. A dedicated data system is in place<\/li>\n\n\n\n
    5. A dedicated data AI is introduced and constantly developed further.<\/li>\n<\/ol>\n\n

      In our view, data analysis offers the greatest potential for AI in sales. But since the starting position is usually not ideal, we recommend that companies concentrate all their AI activities here now. Even if points 1 and 2 are simpler and faster to implement, data analysis is the core of future success. Data analysis using AI can support the following points:<\/p>\n\n

        \n
      1. Lead scoring<\/li>\n\n\n\n
      2. Sales forecasting<\/li>\n\n\n\n
      3. Churn prediction<\/li>\n\n\n\n
      4. Cross-\/up-selling potential<\/li>\n<\/ol>\n\n

        Microsoft and Salesforce in particular are currently outdoing each other with the capabilities of their systems. Companies should critically examine whether the dependency, in combination with the quality of the systems and the costs, is optimal. We recommend building your own data platform and establishing your own AI for data analysis that supports the individual sales goals precisely. Lead scoring and forecasting are the rather simpler tasks. It should be noted, however, that it takes many customers, salespeople and orders so that the calculations are not too error-prone. Churn prediction is already considerably more complex to model via AI. The greatest challenge, but also the greatest potential, lies in cross-\/up-selling. Here we observe that the large B2B manufacturers in Switzerland have now established their own developments, or are in the process of establishing them, in order to be more successful in sales. For smaller companies, we can therefore only recommend taking this path too and, despite the big promises of the many CRM and automation providers, taking data analysis into their own hands.<\/p>\n\n

        (4) Sales enablement builds on data analysis and connects forecasts with content suggestions. The applications are still very basic but can already provide support. One example is Gong or Exceed. It will certainly take some more time here to determine which implementation is to be recommended. Too much AI, especially in content creation, can also mean that customers feel they are interacting with a machine. That’s why more experience is needed regarding the optimal combination of AI and salesperson. We currently recommend focusing on data analysis, because this means the most time for implementation and enables the greatest profit lever.<\/p>\n\n

        Overall, AI is a great support for sales. Cross-selling in particular \u2014 which many companies have so far hardly been able to implement \u2014 can be tackled systematically with AI, even for small companies. One challenge lies in the technical implementation. Are standard systems really as powerful as currently announced on the websites, and how is the ROI of these systems to be assessed? Here we see a major danger that companies enter into a dependency out of fear of falling behind.<\/p>\n","protected":false},"excerpt":{"rendered":"

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