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Artificial Intelligence in Sales: Chatbots and Customer Profiles


Digitalization affects different areas of companies and is a complex issue. It is also a hot topic in research: Fischer et al.’s (2022b) literature review on articles published in 134 journals found that publications related to this area increased drastically since 2019.

In this article, we will focus on AI as one potential aspect of digital sales. It could be said that AI interprets data, learns from it, and exhibits flexible adoption. Due to the characteristics of AI, sales could be considered a department in which AI would have a meaningful impact as it can influence sales processes, customer service, and customer behaviours. AI, as a technology, exhibits human intelligence and emulates intelligent human behaviour in an online environment “based on their pattern recognition ability, allowing the tools to demonstrate intelligence by acting or reasoning” (Rusthollkarhu et al., 2022, p. 241). According to Davenport et al. (2020, p. 26) it “relies on several key technologies, such as machine learning, natural language processing, rule-based expert systems, neural networks, deep learning, physical robots, and robotic process automation”. Therefore, it needs a significant amount of data for training.

AI may take care of repetitive tasks

According to the existing literature, one of the most promising advantages of AI lies in automation processes (Singh et al., 2019), as well as enabling different forms of interactions, with huge potential in areas like profiling. However, despite the various advantages of AI, there are also several associated challenges for sales professionals, especially in re-defining the roles and the number of “needed” humans in sales (Singh et al., 2019). To address this, we provide two use cases based on the study by Fischer et al. (2022a) in the following sections.

Customer profiles

A customer profile can contain what, how and why a customer made a purchase decision and what its consequences are. For example, customer profiles can contain browsing behaviour, demographic characteristics, contact details, interactions with the company or on social media platforms. This knowledge can be used in many ways to support sales functions. Companies could use AI (e.g., to gather the most relevant data to find prospects that may be interested, collect data about leads and help them predict their potential via natural language processing or image recognition (Rusthollkarhu et al., 2022).

Furthermore, AI can help improve market research, e.g., by classifying market survey responses automatically based on sentiments and quality (Rusthollkarhu et al., 2022). The information gathered could also be used to improve customer segmentation to assess customer performance and potential for future sales (Davenport et al., 2020).

AI can crawl through vast amount of information and come up with suggestions for customers

For internal usage, companies could use past data on salespersons’ interactions and profiles to identify the required competencies, develop ideal employee profiles to improve salesperson training and match these profiles with different customer groups (Singh et al., 2019). One issue here could be that AI needs a vast amount of training data that in B2B might not be available do derive meaningful customer groups.


Lately, there has been a lively debate surrounding ChatGPT, a tool that is able to write entire articles and solve coding issues. This means that chatbots could help in writing offers or technical documentation. While the ChatGPT tool is relatively more capable when compared to “simpler” chatbots, the latter can provide customers with 24/7 support for non-complex questions, currently found in Q&A on websites. In particular, chatbots are very useful for answering standard questions immediately and accurately (Singh et al., 2019).

Therefore, such a tool would allow the customer to receive product recommendations and additional information independently and without the help of a salesperson (Bongers et al., 2021), which would even be documented automatically. This indicates that such chatbot interfaces can help in guiding prospective and existing customers throughout the customer journey (Rusthollkarhu et al., 2022). In addition, they can be used to make initial contact with prospective customers in certain cases (Davenport et al., 2020).

AI can take over standard conversations in different languages

As this short overview revealed, AI offers meaningful solutions for every phase in the sales process. Currently, most companies focus on a narrower view of AI and how it may help replace routine tasks. There are now initiatives in several companies to utilize AI in screening contracts or calls for tenders to focus on the most relevant parts of the documents like legal issues or technical contact. By doing so, AI can help make offering much more efficient as often tenders include more than hundred pages of information. Humans then could focus on the most important parts of these documents and save a lot of time for drafting the offer or communicating with customers.

However, we identified some applications that already make use of AI in a more general sense. While salespeople currently interact directly with prospective clients to give them advice or to acquire them as customers, this may change in the future as AI becomes more prevalent. For instance, AI applications can interpret human emotions to give support to salespeople. A bot may also be able to imitate the behaviour of salespeople in order to converse in real-time with customers in a way that matches their own behaviour. It also may be possible to analyse the vast amount of information often provided by customers and then summarize it or highlight certain points.

For successfully applying AI in Sales, companies must take a strategic view and take into account technological, organizational and human aspects of this change

One of the main issues in applying them in an organization according to Enholm et al. (2022, p. 1730) “is a lack of a coherent understanding of how AI technologies can create business value and what type of business value can be expected”. At present, several companies simply apply some form of AI in some part of the sales process without systematically analysing it or deriving a general framework with concrete applications. Evidence suggests that it is important to follow the customer journey approach when deriving such a framework. When applying such an approach, one must keep in mind that this customer journey also undergoes changes due to digitalisation as customers are becoming more informed and “the purchase phase has significantly shortened” (Ahearne et al., 2022, p. 29).


M. Ahearne, Y. Atefi, S.K. Lam, M. Pourmasoudi, 2022, “The future of buyer–seller interactions: A conceptual framework and research agenda”, Journal of the Academy of Marketing Science, vol. 50, no. 1, pp. 22–45.

F.M. Bongers, J.H. Schumann, C. Schmitz, 2021, “How the introduction of digital sales channels affects salespeople in business-to-business contexts: A qualitative inquiry”, Journal of Personal Selling & Sales Management, vol. 41, no. 2, pp. 150–166.

T. Davenport, A. Guha, D. Grewal, T. Bressgott, 2020, “How artificial intelligence will change the future of marketing”, Journal of the Academy of Marketing Science, vol. 48, no. 1, pp. 24–42.

I.M. Enholm, E. Papagiannidis, P. Mikalef, J. Krogstie, 2022, “Artificial Intelligence and Business Value: A Literature Review”, Information Systems Frontiers, vol. 24, pp. 1709–1734.

H. Fischer, S. Seidenstricker, T. Berger, T. Holopainen, 2022a, “Artificial intelligence in B2B sales: Impact on the sales process”, Artificial Intelligence and Social Computing, vol. 28, pp. 135–142.

H. Fischer, S. Seidenstricker, J. Poeppelbuss, 2022b, “The triggers and consequences of digital sales: A systematic literature review”, Journal of Personal Selling & Sales Management, pp. 1–15.

S. Rusthollkarhu, S. Toukola, L. Aarikka-Stenroos, T. Mahlamäki, 2022, “Managing B2B customer journeys in digital era: Four management activities with artificial intelligence-empowered tools”, Industrial Marketing Management, vol. 104, pp. 241–257.

J., Singh, K. Flaherty, R.S. Sohi, D. Deeter-Schmelz, J. Habel, K. Le Meunier-FitzHugh, A. Malshe, R. Mullins, V. Onyemah, 2019, “Sales profession and professionals in the age of digitization and artificial intelligence technologies: Concepts, priorities, and questions”, Journal of Personal Selling & Sales Management, vol. 39, no. 1, pp. 2–22.

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