21.10.2024

The megatrend of digitalization is transforming B2B sales, particularly through the use of Artificial Intelligence (AI).

AI is currently one of the most discussed developments in sales and has major impacts on buyer-seller interactions. Especially, in combination with automation, AI holds great potential (McClure et al., 2024). Marketing and sales are increasingly adopting AI to enhance their customer-related activities, resulting in an increased need for AI capabilities in these domains (Stanford University, 2024). In 2020, AI in sales was ranked as the second most important usage after automated customer service solutions (Mehta & Senn-Kalb, 2021). Estimates suggest that by 2028, up to 60% of sales tasks could be automated by AI (Gartner, 2023).

AI in B2B Sales

Literature indicates that sales processes vary across industries. Despite these differences, AI applications can support the entire sales processes. AI can automate routine tasks such as data entry, email follow-ups, and lead prioritization. Technologies like machine learning (ML) and natural language processing (NLP) enhance personalization and customer engagement. NLP, for instance, enables AI systems to interpret and respond to customer queries in real-time, improve the overall customer experience. Tools such as generative AI and large language models (LLMs), including ChatGPT, have expanded automation capabilities further, allowing sales professionals to generate personalized content and streamline customer communications (McClure et al., 2024). Potential use cases of AI in B2B sales are for instance the following (T. Davenport et al., 2020; Fischer et al., 2022; Fischer et al., 2023; Paschen et al., 2019; Paschen et al., 2020):

1. Customer Profiles: AI analyzes purchasing behavior and social media data to better understand customer needs and develop targeted offers. These profiles are the basis for personalized recommendations, improving lead management and customer engagement.
2. Chatbots: AI-driven chatbots support both customers and sales teams by answering simple questions and contributing to lead qualification. This automation helps free up sales teams to focus on higher-value activities.
3. Personalized Recommendations: AI-based systems generate tailored product suggestions based on customer profiles, allowing for more personalized sales interactions and targeted offers.
4. Dynamic Pricing: AI enables dynamic pricing strategies, adjusting prices based on real-time customer data and market conditions, such as a customer’s willingness to pay.
5. Order Processing and Automation: Post-contract, AI can automate entire processes, from order handling to payment processing, enhancing operational efficiency.
6. Emotional Support: AI can assist sales staff in real-time by analyzing facial expressions and voice tones, providing insights into customer emotions and helping adjust communication strategies accordingly.

The use cases point out that AI is particularly helpful for routine tasks in the sales process and coaching salespeople along the sales process. The phases of information gathering and customer care can be fully automated by AI. However, for more complex tasks, such as overcoming objections or closing deals, human salespeople are necessary. Profiles play a key role as they form the basis for many AI applications. Moreover, the use cases show that integration of AI into sales offers numerous advantages, including increased efficiency, enhanced decision-making, and improved customer experiences.

AI is particularly helpful for routine tasks in the sales process

AI tools can prioritize leads, generate actionable insights, and automate repetitive tasks. Furthermore, AI’s ability to deliver personalized customer experiences can drive higher customer satisfaction and loyalty.

Implementation and challenges

To implement AI, companies should take a step-by-step approach. It is important to start initiatives within the company that enjoy the support of the its top-level managers. While first applications can be very simple and designed as stand-alone to gain experience, companies should move forward to integrate AI with their existing systems (T. H. Davenport et al., 2021). Principally, the design of AI applications is defined by two determinants: the level of relationship with customers and a seller company’s level of process. The customer relationships can range from transactional, which is considered as a low level, to trusted co-creator, which is considered as a high-level relationship. The process can have ad hoc, informal, formal, agile, and customized patterns. The more sophisticated the relationship and the more sophisticated the process, the higher the value achieved with AI applications (Dickie et al., 2022).

However, there are challenges associated with AI adoption in sales. One significant issue is the “black box” problem, making it difficult for sales professionals to fully understand or trust AI-generated recommendations (Floridi et al., 2018; Rai, 2020). Additionally, while AI can automate routine tasks, it lacks the emotional intelligence and human connection necessary for building strong relationships. Over-reliance on AI could lead to impersonal interactions that may disappoint customers (Oritsegbemi, 2023).

Conclusion

Undoubtedly, AI will play an increasingly central role in B2B sales. Particularly in combination with big data, companies can increase their efficiency and make more precise, data-driven decisions (Paschen et al., 2019; Sinisalo et al., 2015). However, the implementation of AI must be carefully planned and aligned to the needs of both salespeople and customers. Companies should start building capabilities early and develop a clear strategy for utilizing AI in sales to fully leverage the advantages of this technology and achieve competitive advantages. AI in sales shouldn’t developed as an isolated solution but embrace the whole organization and be combined with other digital technologies.