Implementing Artificial Intelligence (AI) in Sales
How you can successfully implement AI in your B2B sales
The rapid development of artificial intelligence (AI) has revolutionized B2B sales and created numerous opportunities for companies. By choosing the right AI application, companies can strengthen their sales, take a leading role in the market and achieve their business goals in the long term.
Artificial Intelligence in Sales
The rapid development of artificial intelligence (AI) has revolutionized B2B sales and created numerous opportunities for companies. AI has developed tremendous potential for nearly every industry in recent years. According to a McKinsey survey, the use of AI has more than doubled since 2017. For sales, AI applications also offer precise solutions for a wide range of areas. These include the reduction of routine tasks, the prediction of customer needs or the optimization of sales processes – in other words, all innovative solutions that can provide valuable support to sales staff.
Possible uses of AI in sales
The potential uses of AI in B2B sales are many: AI can take over tedious and time-consuming routine tasks, such as data visualization, research and personalized emails. This frees up sales reps’ time for strategic activities and interpersonal customer contact. AI systems analyze vast amounts of data to better understand customer behavior and make predictions about future needs, enabling the development of tailored solutions and a more customer-centric sales strategy.
AI also enables revenue forecasting, automated lead scoring, and the creation of data-driven personas for better customer understanding. In addition, AI can capture customer sentiment in real time and perform churn analysis to predict customer churn. Improving the data quality of customer data in CRM and using speech-to-text technology to document in-car visits are other benefits of AI in sales. In addition, AI enables accurate price optimization by predicting the likelihood of offer acceptance and determining optimal prices for products or services.
This list could go on and on. However, how companies use AI for B2B sales ultimately depends on their requirements. That’s why we’re now turning our focus to the implementation of AI systems in sales.
AI systems in sales enable efficiency gains, better sales closures and higher customer satisfaction. Successful implementation requires a human-centric approach. Close collaboration between people and technology is critical for sales to realize the full potential of AI and sustainably improve sales performance. A successful approach to this is so-called human-centered AI, or human-centric AI, which puts human needs and requirements first. By focusing on algorithms that exist in a human-based system, the interaction between humans and AI is improved – and the overall user experience (UX) is optimized.
Those looking to implement AI systems with human-centered AI should consider the following steps:
1. Understand context of use.
A comprehensive analysis of the context of use makes it possible to better understand user requirements for the AI solution and derive clear goals for AI development.
2. Define user-oriented requirements
Based on the context of use analysis, user-centric requirements for the AI solution are defined to focus on user needs and desires.
When implementing the user-oriented requirements, the focus is on usability and user experience. The user interface should be intuitively designed and easy to understand to enable easy interaction with the AI.
Usability testing, user surveys, and feedback iterations with potential users are used to verify the extent to which the requirements have been met and how well the AI system meets the users’ needs. The evaluation results help identify weaknesses and improve the AI system. A successful human-AI interaction leads to a positive user experience for sales teams, which can increase efficiency, productivity, and revenue for the company.
Make or Buy?
Implementing artificial intelligence in sales requires a strategic decision: Do you want to develop a custom AI solution (Make) or buy a ready-made solution (Buy)? If the decision is “Make”, it must also be decided whether the solution should be developed internally or externally. In all cases, several questions should be considered to make the right decision, including:
- Do we have enough resources to implement it in-house?
- Are there vendors with experience in our sector?
- What data will be needed for the implementation?
- How can we ensure data privacy and security?
- What interfaces do we need?
Each solution has advantages and disadvantages: for example, one advantage to buying an external solution is that it can be implemented quickly and requires few resources. Disadvantages are that the solution cannot be adapted to the company’s own needs and the company becomes dependent on the provider. Those who want to develop their own solution internally should ask themselves: Do we have enough financial strength, capacity, know-how and manpower for this? After all, an in-house solution is associated with higher costs at the outset. The advantages are that the own application is maximally adaptable and the company has full control over it. The third option is to have your own solution produced by an external service provider. The advantages for this are potentially lower costs and high efficiency due to collaboration with experienced developers. Disadvantages are: Dependence on the provider, communication challenges, and data security concerns.
When making a decision, managers should carefully analyze the company’s individual situation and thoroughly weigh the pros and cons. It is important to consider the company’s long-term vision and view the implementation as an investment in the future. Regardless of the option chosen, sales should be directly involved in the selection process. By making the right choice of suitable AI application, companies can strengthen their sales force, take a pioneering role in the market and achieve their business goals in the long term.
McKinsey (2022): The state of AI in 2022-and a half decade in review. Link: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review. [Accessed 07/21/2023].
Sinha, P. et. al. (2023): How generative AI will change sales. Harvard Business Review. Link: https://hbr.org/2023/03/how-generative-ai-will-change-sales. [Accessed 07/21/2023].
Gartner (2020): AI use case prism for B2B sales. Download link: https://www.gartner.com/en/sales/trends/ai-use-case-prism-for-b2b-sales. [Accessed 07/21/2023].
Gartner (2021): Gartner Predicts 75% of B2B Sales Organizations Will Augment Traditional Sales Playbooks with AI-Guided Selling Solutions By 2025. link: https://www.gartner.com/en/newsroom/press-releases/gartner-predicts-75-of-b2b-sales-organizations-will-augment-tra. [Accessed 07/21/2023].