AI has drastically changed B2B sales, providing countless opportunities. Its potential has grown immensely in various industries. McKinsey found that AI usage has doubled since 2017. In sales, AI can address many areas, such as automating tasks, predicting customer needs, and optimizing processes, ultimately supporting sales teams.
Possible uses of AI in sales
AI in B2B sales has various uses: taking over repetitive tasks, analyzing data, predicting future needs, and improving customer understanding. It also enables revenue forecasting, automated lead scoring, and real-time sentiment analysis, predicting customer churn, improving data quality, using speech-to-text technology, and accurate price optimization.
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.
Implementation
AI systems in sales can improve efficiency, sales closures, and customer satisfaction. Implementing them successfully requires a human-centric approach, with close collaboration between people and technology. This approach, called human-centered AI or human-centric AI, prioritizes human needs and requirements. By focusing on algorithms that work well with humans, the interaction between humans and AI is enhanced, leading to an optimized user
experience.
Those looking to implement AI systems with human-centered AI should consider the following steps:
1. Understand context of use.
Understand how the AI solution will be used and analyze thecontext to determine user requirements and development goals.
2. Define user-oriented requirements
User needs and desires are the focus of defining requirements for the AI solution, based on the analysis of context of use.
3. Implementation
When implementing user-oriented requirements, the goal is to create a user-friendly and intuitive user interface for easy interaction with the AI.
4. Evaluation
User testing, surveys, and feedback are used to check if requirements are met and how well the AI system meets user needs. Evaluation results help find weaknesses and improve the AI system. Good human-AI interaction leads to a positive user experience for sales teams, which boosts
efficiency, productivity, and revenue.