My Thoughts on Artificial Intelligence (AI) in B2B Sales
There’s a good bit of AI in B2B selling already, and the future looks bright! However, crappy CRM data will hinder many organizations from harnessing the power of AI for four reasons:
1. Flawed Recommendations: AI algorithms rely on historical data for predictions and recommendations. The AI will base its predictions on inaccurate data if the CRM data is defective or contains incorrect or outdated information. This leads to wrong sales forecasts, flawed lead scoring, and unreliable recommendations.
2. Poor Segmentation and Targeting: AI-powered systems use customer data to segment and target specific audiences. If the CRM data is incomplete or inconsistent, it becomes challenging to segment and target potential customers accurately. The result is wasted marketing efforts and ineffective sales strategies.
3. Amplified Biases: AI algorithms can inadvertently amplify biases in the data the algorithms rely upon. If the CRM data is biased, such as containing uneven representations of certain firmographics, the AI system will learn and replicate those biases. Amplified biases often lead to lost opportunities and inefficient resource allocation.
4. Decreased Trust and Adoption: If CRM data quality is lacking, sales teams may lose trust in the AI recommendations or predictions. If AI-powered tools consistently provide inaccurate or unreliable insights, sales professionals may hesitate to rely on them, leading to low adoption rates.
To better harness the power of AI, it is crucial to have robust data governance practices and invest in data quality monitoring and improvement efforts.
With reliable CRM data, AI in B2B sales can leverage accurate information to generate valuable insights, make better predictions, and enhance sales and marketing performance. Before your organization get’s too excited about AI, focus on getting CRM data in shape.