If you’re a B2B Sales Leader, it’s critical that you develop an AI strategy to help make your team more efficient and effective in 2023 and beyond. In this series of posts, we aim to demystify the application of AI to B2B Sales.
In our first post, we explored the B2B Sales patterns that make it a great fit for AI and the types of AI that can help sales teams better understand their prospects, improve the buying experience, and drive more sales. In the second post, we looked at the most impactful B2B use cases for AI and some example products that map to each of these use cases. In the last post, we looked at how you can develop an AI strategy for your Sales team.
In this post, we look at why B2B Sales teams struggle to apply AI and what they can do to address these challenges and realize the promised productivity benefits.
Addressing 3 Common Challenges to AI Adoption
The three major factors that hold back the application of AI at B2B companies are data quality and availability, a lack of internal expertise, and model bias. Let’s look at each of these in detail:
Data Quality and Availability: Sales teams often face challenges when working with a large volume of data that is spread across multiple systems or departments, leading to data silos. Incomplete data is another challenge, as organizations may not collect all the necessary data required to build accurate AI models. Data entry errors are also a concern, as human errors can lead to inaccurate or inconsistent data, which can impact the accuracy of AI models and lead to incorrect predictions. The resulting incomplete or inconsistent data can be a significant problem as it can lead to biased algorithms, incorrect predictions, and missed opportunities.
To address data quality issues, B2B sales teams can take the following steps:
- Clearly define data requirements: B2B sales teams should work closely with data analysts and IT teams to clearly define data requirements for AI models. This includes identifying relevant data sources and specifying data quality standards.
- Conduct regular data audits: Regular data audits can help in identifying data quality issues and inconsistencies. B2B sales teams should work with IT teams to regularly review and audit data used in AI models, and take corrective action where necessary.
- Invest in data quality tools: B2B sales teams should consider investing in data quality tools that can help in identifying and correcting data quality issues. These tools can include data profiling, data cleansing, and data matching tools.
- Leverage data enrichment services: Data enrichment services can help in filling gaps in data and improve the accuracy of data used in AI models. B2B sales teams should consider leveraging data enrichment services to improve the quality of data used in their AI models.
- Implement data governance policies: Data governance policies can help in ensuring that data used in AI models is consistent, accurate, and reliable. B2B sales teams should work with IT teams to develop and implement data governance policies, including policies for data access, data ownership, and data quality.
Model Bias: Model bias issues can have significant negative impacts on the application of AI for B2B sales teams. Bias in AI models refers to the tendency of algorithms to make predictions that are systematically inaccurate or unfair, often reflecting underlying biases. In the context of B2B sales, biased models can result in inaccurate predictions of customer behavior, missed opportunities, and flawed decision-making. Biased models can also result in discriminatory practices that negatively impact certain groups of customers.
AI models can be biased due to several reasons. AI models are trained using historical data, and if the historical data is biased, the AI model will also be biased and inaccurate. A related issue is that limited data sets may not represent the entire customer population, leading to biased AI models. Human biases can inadvertently be introduced into AI models during the model development process. This can include biases in the choice of variables or algorithms used.
B2B sales teams can take several steps to address the issue of bias in AI models. First, it is important to ensure that the data used to train AI models is diverse and representative of the population being served. This may require collecting additional data or using alternative data sources to mitigate bias in the training data. Second, sales teams should regularly monitor AI-powered tools and analyze their outcomes to identify and correct biases. This may involve tweaking algorithms or adjusting data inputs to improve accuracy and fairness.
Finally, Transparency into AI models can help avoid bias and also speed up the adoption of AI in a Sales organization. Companies can improve the transparency of AI models by using models that are more explainable, such as decision trees or linear models. Additionally, they can provide transparency into the AI models by sharing insights and explanations with stakeholders.
Lack of Internal Expertise: AI is a complex and rapidly evolving field, and many companies lack the in-house expertise to develop and implement AI models effectively. To address the lack of expertise, B2B sales teams should invest in training, collaboration with experienced vendors and experts, and ongoing education and professional development. B2B sales teams should partner with knowledgeable AI professionals, who can help guide them through the implementation process and offer best practices. A strategic approach to AI implementation, including clear goals and objectives, can help ensure the process runs smoothly. Additionally, teams should be open to learning and try to understand the data they are working with to ensure they are maximizing the potential of AI.
By addressing these issues, B2B sales teams can unlock the potential of AI to improve their efficiency and effectiveness.
Interested in learning how AI could help you identify your most ready-to-buy prospects right now? Drop us a line and we’d be thrilled to share our insights with you.