AI in B2B Sales: Decision Augmentation and Automation

As businesses increasingly turn to artificial intelligence (AI) for support in decision-making, many are exploring the use of decision augmentation to improve their sales strategies. This involves the use of AI to recommend decisions or multiple decision alternatives to humans, leveraging the ability of the technology to quickly analyze large amounts of data and deal with complexity. This trend will be even more important in 2023. 

Basing decisions on AI

It is expected that the adaption of decision automation, which relies on AI to make decisions based on prescriptive or predictive analytics, will continue to grow in the coming years. This has also been noticed by Gartner®, whose recent publication describes this issue in more detail.

AI-based decision-making is more efficient and removes the need for human analysis of huge amounts of data, which in turn increases an employee’s productivity in other tasks. Information for analysis includes structured data, such as the size and type of the business, its location, past purchasing history, and engagement with marketing materials, and unstructured data, such as interactions with customer service representatives, analysis of business trends and customer demographics, social media activity, industry competition, and economic conditions.

Three areas of AI decisions development

Decisions made by AI should be appreciated. To better understand that, let’s look at three areas in decision management where AI is becoming essential, and not only considered as an additional feature.

Decision support software

Decision support systems provide human employees with descriptive, diagnostic, or predictive analytics to support their decision-making. These systems generate a condensed summary of a customer's interests, financial situation, and sales strategy for a human operator to consider. Examples include a customer relationship management (CRM) system that provides a salesperson with a summary of a customer's purchase history, preferences, and contact information to help them tailor their sales pitch, and a financial analysis tool that generates reports on a company's financial performance, including trends in revenue and expenses, to help managers make informed decisions about budgeting and resource allocation.

AI-driven decision augmentation

Decision augmentation systems recommend decisions or multiple decision alternatives to humans using prescriptive or predictive analytics, combining human knowledge with the ability of AI to quickly analyze large amounts of data and deal with complexity. Examples include a supply chain management system that suggests the most cost-effective suppliers and transport routes based on real-time data on demand, inventory levels, and shipping costs, and a recommendation engine that suggests products or services to customers based on their browsing and purchase history, as well as similar items purchased by other customers with similar interests.

Decision automation

Decision automation systems make decisions using prescriptive or predictive analytics, offering benefits such as speed, scalability, and consistency in decision-making. Examples include:

• an automated sales quoting system that uses algorithms to analyze customer data (including location, usage patterns, and budget) to generate customized service plans and pricing quotes,
• a customer segmentation system that uses machine learning to classify customers into different groups based on their demographics, purchasing history and behavior, and then automatically assigns targeted marketing campaigns to each group,
• and a lead qualification system that uses predictive analytics to identify potential customers who are most likely to purchase a telecom product or service, and then automatically routes those leads to the appropriate salesperson or team.

In the near future we expect to see significant growth in adaptation of the decision automation systems for sales support.