The Gartner 2021 CSO Priorities Pulse Survey reveals that 88% of chief sales officers (CSOs) have already invested in or are considering investing in AI analytics tools and technologies. However, the rapid rise in AI’s popularity as a business topic, coupled with vendors trying to reposition their offering portfolio as AI, has created confusion about what it truly represents.

“AI has many subdomains that often overlap with one another in different applications, and a universal definition has not yet been accepted,” says Greg Hessong, Senior Director, Advisory, Gartner. “For business purposes, AI is a piece of software designed for specific use cases. It analyzes available data to gain understanding, then uses this understanding to help achieve the desired business outcomes.”

Traditionally, AI was dominated by rule-based systems, but as it evolved to tackle more complex problems, machine learning (ML) developed. With ML, “telling machines what to do” has shifted to “helping machines learn what to do.” With this approach to AI, machines are able to repetitively learn from data, identify patterns and uncover hidden insights without being explicitly programmed to do so.

Capabilities of AI commonly seen in sales

Identifying the type of AI technology solutions most suited to the sales organization requires a foundational understanding of its varied capabilities. Those commonly seen in AI-enabled sales technology solutions are: 

• Natural language processing (NLP): NLP provides intuitive forms of communication between humans and systems. It is designed to recognize, read, interpret, tag and generate natural languages, such as emails and sales-call transcripts. It is commonly used to enter data into systems without human intervention. Sales-call analysis, automated note-taking and virtual agents are among NLP’s common applications.
• Diagnostic analytics: Diagnostic analytics analyze correlations among variables in a dataset to find existing relationships between them. The established relationships between variables in the captured data can be used to analyze problems and cluster data for sales purposes, such as personalization, segmentation and tiering.
• Predictive analytics: Predictive analytics tries to estimate what will happen in the future. It does so by identifying the factors that influence a given outcome and understanding how they do so. This behavior is distinct from diagnostic analytics, which only tries to explain why something happens. Sales forecasting commonly leans on predictive models to inform pipeline planning.
• Optimization and prescriptive analytics: This form of analytics uses optimization techniques to prescribe the best course of action when a complex decision involves tradeoffs between business objectives and constraints. Commonly referred to as a recommender/next-best-action system, this capability can identify and suggest cross-sell and upsell opportunities in accounts to sellers and sales managers.
• Smart process automation (SPA): SPA differs from traditional robotic process automation in the complexity of tasks that can be automated. Using ML, SPA capabilities can automate higher-order tasks that require complex reasoning to process.

“By understanding the commonly leveraged AI capabilities, CSOs will be able to effectively assess whether a proposed AI technology’s features serve their intended use case,” says Hessong.

Beware of the pitfalls 

While AI can improve business outcomes, be aware of the common challenges — such as poor use-case selection, low data quality and inadequate attention to employee upskilling — that can hinder successful implementation. If you are responsible for investing in AI, and/or implementing and managing AI capabilities, you should:

• Defuse the hype and set realistic expectations for AI investments based on an accurate understanding of the current capabilities of AI.
• Review the viability of potential AI use cases by assessing how and to what extent AI can improve target sales outcomes in the context of your organization’s unique needs.
• Strengthen AI implementation efforts by focusing on a narrow set of clearly defined use cases, improving the quality of data used to train AI tools and helping employees upskill and adopt AI in workflows.