The Role of Artificial Intelligence in Sales

With 87% of sales leaders reporting a top-down push from CEOs and boards to implement GenAI, it falls to CSOs to guide their leadership teams in making smart sales AI decisions. But decisions can be skewed by inflated vendor promises and IT teams that may not fully grasp the complexities of frontline sales. 

Navigating the complexities of AI in sales requires a nuanced understanding of how sales actually works. Enter: actions.

Actions are the granular units of work required to make optimal AI design choices. Most CSOs can recall the unstructured, highly detailed actions of traditional frontline sales workflows — like the complexity of following up a sales call or trying to gather internal stakeholders to make a decision for a big deal.

Gartner’s Seller Action Hub (see image) takes actions a step further by using a frontline-centric approach to AI tech stack design. The needs and workflows of the sales team drive the tech stack, rather than vendors’ capabilities.

The Seller Action Hub guides sales technology teams in making optimal AI design decisions using three tenets:

• Understand sales at an action level to pinpoint where AI can enhance or automate tasks.

• Recognize that different sales roles often require different actions and technologies.

• Integrate AI to augment or automate specific actions of frontline sales work.

GenAI can support a wide range of actions, like synthesizing information, writing content, answering questions, editing for tone and simplifying information as part of far more complex selling workflows.

You can integrate AI into actions to:

• Create insights by linking underlying data signals to sales behaviors that drive positive outcomes

• Recommend internal or customer-facing actions, as well as the next best tasks associated with insights

• Facilitate team selling, collaboration or management tasks

• Execute customer-facing engagement tasks

• Capture the impact of actions on sales outcomes to improve AI/machine learning (ML) models

Every buyer interaction with your organization — human and digital — gives insight into a buyer’s progress on the purchase decision. Using these insights is essential for forecasting, but every sales organization deals with the same fundamental issues:

• Forecasting is time-consuming — and invisible to your customers.

• Only 7% of teams achieve a forecast accuracy of 90% or more, and the median accuracy is 70% to 79%. As a result, actionability is limited.

• Forecasting is getting more difficult. Sixty-nine percent of sales operations leaders we surveyed say forecasting is harder than it was three years prior.

AI-augmented forecasting is a growing alternative. As AI in sales advances, sellers can increasingly offload forecasting to technology — which reduces the burden on sellers, tightens forecast accuracy and simplifies the overall process.

• Activity intelligence detects buyer interactions in other systems (such as email, calendars, web meeting platforms and team collaboration tools) and logs them without human intervention. An AI sales assistant can spot, recommend and simplify moments to capture data.

• Conversation intelligence extracts insights from buyer interactions and converts that information into guidance. Conversation intelligence can pair with GenAI to provide call summaries and next steps. These outputs will be more thorough and objective than a seller could provide.

The challenge? Making the shift to AI in sales forecasting requires a significant culture change.

The following steps will help you prepare:

1. Personalize your vision. Determine how AI-augmented data capture aligns with your specific business goals. For example, your sellers may struggle to update their opportunities as deals progress. In this case, start by communicating a future state where sellers no longer have to log calls (because of activity intelligence) or project outcomes (because of AI deal scoring).

2. Audit your existing processes. Discover how much time various participants currently spend on forecasting tasks so you can quantify the potential benefits of AI-augmented data capture.

3. Learn about vendor offerings. Compare the capabilities offered by different vendors in the market. Then compare those capabilities with your needs, as articulated in your vision and/or revealed by your audit.

4. Build support for the technology-as-a-teammate (TaaT) approach. Start building awareness and desire for AI-augmented forecasting. Use storytelling to convey the benefits and shortcomings. Push sales leaders to renew their focus on data-driven leadership.
   
   To take advantage of TaaT, you’ll first need to build the sales team’s trust. How you do that depends on where you are on the AI-augmented forecasting spectrum today — i.e., whether you still use reports and spreadsheets, have completely delegated forecasting to AI or are somewhere in between.