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AI in Sales: Boost Revenue and Close More Deals
Empower sellers with generative AI to better serve prospects and sell more
By 2025, 35% of chief revenue officers will resource a centralized “GenAI Operations” team as part of their go-to-market organization. As adoption accelerates, sales enablement leaders can drive responsible use of the technology to help achieve better sales outcomes. A key success factor will be to develop enablement programs based on use cases relevant to sellers’ roles as revenue generators.
Download the research to learn to:
Create content and training for revenue-generating roles
Invest in generative AI literacy
Experiment with prompt crafting
Train sellers to create generative value messaging
Evaluate technology and ensure compliance
Harness sales AI capabilities to drive better sales outcomes
The use of AI in sales has great potential to transform the function — from identifying opportunities and resolving challenges to boosting performance and client engagement. Focus on these key areas:
- Efficiency and Engagement
- Sales Analytics
- Assistance and Automation
Reduce sales cycles and improve customer engagement with AI in sales
Advanced sales AI technologies such as natural language processing (NLP) and generative AI create significant opportunities to improve sales efficiency and customer engagement. These technologies work best when used to support B2B sales reps in their daily sales tasks and to increase customer engagement.
Following are some of the areas where AI in sales promises the greatest impact:
Demand generation. Cleansing your CRM data is a first step in activating AI tools that automate digital marketing processes and enable chatbots and virtual customer assistants (VCAs) to have real-time customer interactions. AI also automates content distribution for buying tasks and helps to personalize responses to convert more leads.
Deploy predictive lead qualification, guided selling and AI-based opportunity scoring across the B2B sales organization to:
Better understand and predict customer behavior with data collection across the entire revenue production life cycle
Improve engagement with hyperpersonalization that shares the right content at the right point in time during the buyer journey
Convert more leads into wins with better opportunity scoring
Improve sales and marketing alignment by bringing together all interaction data for a continuous customer experience
Forecasting. An immediate opportunity for AI investment, predictive forecasting offers:
AI-based expected revenue for future periods
Predicted revenue shortfalls/excesses versus assigned quotas
Predicted revenue shortfalls/excesses versus sell-submitted forecast levels
Forecast indicators at the opportunity level, used to help sales teams determine if specific deals should be included or excluded from specific forecast categories
Improvements here may increase sales process efficiency, prioritize the right customers and deals, and ultimately decrease the cost of sales and cost of revenue acquisition. These savings could then be used to fund additional AI investments in sales and marketing.
Conversation intelligence. This rapidly growing area leverages NLP to understand speech and text-based communication across different platforms such as CRM, emails and call logs, to serve different sales use cases, such as:
Analysis of call recordings. Instead of listening to hours of call recordings to identify coaching opportunities, sales managers can rely on conversational intelligence to analyze all available call recordings and identify what their high performers do differently.
Deployment of virtual agents. Deploy conversation intelligence as a virtual agent to interact with customers and serve as an initial point of contact, funneling the customer to the right person within the sales organization. It can even follow up to gauge progress on the opportunity.
Automated note taking. Automate note taking during client conversations, reducing sellers’ burden and allowing sellers to focus on the client.
Equip your organization to make the most of prescriptive and predictive sales AI
Traditionally, AI in sales was limited in scope, executing tasks based on explicitly stated rules. As AI has evolved from executing tasks to tackling more complex problems, “telling machines what to do” has shifted to “helping machines learn what to do.”
These enhanced ML capabilities can be applied to sales data and analytics in the following ways:
Diagnostic analytics analyzes correlations between 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 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 uses predictive models to inform pipeline planning — for example, predicting next month’s sales bookings by uncovering trends and seasonal patterns in the sales data.
Optimization and prescriptive analytics goes beyond predicting outcomes, and recommends the next action(s) to take for a given prediction. This form of analytics prescribes the best course of action when making a complex decision that involves trade-offs between business objectives and constraints. It’s also commonly referred to as a recommender/next-best-action system because it identifies and suggests cross-sell and upsell opportunities in accounts to sellers and sales managers.
To take advantage of more predictive and prescriptive sales AI in the short term, sales leaders need to invest in data science specialists to envision, develop and harness the potential of AI innovations. However, many of the specialized skills these experts provide will soon be replaced by automated analytics ecosystems — and the need for data scientists will evolve toward a higher-ROI delivery model that applies only to the most complex and unstructured sales problems.
No longer a one-size-fits-all solution, AI in sales can enhance decision making by flagging patterns and forecasting broad outcomes better than humans can do. For example:
District and region leaders will become better strategists and coaches as sales AI tools enable them to synthesize customer needs at the district, territory, account and opportunity levels simultaneously.
Sellers themselves will make better short- and long-term decisions, as augmented analytics deliver data-led decision support at the portfolio, account and opportunity level. Intelligent decision support will help sellers make the most of their human judgment (and will quickly weed out sellers who lack those capabilities).
Balance the possibilities and risks of generative AI in sales
The combination of GenAI technologies and sales technologies is transforming the landscape from sales technology as a tool to sales technology as a teammate.
With the ability to provide more novel real-time insights, combine external and internal data, and automate complex processes, generative AI in sales is ushering in a new era of improved sales decision making and proactive planning.
Generative AI in sales comes in the following formats:
Existing sales tech vendors provide some GenAI capabilities. These current sales tech vendors have developed and include their own user interface and prompt engineering technology, harnessing data sources from within and outside the enterprise to contextualize the inquiry. Some sales tech vendors are providing the prebuild prompts for the sales team. For example, an SFA platform vendor could use GenAI to summarize content and recommend the next best action.
Specialist vendors offer applications that lead with GenAI as the leverage. All vendors will use one or more (public or private) LLMs enhanced by specialist sales data to examine and generate answers or content. For example, applications could use buyer intent data to personalize message copy.
Digital workplace applications are also coming with GenAI capabilities. Although the outcomes of these applications are not specific to sales teams, these applications could give sales teams some starting points. For example, an application could create a standard sales email without much information on the account or nature of the deal.
GenAI provides a platform for performing a task that is prompted by the users. These types of applications could be used for various tasks. They often have the highest risk of inaccurate information.
Probably the biggest obstacle to GenAI adoption in sales is trust — both between an AI user and the AI itself, and between seller and buyer. To establish a trust-based relationship on either side, it’s critically important to build positive associations with sales AI technology over time, to gauge whether GenAI outputs are exercising good judgment and expertise, and ensure the GenAI tool delivers a consistent experience.
It is also important to confront fears about sales AI “replacing” humans. Communicate openly with sales teams about the tasks they would like to see GenAI perform and emphasize the value GenAI can potentially deliver by freeing sales reps to do more of what only they can do.
When evaluating a use case for generative AI in sales, consider the following questions:
What kind of competitive impact will GenAI have?
What’s the overall business value in terms of key sales metrics such as conversion rates or pipeline advancement?
How will GenAI impact productivity and efficiency?
What is the urgency to implement GenAI?
What are the costs and risks associated with implementing GenAI?
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FAQ on AI in sales
What are some examples of AI applications in sales?
When applied to B2B sales cycles, AI has multiple applications — for example, it can automate initial contact with potential clients, conduct follow-ups and maintain engagement with leads. Advanced AI sales technologies, such as NLP and generative AI, can not only provide a deeper understanding of customer inquiries, but can also manage countless conversations simultaneously, with the ability to personalize targeted outreach.
What are the benefits of using AI tools in sales?
One of the main benefits of AI is its ability to analyze data and content and improve sales performance and outcomes in an automated way. With the help of AI, sales teams can:
Save time and improve pipeline visibility and win rates
Engage more effectively with prospects and customers
Scale by automating labor-intensive tasks
What are the future trends in AI in sales?
The adoption of generative AI in sales is a trend that has enormous potential to transform the sales organization. Augmented RevOps is one upcoming use case, in which generative AI can help the teams that manage data, design automations and administer technology. Another exciting use case is AI-generated training centers for sales learning and development.