Departments across the organization are discovering more and more use cases for generative AI (GenAI).
Sales is no exception, isolating many applications to better manage their time, find qualified leads, handle objections, and more.
From customer research through to post-purchase activities, GenAI is reimaging the sales process.
That's critical, as many sales teams have long struggled to operate with speed, precision, and efficiency as customer preferences continue to shift.
In 2023, McKinsey predicted that GenAI tools could increase productivity among sales teams by three to five percent, adding trillions of dollars in value to the global economy.
Let’s explore some of the use cases helping sales teams move the needle.
1. Admin Automation
Example: Salesforce Einstein (and Agentforce Agents)
Sales professionals often get stuck doing everything except selling. There’s a lot of admin work that can consume a team’s energy and productivity.
Generative AI in sales can tackle this head-on by automating various tedious tasks. AI tools like Salesforce Einstein can automatically update CRM records, summarize call notes, extract data from emails and messages, and more in real-time.
Some businesses are now taking this further with agentic AI, creating custom agents that automate entire workflows.
Indeed, AI agents can build tools that can craft pitches, recommend discounts or deals, and even design sales strategies with relevant resources for specific teams.
2. Personalized Customer Comms.
Example: Gong Engage
71 percent of buyers now expect personalized experiences, as per McKinsey. Companies that deliver earn 40 percent more revenue than those that don’t.
However, keeping every message, pitch, and proposal tailored to each prospect is difficult at scale.
Generative AI can help personalize everything from cold emails to full presentations, taking customer context to tailot the response.
Gong Engage is a great example of a tool that can compose custom content for customers based on prior communication, buying stage, and tone preferences.
The platform can even recommend key tasks to specific team members, advising them on when to follow up with customers and on which channels.
3. AI-Powered Prospecting
Example: HubSpot Breeze Copilot
Gartner predicts that by 2027, 97 percent of sales workflows will begin with AI. For many companies, that means optimizing prospecting and account research tasks.
Generative AI in sales platforms can help companies sift through massive datasets fast, combining intent data, firmographics, buying signals, and more to identify valuable targets.
For instance, HubSpot offers capabilities within Sales Hub to research prospects, isolate decision-makers, score leads, and create personalized outreach strategies.
Companies like Zoom are experimenting with similar solutions, like prospecting agents for Zoom’s Revenue Accelerator platform.
4. Sales Content Creation
Example: Jasper.AI
From auto-generating sales decks to email templates, content creation has always been a time sink for sales reps. Customizing materials for each prospect takes even more effort.
AI writing platforms, like Jasper, Copy.AI, and Writer, help sales and marketing teams develop high-quality content fast. These tools can generate email copy, blog introductions, case studies, cold call scripts, and sales battle cards.
Some AI assistants, like the Microsoft 365 Sales Copilot, can also draw on CRM data to personalize each piece of content.
Additionally, the Jasper platform empowers companies to upload examples of their brand’s tone of voice for improved content consistency.
5. Real-Time Sales Coaching
Example: Dialpad AI Sales Coach
Sales coaching has always been limited by time. Managers can’t join every call. Employees spend weeks waiting for feedback, which means growth slows.
Generative AI assistants and copilots are changing that, with real-time coaching and performance feedback.
Dialpad’s GenAI sales coach is an excellent example, listening to live sales calls, analyzing dialogue, detecting sentiment, and prompting reps with suggestions through the call.
The solution can even advise reps on adjusting their tone of voice in real time to ensure they’re showing emotional intelligence, empathy, and professionalism.
6. Simulated Sales Training
Example: Second Nature
Role-play exercises are a classic part of sales training, but they’re often awkward, limited, and time-consuming.
Generative AI brings sales training into the digital age by enabling on-demand, interactive simulations that mirror real customer conversations.
Platforms like Second Nature allow companies to create realistic, GenAI-augmented AI avatars that can simulate the responses of a real human being during a sales conversation.
Teams can then practice addressing objections, pitching new products, and delivering value props as often as they like.
They also receive immediate AI-generated feedback on clarity, confidence, and engagement.
GoHealth claims this use case has helped decrease onboarding time by 36 percent and increase won deals by 46 percent.
7. Predictive Sales Forecasting
Example: Zoho Zia
Gartner suggests only around seven percent of sales teams currently achieve a forecast accuracy of 90 percent or more. Generative AI could provide an answer.
Indeed, GenAI-driven tools can combine more data, from multiple resources, to deliver more informed predictions.
For instance, Zia, the AI copilot in Zoho, can study sales deals to decode the patterns behind wins and losses. From there, the tool can predict the conversion potential for each lead and score accordingly.
The solution can also create comprehensive prediction dashboards, which outline the potential value of each deal and when it's likely to close.
From there, teams can use advanced filters to sort through records with similar patterns, scores, or opportunity levels.
8. Scheduling Automation
Example: Calendly
Few things derail momentum in a sales cycle more than trying to coordinate calendars. Setting a time for a discovery call or demo can take weeks without a little help.
Generative AI in sales can solve this problem by automating the scheduling process.
