AI Sales Coaching: A Complete Guide

Here’s the tea: Everyone can agree that coaching is important for sales teams, but managers are swamped. Sales reps need to be prepared for unique customer conversations. Coaching is key, especially for new reps, to help them succeed in sales calls. Sales leaders have noted that there’s always room for performance improvement, making coaching a critical function.

In the State of Sales report, improving training has been cited as the #1 growth tactic for sales leaders over the next 12 months – and one of the ways companies are improving training is with AI sales coaching. Below, we cover the ins and outs of AI sales coaching – from benefits, best practices, and everything you should look out for.

Not all AI sales coaches are created equal. While there are several sales coaching tools on the market, very few are capable of carrying out end-to-end tasks, or built on top of trusted large language models and reasoning engines. While many third-party sales coaching applications offer performance analytics or evaluate call transcripts, few utilize autonomous, agentic AI.

To understand how agentic AI sales coach tools, like Agentforce, work, we need to take a look under the hood.

• Agent builder: A low-code tool that lets users create, customize, and configure AI agents for specific roles or tasks. Businesses can define specific areas or tasks the agent should handle (in this case, sales coaching), predefined tasks the agent can execute and give natural language instructions that directs how the agent should behave or respond in various situations.
• Skills library: Agentforce AI sales coach agents come with a library of pre-built skills coaching and training. These skills help businesses deploy agents quickly without starting from scratch.
• Atlas Reasoning Engine: Part of what differentiates AI sales coaching agents from others on the market is its reasoning engine. This functions as the “brain” of the agent, to process information, understands queries, and determines the best actions to take.
• Data integration: Powered by Data Cloud, sales coach agents have access to real-time, unified customer data, allowing it to deliver personalized roleplays, execute actions based on up-to-date information, and analyze interactions to inform future interactions.
• Autonomous execution: Once a task is triggered, the agent performs actions autonomously or with human input, providing real-time feedback and recommendations to reps.
• Security layer: Since AI sales coach agents deal with customer data, security is extremely important. With Salesforce’s Einstein Trust Layer, Agentforce ensures that all actions are secure, compliant with enterprise-grade standards, and respect data privacy regulations.

Once the agent has been created, reps can launch a practice session directly from Salesforce. First, the agent simulates a sales conversation with a lead, such as a new product inquiry or a tough objection. The rep then delivers their pitch as if speaking to a real customer.

As the rep talks, the sales coach listens and analyzes in real time. Once the session ends, the agent provides instant, specific feedback on key areas like tone, clarity, empathy, and how well objections were handled. The rep can then adjust their approach and repeat the exercise as many times as needed, building confidence and refining their skills with every round.

Here’s an example of how one company customed their AI sales coaching to a healthcare use case:

Precina, a healthcare company focused on helping rural and low-income patients manage type-2 diabetes, needed to onboard new clinicians to help them create personalized treatment plants during patient intake calls. Normally, this training is resource-heavy, time consuming, and difficult to scale.

To manage this, they deployed Agentforce's coaching skills, creating an always-on learning environment where clinicians can practice their intake calls and receive real-time, actionable feedback.

This feedback measures them on everything from accuracy and compliance to empathy, also allowing them to train flexibly and independently. All in all, Precina was able to scale their outreach and improve the quality of their intake calls in order to help more patients manage their health.

When sales teams are looking to implement an AI sales coaching solution, they’re likely hoping to address issues with onboarding time and their capacity to scale training. As mentioned above, not every coaching tool is created equal – many sacrifice security for cost cutting, while others might not be able to unify data from disparate sources in order to craft truly personalized, actionable feedback.

When considering an AI sales coaching solution, businesses should look for:

• CRM integration: The agent should work seamlessly with existing sales platforms for contextual, personalized coaching specific to deals, all in one workspace.
• Speech and sentiment analysis: Coaching agents should be able to detect conversation sentiment, understanding when and how a sales rep is hitting the necessary points and where they have room to improve.
• Roleplaying: Simulated customer interactions should help reps practice handling objections and refine their sales techniques.
• Security and compliance: AI sales coaching tools should maintain enterprise-level data security and comply with privacy regulations​ for the benefit of your customer and your organization. Agentforce is built on the Einstein Trust Layer to keep company and customer data secure.

Businesses can choose between numerous third-party AI sales coach tools, or take advantage of agents that come out of the box with deep integrations into their existing sales platform, like Agentforce. These AI agents come prepackaged with skills to carry out coaching and sales training.

One way businesses can customize their own sales coach agents is by using a tool like Agent Builder.

Agent Builder is a low-code tool within Agentforce that allows businesses to easily create and customize AI agents for specific roles, like a sales coach. Using natural language instructions, users can define the agent’s topics (like objection handling and deal negotiation), set clear instructions for how the agent should respond in various scenarios, and assign specific actions (like providing feedback, suggesting next steps, or prompting follow-up tasks).

A sales coach agent, for example, can simulate customer interactions, analyze sales pitches, and offer real-time feedback on areas like tone, empathy, and compliance—helping sales reps improve their skills through personalized, actionable guidance tailored to each deal.

Until recently, AI sales coaches were purely assistive tools that mostly offered summaries of sales call transcriptions. Yes, this was useful insight – nobody loves poring over sales call logs – but there’s more that AI sales coaches were capable of. With more sophisticated LLMs, deeper integration with sales tools, and features like live roleplays, the future of AI sales coaches is exciting.

It’s easy to imagine that in the near future, sales roleplays will become more realistic, incorporating details to make the process more human-like and conversational than it already is. Over the long term, a personal AI sales coach might provide customized coaching plans that evolve as the rep evolves, analyzing their unique selling style and adapting feedback accordingly.

For businesses that want to stay ahead of the curve, early adoption is key. With sales leaders planning to increase AI adoption to further their training goals, organizations who scale coaching efforts with AI, and thus increase closing rates, are poised to see higher sales success and gain a competitive edge

Help your reps sharpen their skills, build confidence, and close more deals with personalized, AI-powered coaching available anytime they need it. See why companies like Precina and Salesforce leverage AI sales coaching with Agentforce to boost performance and increase win rates.