AI Sales Agents: A Complete Guide

If you were to ask a sales rep why they chose a career in sales, they’d probably tell you something like, “I love talking to people. I’m ambitious and goal oriented and no two days are ever the same.”

The reality is that a lot of it can be downtime. Recent data suggests that sales reps only spend 28% of their time actually selling, with the rest spent on administrative tasks and non-revenue-generating work. To alleviate the pressure and busywork, sales teams are turning to AI sales agents, which allows them to focus on actual selling.

Below, we break down the different types of AI sales agents and share how businesses are using them to augment their sales teams to increase team productivity and revenue.

What you’ll learn:

• What is an AI sales agent?
• Types of AI sales agents
• Key features of AI sales agents
• Benefits of AI sales agents
• Use cases for AI sales agents
• How AI sales agents work: set-up and deployment
• Examples of AI sales agents in action
• Future trends for AI sales agents
• FAQs

AI sales agents are autonomous applications that analyse and learn from your sales and customer data to perform tasks with little or no human input. These agents can perform a wealth of functions, from top-of-funnel tasks like nurturing leads with email outreach, answering questions, booking meetings with sellers and quote creation to tasks more deeply integrated inside sales teams, like active buyer roleplays and coaching. What makes them different from simple workflow automation is that agents are capable of learning, using data analysis to work more efficiently, taking action on their own.

Agents are often pre-built, easily plugging into an existing CRM, but can be fully customised for specific use cases.

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There are two main types of AI sales agents:

Autonomous agents: Agents that act independently of human input, based on available data, workflows and intelligent reasoning. For example, a sales development representative (SDR) agent might support reps by autonomously engaging inbound leads via email or chat, answering questions and scheduling sales meetings for reps.

Assistive agents: Agents that help humans complete certain tasks while carrying out autonomous reasoning and actions. For example, a sales coach agent that roleplays with sellers and offers real-time feedback.

Searching for and analysing data, formulating a plan and acting on that plan is what differentiates modern AI sales agents from other bots and sales tools - set apart by their more complex pre-defined rules and decision trees.

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While AI sales agent tasks differ, their key features remain largely the same:

Data-dependant: Agentforce AI sales agents are built on trusted CRM and business data. Only when they fully process organisational systems, customer details and deal specifics can they deliver accurate and personalised outputs.

Available 24/7: Autonomous agents engage with leads and answer enquiries around the clock, ensuring no opportunities are missed​. Assistive agents are also available 24/7, but may not be able to complete certain tasks unless/until a human intervenes.

Customisable: Some AI sales agents work straight out of the box, while others are built from scratch using low-code builders, tailored workflows and APIs that connect disparate tools in a sales tech stack.

Secure and compliant: To ensure customer data remains secure, agents rely on guardrails to operate within predefined limits. These guardrails include everything from compliance clauses in quote generation to rules that determine how or when an agent can send an email. Beneath the guardrails, agent responses are fed through a trust layer to ensure safe data usage.

Scalable: AI agents are designed to handle high volumes of tasks without requiring more human reps​​. Designed to be either completely or partially autonomous once deployed, they don’t require the same level of manual input as certain bots and workflow automation tools.

Integrated: The purpose of AI sales agents is to increase team productivity and efficiency across a sales org. To make possible that, agents are often connected to your CRM and sales force automation tools so they can slide right into the flow of work.

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• Always on: While sales teams are limited by set work hours, agents can work around the clock. This is especially true of autonomous agents that aren’t dependant on human input, but are grounded in CRM data. The result is faster response times with always-on personalised responses.
• Increased rep productivity: Because AI sales agents handle routine tasks like communicating with new leads, sales reps are freed up for more strategic activities​ like relationship building and deal negotiations. This helps increase deal velocity, enabling reps to hit quota faster. Some agents can also help prepare reps to enter meetings with personalised training tailored to specific deals.
• Accuracy: When powered by up to date, comprehensive data, AI sales agents deliver consistently accurate outputs — free from manual errors seen in human work.
• Customer satisfaction: Due to their speed and accuracy, AI sales agents can dramatically increase customer satisfaction and issue resolution.
• Scalable: Agents handle large volumes of leads without the need to hire more sales reps and take on tasks like more frequent coaching that would otherwise require more managers.
• Easy to customise and use: Certain AI sales agents work out-of-the-box with no-code or low-code customisation, allowing teams to see results quickly. Others can be built to fit different needs and use cases.

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Use cases between different agents differ as they augment different roles on the sales team. Here are a few common examples:

• Scaled outreach: With CRM and external data at their disposal, agents can autonomously perform personalised communications at scale, like collecting information from prospects and answering nuanced product questions.
• Nurturing inbound leads: Well-deployed agents touch every inbound lead - even the ones your team doesn’t have time for - pulling them into a nurture flow by answering product questions before booking a meeting with a human rep.
• Sales training: AI sales agents that are built to keep reps performing at their best, like Agentforce Sales Coach, enable reps to practise pitches with roleplays tailored to their specific deals and receive actionable feedback​.
• Rep onboarding: New sales reps and AEs can work with a personal coaching agent to receive guidance throughout their onboarding journey, with consistent and personalised feedback on their sales calls.
• Quoting, billing and invoice management: WIth predetermined guardrails and access to customer and deal information, AI sales agents can build customised quotes, send invoices and collect payments.
• Partner onboarding: AI sales agents can be built to handle onboarding for partners handling specific products, so managers don’t have to build out customised training programmes.

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As mentioned above, AI sales agents come either prebuilt or are customised using a low-code builder and workflows. Once connected to your CRM and sales software, you need to input rules and resources to make sure your agents complete the tasks you want accurately.

As an example, here’s how you set up autonomous Sales Agents in Agentforce Agent Builder:

1. Either select a prebuilt agent or create a custom one. Even if you’re using a prebuilt agent, you may need to tweak its out-of-the-box functionality to meet your business needs, so make sure that you complete all of the set-up steps.
2. Define agent with roles and data: Using simple form fields, assign and name your agent with its specific function. Agentforce will suggest actions your agent can take based on data you already have in your CRM. Pick the ones you want, then customise agent instructions in plain language.
3. Set up guardrails: Guardrails ensure compliance and secure use of customer data​​. Use plain language to give your agent rules to follow. This is not only critical for internal use, but it teaches the agent how to engage with customers and prospects.
4. Add source materials. With your agent’s actions selected, upload or select relevant data from your CRM it can use to inform its work. Think product documents, FAQs and industry one-sheeters.
5. Test, deploy and go live: Once you verify all of your inputs are accurate and confirm that the agent outputs are going to the right places at the right time, you can either test the agent or activate.

One of the unique advantages of using autonomous sales agents, like those powered by Agentforce, is the ability to use natural language to communicate what you want your agent to do. That means anyone can build an agent for just about any use case.

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AI sales agents work autonomously like human users in your existing sales CRM and tech stack. For customers, the experience is seamless - they’re aware they’re interacting with an AI agent, but carry out conversations in natural language to supply information or schedule meetings.

Here’s how that might play out in two common sales scenarios:

Preparing reps for sales calls: Most sales teams agree that they could benefit from training and enablement, but only 32% of sellers have regular one-on-ones with their managers. This creates massive missed opportunities for coaching and improvement, especially as teams struggle to attain quotas.

Imagine a new or junior sales rep is getting ready for a call. They click the sales coach agent in their dashboard, like Agentforce Sales Coach, to help them work through questions and objections before they get on the phone. Depending on whether they’re making a pitch or are expecting to negotiate in later stages of a deal, they can either roleplay a conversation live or record a sales pitch. Supplied with customer information, buyer personas and all available CRM data, the Sales Coach then delivers actionable feedback and guidance specific to the deal in question. Feedback can be anything from, “Make sure that you mention pricing options” to “Don’t be so eager to lean on your manager for additional information — have answers ready.” The experience ensures sellers are supported and able to perform their best regardless of their manager’s availability.

Nurturing inbound leads: Top-of-funnel marketing efforts, with gated content forms, demos or webinars, can create tons of leads - often too many for reps to handle on their own. A rep could spend time trying to qualify every lead individually, but many marketing qualified leads (MQLs) are not ready to move on to the next stage, resulting in time lost. An AI sales agent can step in and proactively connect with these leads, so by the time a sales rep is ready to jump in, they’re better able to move the deal down the pipeline.

Agentforce SDR is a great example of this nurturing in action. Let’s say your company gets a lot of leads from a new report you published. Agentforce SDR is there to keep in touch as the leads come in, emailing them to ask why they downloaded the report and if there are any questions they need answered. As emails and reponses continue, Agentforce SDR can surface problems and introduce your product as a solution the lead may not have considered. Interest established, it can book a meeting with a human rep to move the deal forward.

While Agentforce SDR’s responses are detailed and natural, the end-user is fully aware they’re interacting with an AI sales agent. For sellers, the agent acts like a regular user in their CRM, logging changes and data for visibility.

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In the recent past, sales AI worked like an assistant or a copilot. At its best, it helped sellers summarise data insights and do tasks like create forecasts. At its worst, it required a lot of intervention and human input while creating disconnected islands of customer data. Today, AI agents work autonomously with humans, which lets humans focus on high-value tasks, like relationship-building.

As AI agents grow more robust, so too will their ability to handle more sophisticated workflows and multi-step processes autonomously, across more channels and with richer actions. Think proactive processing and resolution of customer requests — before issues surface. Or co-ordination of deal approvals with stakeholders, requirements and deadlines across multiple working groups.

AI sales agents are also likely to have native multilingual support, feature dynamically-generated dashboards for tracking complex performance metrics and engage in enhanced roleplays that incorporate real buyer personas — or even join sales calls directly to offer real-time feedback.

The long and the short is: AI sales agents will open the door to a wealth of complex use cases that make humans more efficient. And the customisation options available today will be even more robust tomorrow — with simple interfaces that anyone can use.

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According to recent data, sales leaders’ tactics for improving growth involve “improving sales enablement and training, targeting new markets and improving the use of new tools and tech.” They’re concerned about scaling personalised interactions and hitting their quotas. AI sales agents answer to many of these points, changing the way sales teams operate by allowing them to offload work to autonomous agents without sacrificing quality and personalisation.

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