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Join nowNew autonomous applications are helping sales teams scale fast. Here’s how they work.
Kris Billmaier, EVP & GM, Sales Cloud, Salesforce
December 4, 2024
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.
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AI sales agents are autonomous applications that analyze 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 customized for specific use cases.
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 analyzing 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.
While AI sales agent tasks differ, their key features remain largely the same:
Data-dependent: Agentforce AI sales agents are built on trusted CRM and business data. Only when they fully process organizational systems, customer details, and deal specifics can they deliver accurate and personalized outputs.
Available 24/7: Autonomous agents engage with leads and answer inquiries 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.
Customizable: 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 that possible, 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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Use cases between different agents differ as they augment different roles on the sales team. Here are a few common examples:
As mentioned above, AI sales agents come either prebuilt or are customized 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:
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.
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 on 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, “Be sure to 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 reach out 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 summarize 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 alongside 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 coordination 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 customization options available today will be even more robust tomorrow — with simple interfaces that anyone can use.
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 personalized 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 personalization.
AI sales agents are used by sales teams to handle repetitive tasks, like lead qualification, follow-ups, scheduling, and coaching. They work to save time, scale outreach, improve quota attainment, and increase customer engagement. Sales is about relationship building, so teams benefit from agents handling repetitive tasks so they can focus more on connecting with customers.
As trust and security are major concerns for businesses relying on AI, it’s important to use tools grounded in trusted data sources. While several companies have developed or are developing agents to assist with sales tasks, Agentforce Agents (developed Salesforce) have a distinct advantage: They are built right into the Salesforce CRM, Data Cloud, and the Einstein Trust Layer. Unsecured tools can result in loss of privacy, misuse of data, and inaccurate agent outputs.
Chatbots are one way of conversing with AI. Before generative AI stormed the scene, chatbots relied on scripts without much personalization, creating clunky and sometimes laughable user experiences. With generative AI, chatbots became a lot more adaptable, with many businesses using them to deflect customer service queries.
AI sales agents are several levels more advanced. They’re proactive, autonomous applications that execute complex tasks, escalate issues, and rely on business-specific data to augment sales teams. Some interfaces of AI sales agents are similar to chatbots – roleplaying with an AI sales coach, for example – but much of what these agents accomplish is more sophisticated, personalized, and relevant to the moment or circumstance.
The cost of AI sales agents varies based on business needs, the number of conversations they’re having, and the level of integration with business data. Agents are flexible and scalable, and give teams the ability to adjust usage based on sales pipeline needs while helping teams offset the costs of training, coaching, and hiring new reps.
Generally yes, when sales agents are integrated with secure platforms and data. Agentforce, for example, is built on the Einstein Trust Layer, ensuring data security and adherence to business protocols when handling tasks. AI sales agents built with Agentforce rely on trusted business data to ensure accuracy and security.
One of the most commonly cited anxieties around AI is its potential to replace humans and affect jobs. Here’s the thing: Selling will always involve real relationships between real people. But the deal-long engagement between salespeople and their customers are full of touchpoints that don’t actually require a lot of human oversight. In the past, there was no choice; following up, scheduling, populating CRM data, and coaching were all part and parcel of working in sales. AI sales agents are taking on the most time-consuming tasks with less human intervention.
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