AI Agents: What Are They and What Impact Do They Have?
When you think of an AI assistant, your mind probably jumps straight to voice assistants like Siri and Alexa. However, these aren’t the only types of AI assistants out there. There are many different ways AI is helping us be more productive, not only in our daily lives but in professional settings, too.
AI agents are helping companies handle manual and repetitive tasks, like capturing customer meeting outcomes and updating Salesforce without requiring field sales representatives to stop and enter information manually. The result? Field sales teams that have more time to focus on customers, while important account, opportunity, and deal information makes its way into Salesforce.
In this post, we’ll take a closer look at what AI agents can do, how they work, their benefits, and some examples of how they can support field sales teams.
What Are AI Agents?
An artificial intelligence (AI) agent is a type of software that can handle tasks and responsibilities on behalf of users, including field sales representatives working with Salesforce. These AI agents can interact with users, make decisions, collect data, understand speech and text, answer questions, retrieve information, and complete specific actions. Since they’re based on different AI technologies, these agents can also learn from interactions over time to improve responses and deliver more accurate results.
What are the key components of AI agent technology?
Based on technologies like natural language processing (NLP), generative AI, machine learning (ML), and large language models (LLMs), AI agents can handle a variety of tasks like answering questions, solving customer issues, retrieving information, and a lot more. These technologies are what enable the architecture to function, creating an environment where an AI virtual agent uses percepts to affect the action it takes from a user input. Let’s take a closer look at the role of each of these technologies in AI agent architecture.
- Natural language processing allows AI agents to understand and process human language, whether that’s speech or text. With NLP, AI agents can decipher the intent behind a question for more natural interactions
- Generative AI powers the creative side of AI agents and is what enables them to create new content, like email drafts, content ideas, and responses to complex questions
- Machine learning is the backbone of AI agent architecture, allowing it to learn from data and improve over time, like learning customer preferences of predicting the next best course of action in a conversation
- Large language models give AI agents another layer of understanding, providing context and knowledge so that they can deliver detailed answers and complete complex tasks by instantly processing massive amounts of data
How do AI agents work?
There’s a lot that goes on behind the scenes of AI agent technology, most of which is invisible to the user. Below, we’ll take a brief look at how AI agents work and respond to users in a series of steps.
- Data collection: First, an AI agent is trained to gather data from different interactions along with customer, account, and opportunity information and other relevant factors. Once trained on a wide range of data, an AI agent can better understand queries in context.
- User input: Next, the user provides an input in the form of instructions or a command. The AI agent determines a goal from the input and may break down its actions into smaller tasks.
- Decision making: The AI agent will begin to analyze the input and organize the data. It can identify specific goals from the input and may break down its actions into smaller tasks or determine the best course of action based on previous experiences.
- Implementation: After a decision is taken, AI agents can begin to execute specific actions, such as answering a question, retrieving customer or account information, or processing a request like updating a Salesforce record. These interactions are in real-time, meaning the user doesn’t experience unnecessary delays.
- Adaption: Finally, the AI agent will continue learning and improving with every interaction, using past experiences to refine their efficiency and provide even more accurate responses in future interactions.
Types of AI Agents
Not all AI agents are built equally. Some are designed to handle more complex requests, while others are very straightforward and follow a more strict set of rules. Below, we’ll look at the main types of AI agents and some examples of how they’re used.
Simple reflex agents
This type of AI agent uses a condition to define the action. Think of it as an “if this, then that” agent. They are built to react to current conditions without memory of the past, making them perfect for straightforward tasks like creating a follow-up task or updating a Salesforce field based on specific information.
Model-based reflex agents
These agents take it up a notch by maintaining an internal model of their environment, meaning they can perceive things that aren’t immediately obvious. They don’t just react, they evaluate context and fill in gaps using stored data. For example, an AI agent that accesses customer and account information in Salesforce can use available data to better understand the context of a field sales representative’s request.
Goal-based agents
Goal-based agents are all about achieving objectives. They evaluate different paths and choose the one that best reaches their target. Think of an AI agent helping a field sales representative complete a Salesforce update by identifying the information needed and the actions required to complete it.
Utility-based agents
These agents focus on optimizing outcomes by calculating “utility” or the value of different actions. For instance, an AI agent helping a field sales representative prepare for a customer meeting might weigh different customer, account, opportunity, and deal information to provide the most relevant information.
Learning agents
Learning agents grow smarter over time by learning from past experiences. A good example is an AI agent that learns from company language, usage, outcomes, and feedback to improve its performance across interactions.
Hierarchical agents
These agents work in a team structure, with higher-level agents delegating tasks to lower-level ones. It’s like a manager breaking down a project into smaller tasks for team members, ensuring everything works toward a common goal.
Examples of AI Agents for Field Sales Teams
AI agents are much more than just customer service bots that are built to answer questions. While these types of bots are highly efficient at relieving stress on customer support teams, AI agents can also support field sales teams in different ways. Below, we’ll look at a few examples.
- Customer Meetings: AI agents can make it simple for field sales representatives to capture customer visits, meeting outcomes, important account information, and next steps through natural conversation while the information is still fresh.
- Salesforce Updates: AI agents can help field sales representatives communicate with Salesforce using their own words, turning spoken information into structured and validated Salesforce data without requiring reps to remember specific field names or processes.
- Pre-Meeting Preparation: AI agents can give field sales representatives access to customer, account, opportunity, deal, and pipeline information through conversation, making it easier to prepare for customer meetings while on the go.
- Follow-Up Tasks: For field sales teams, AI agents can help create follow-up tasks and trigger Salesforce actions through voice, making it simpler to move from a customer conversation to the next steps that need to happen in Salesforce.
What Are the Benefits of Using AI Agents?
There are many ways businesses stand to benefit from using AI agents, especially when it comes to helping field sales teams communicate information, complete tasks, and stay connected while on the go. Let’s look at a few key advantages.
Improved productivity and efficiency
AI agents can handle specific tasks and actions, which means field sales representatives are free to focus more of their time on customers, meetings, and opportunities. This can help teams capture important information from customer visits, complete Salesforce updates, and create follow-up tasks without having to stop and manually enter information later.
Reduced administrative work
With AI agents working alongside field sales teams, companies can reduce the manual work related to entering customer information, updating Salesforce records, and completing follow-up tasks. This can also help reduce delayed, incomplete, or incorrect Salesforce updates.
More current Salesforce information
Field sales representatives gather important customer and deal information throughout their day. AI agents can help capture that information while it is still fresh and turn it into structured Salesforce data, giving representatives, managers, and sales operations a more current view of customers, accounts, opportunities, and activities.
Scalability
AI agents can be configured to support different field sales workflows and Salesforce processes. As needs evolve, companies can create and adjust agents around their existing Salesforce objects, fields, forms, validation rules, terminology, and business logic.
Provides deeper sales insights
Since AI agents can capture information from field sales interactions, these systems can help make important customer, meeting, and opportunity information available in Salesforce. Companies can use this information to gain a clearer view of customer activity, deal progress, follow-up tasks, and what is happening across the field.
What Are the Challenges and Risks Involved With AI Agents?
As with every new technology, there are some risks involved. However, with careful planning and a solid implementation strategy, these risks are no reason to avoid using AI agents, but they’re still important to be aware of.
- Data privacy: With any system that gathers and processes customer and business data, it’s critical to be aware of data privacy standards and best practices and to implement a privacy and security plan to stay compliant with regulations.
- Ethical challenges: Sometimes, machine and deep learning models can lead to ethical issues and inaccurate or biased results, which is why it’s important to make sure you have quality data and human review systems in place.
- Technical and computational complexity: Companies need to consider their existing IT infrastructure, Salesforce environment, business processes, and technical requirements when implementing AI agent technology.
- Multi-agent requirements: With multi-agent frameworks where specific tasks require multiple AI agents, companies need to consider how different agents, systems, and actions work together to complete processes accurately and securely.
AI Agents: An Unlimited Future of Opportunities
The emergence of AI agents in field sales is changing how representatives communicate with Salesforce and manage information while on the go. With technologies like aiola’s Voice AI leading the way, field sales representatives can communicate with Salesforce through natural conversation while AI helps take care of manual tasks. aiola works alongside field sales teams to help them capture customer visits and meeting outcomes, retrieve customer and opportunity information, create follow-up tasks, and trigger Salesforce actions through the power of voice, making it easier for representatives to stay connected to Salesforce while they keep moving.
Book a call with one of our experts today to see how aiola is helping field sales teams communicate with Salesforce through Voice AI.