AI SDR: How To Make Your Own AI Sales Agent

AI agents are like your digital sidekicks—smart, autonomous systems powered by machine learning, natural language processing (NLP), and automation. They’re built to take over the grunt work, whether it’s managing emails, qualifying leads, or engaging with prospects—so your sales team can focus on what they do best: building relationships and closing deals.

What's an SDR?

Sales Development Representatives (SDRs) are the frontline of lead generation—they spark initial conversations, assess potential customers, and pass them on to your sales team. But let’s face it, it’s a lot of work.

Enter the AI SDR: a tireless, digital version of your best rep. It doesn’t need coffee, never sleeps, and can juggle thousands of leads without blinking.

The Benefits of Using AI SDRs

AI SDRs are more than just time-savers—they’re game-changers for your sales process. Here’s how they give your sales team an edge:

• 24/7 Lead Engagement: AI SDRs never clock out. They can engage leads at any time, ensuring no opportunity slips through the cracks.
• High-Volume Efficiency: Whether it’s 10 leads or 1,000, AI SDRs can handle outreach at scale without losing steam.
• Personalized Outreach at Scale: Using data and insights, AI SDRs can craft messages that feel tailored—even for thousands of prospects.
• Rapid Response Times: AI agents can respond to inquiries or follow up on warm leads immediately, increasing conversion chances.

Pitfalls to Avoid

While AI SDRs offer huge potential, there are a few common pitfalls to watch out for. Here’s how to avoid some of the most frequent mistakes when implementing AI into your sales process:

• Over-Automating: It’s tempting to automate everything, but remember: AI SDRs should assist—not replace—human touch. Over-automation can lead to robotic, impersonal outreach that turns prospects off. Striking the right balance between automation and human interaction is key to keeping things authentic.
• Failing to Train Your AI Properly: An AI SDR is only as good as the data you feed it. If it’s not trained with the right information—like your buyer personas, FAQs, or ideal customer profiles—it won’t engage effectively. Ensure your AI has access to the proper resources to understand the nuances of your audience.
• Ignoring Data & Analytics: One of the great advantages of using an AI SDR is its ability to track and learn from every interaction. If you’re not regularly analyzing the data to tweak your AI’s performance, you’re missing out on valuable insights. Make sure to monitor your AI’s progress and adjust your strategy to continuously improve results.
• Underestimating the Need for Human Oversight: While AI SDRs can handle the bulk of the work, human oversight is still crucial. Make sure your sales team is actively reviewing the AI’s interactions, ensuring that it’s engaging prospects in the right way, and stepping in when necessary. AI may be great at initiating conversations, but humans excel at closing deals.
• Not Setting Clear Expectations: Set realistic expectations for what your AI SDR can and can’t do. It’s a tool designed to optimize your sales process, not take over completely. Be clear with your team on how to integrate AI into their workflow without losing the personal touch that builds trust with prospects.

Avoiding these common pitfalls ensures that your AI SDR remains an effective and valuable part of your sales team, rather than a source of frustration. With the right balance of human and AI collaboration, you’ll see impressive results.

Building Your Own AI SDR

Good news—Scout makes creating your own AI SDR simple and efficient. In this example, we’ll explore an AI-powered SMS bot using Scout’s Twilio integration.

You can grab this template here, add it to your workspace, and tweak it to fit your needs. Let’s break down how this workflow operates, step by step, to see how it all comes together.

1. Input Block

• Purpose: This block is the entry point for user interaction. It captures the user's message and the sender's phone number. These inputs are crucial as they drive the rest of the workflow.
• How it works: When a user sends a message, this block records the message content and associates it with the sender's unique identifier (phone number). This setup ensures that each interaction is personalized and traceable.

2. Key-Value Storage Block (Read Past Messages)

• Purpose: To retrieve past interactions with the sender, if any exist. This helps maintain context and continuity in the conversation.
• How it works: The block uses the sender's phone number as a key to look up any existing conversation history stored in a key-value database. If a history exists, it's retrieved as a JSON object. This step is akin to checking a customer's file to see previous interactions.

3. Javascript Block (Conversation History)

• Purpose: To process the retrieved conversation history and focus on the most recent interactions.
• How it works: The JavaScript code in this block extracts the last four messages from the conversation history. This ensures that the AI has the most recent context when generating a response. It's like quickly reviewing the last few emails in a thread to catch up on the conversation.

4. Query Collection Table Block

• Purpose: To search through a database of information to find relevant data that can help answer the user's query.
• How it works: This block performs a hybrid search, combining semantic (vector-based) and textual/keyword (BM25) relevance to find the best matches for the user's message. It queries a specific collection table using the user's message as the search term. This is like having a smart search engine that understands both the meaning and the keywords in the user's query.

5. LLM Block (SDR response)

• Purpose: To generate a response to the user's message, using the context and information available from prior chat history and the knowledge base in your Scout collection.
• How it works: The block uses a language model (like GPT-4) to craft a response. It takes into account the user's message, relevant information from the collection, and the recent conversation history. The model is instructed to be friendly and helpful, and it also assesses its confidence in the response. If the model is unsure, it will indicate this, ensuring that only accurate information is provided.

6. Send Twilio Message Block

• Purpose: To deliver the AI-generated response back to the user via SMS.
• How it works: The block uses Twilio's messaging service to send the response. This step is crucial as it completes the interaction loop, ensuring that the user receives timely and relevant information.

7. Javascript Block (Save Messages to Chat History)

• Purpose: To update the conversation history with the latest interaction.
• How it works: This block appends the user's latest message and the AI's response to the existing conversation history. This updated history is then prepared to be saved for future reference. It's like adding a new entry to a logbook, ensuring that the conversation's continuity is preserved.
  

8. Key-Value Storage Block (Update Convo History)

• Purpose: To save the updated conversation history for future interactions.
• How it works: The block writes the updated conversation history back to the key-value store, using the sender's phone number as the key. This ensures that the next time the user interacts, the AI has access to the full context of past interactions. It's like filing away a document for future reference, ensuring that no information is lost.

Wrapping It Up

An AI SDR can transform your sales process by streamlining the repetitive tasks and empowering your team to focus on what matters most—connecting with prospects and driving results. With the right setup, it delivers efficiency, consistency, and smarter outreach, all while keeping your sales funnel moving.

Scout’s workflow makes it simple to build one tailored to your needs, like the SMS bot example we’ve walked through. Ready to see it in action? Try this template out on Scout today and discover how an AI SDR can elevate your sales game.