What is AI SDR?

The economics of human SDR teams are under pressure. According to Bridge Group research, the average SDR costs $95,000-$130,000 per year (fully loaded: salary, benefits, tools, management overhead, office space). The average SDR ramp time is 3.2 months, and annual turnover exceeds 35%. Each departure costs the organization 6-9 months of lost productivity.

AI SDRs promise to address these economics while scaling outbound capacity. An AI SDR can operate 24/7, process thousands of signals per hour, and generate personalized outreach for hundreds of prospects daily — capacity that would require a team of 5-10 human SDRs.

However, the category is still maturing. Current AI SDRs excel at research, personalization, and initial outreach but struggle with nuanced qualification conversations, objection handling that requires creative problem-solving, and relationship building. The most successful implementations use AI SDRs as a "co-pilot" — handling high-volume initial outreach while human SDRs focus on warm follow-up, complex qualification, and meeting scheduling. Gartner predicts that by 2027, 30% of outbound messages will be generated by AI agents, up from under 2% in 2024.

AI SDRs operate through a multi-step autonomous workflow.

**1. Account and contact selection:** The AI ingests the ICP definition and target account list, then identifies specific contacts to engage based on role, seniority, and relevance. It prioritizes accounts showing active buying signals — funding, hiring, technology changes, or intent data spikes.

**2. Research and signal synthesis:** For each contact, the AI gathers available data: company news, financial filings, technology stack, social activity, job changes, and competitive landscape. It synthesizes these data points into a prospect brief.

**3. Message generation:** Using the prospect brief and configured messaging frameworks, the AI drafts a personalized outreach sequence — typically 4-7 touches across email and LinkedIn over 2-3 weeks. Each message references specific signals and connects them to relevant value propositions.

**4. Send and monitor:** The AI manages sending cadence, respects timezone preferences, monitors deliverability metrics, and adjusts send times based on engagement patterns. It tracks opens, clicks, and replies to determine sequence progression.

**5. Response handling:** When a prospect replies, the AI classifies the response (interested, objection, question, not interested, out of office) and either drafts a follow-up, escalates to a human rep, or removes the prospect from the sequence.

**6. Handoff:** Qualified leads — those who express interest and meet threshold criteria — are routed to human AEs with full context: the signal that triggered outreach, the conversation thread, and the prospect brief.

Autobound provides the intelligence layer that powers AI SDR workflows. While some AI SDR platforms focus narrowly on message generation, Autobound delivers the full signal-to-message pipeline: 400+ signals detected by the Signal Engine, relevance-ranked by the Insights Engine, and converted into personalized outreach by AI Studio. Teams can use Autobound as a standalone AI SDR assistant (via Chrome extension or Salesforce integration) or embed its intelligence into their existing AI SDR platform via the Generate Insights API.