Field guide / AI SDR systems
Build AI SDR systems around pipeline quality.
AI SDR systems can research accounts, qualify inbound demand, route leads, draft outreach, and prepare follow-up. Design each job to move faster without lowering relevance, trust, or control.
Working model
Inbound and outbound need different workflows
Inbound starts with buyer intent. Outbound starts with account selection. They share context and controls, then branch into separate workflows.
- Inbound / Signal
A buyer acts
Form, reply, product event, event scan, or content intent.
- Inbound / Decision
Qualify and route
Resolve identity, fit, urgency, owner, and next action.
- Shared / Context
Know the account
Product fit, CRM history, policy, conversations, and evidence.
- Outbound / Selection
Choose the account
Start with a defined segment and a credible reason now.
- Outbound / Action
Research and draft
Prepare a point of view for review or controlled delivery.
01
What is an AI SDR?
An AI SDR is a workflow or set of agents that performs bounded sales development tasks using account data, company knowledge, and sales policy. It can assist a human rep, prepare work for approval, or execute limited actions under clear rules.
AI SDR can mean autonomous outbound sending, inbound qualification, account research, routing, follow-up preparation, or meeting handoff. Define the job before evaluating the system. Activity metrics alone can hide whether it creates qualified conversations.
02
Inbound AI SDR: respond while intent is fresh.
Inbound work begins with a buyer signal. Speed matters, but the system must first resolve who the person is, whether an account already exists, which segment and territory rules apply, whether there is an open opportunity, and what response is appropriate.
A practical inbound workflow can:
- normalize and enrich the person and account without overwriting trusted CRM data
- check duplicates, ownership, exclusions, and existing customer relationships
- score fit and intent from evidence a rep can inspect
- route to the right queue with a reason that a rep can verify
- prepare a response or booking path that follows approved claims and policy
03
Outbound AI SDR: earn the right to contact an account.
Outbound work should start with account selection, not content generation. Define the segment, the business hypothesis, acceptable signals, exclusions, and the purpose of the conversation. AI can then research the account and map evidence to a relevant point of view.
A strong research output separates facts from inference and shows its sources. It also explains why the account belongs in the sequence now. That makes human review faster and gives the system something concrete to learn from when the rep edits or rejects the draft.
Personalization is not a sentence about a prospect's website. Relevance is a defensible connection between their situation and the problem you solve.
04
Keep people in control of claims, exceptions, and relationships.
Human review is most valuable where context is incomplete or the cost of a mistake is high. New segments, strategic accounts, sensitive industries, unusual pricing, active opportunities, and negative customer history should have clear escalation rules.
The review interface should show the evidence, proposed action, uncertain fields, and relevant policy together. Record why the rep changed the draft. A model mistake and a strategy change need different fixes.
05
Measure accepted work and qualified outcomes.
Use message count, research count, and response speed as health measures. Judge the system by accepted work, corrected routing, reply quality, and contribution to qualified pipeline.
Compare cohorts where possible. A workflow can increase replies while reducing meeting quality, or save rep time while shifting cleanup work to RevOps. The scorecard should make those tradeoffs visible.