Buying an AI SDR is not the same as choosing a database, sequencing tool, or copy assistant. The system may research accounts, decide who belongs in a queue, draft claims in your name, act through connected tools, and create records that sales teams must trust. A convincing workflow demonstration is useful, but it is not evidence that the workflow will remain controllable after launch.

Start with a criteria-led scorecard once you have a shortlist. If you are still exploring the market, begin with a broader AI SDR tools overview or a guide to comparing AI SDR approaches. The purpose here is narrower: make a defensible decision among finalists by asking for proof that can be inspected by the people who will own the outcome.

For every question, apply the same evidence score:

A possible total is 24. Use it as a comparison aid, not as a permission slip. A high total cannot compensate for a safety, ownership, or exit-readiness gap. Flag any score of 0 in those areas for resolution before a pilot or purchase. The discipline is simple: request artifacts, test a realistic scenario, and record what was actually shown.

1. Can it show where account and contact research came from?

Research provenance determines whether a rep can defend why an account was selected and why a message says what it says. Without provenance, a plausible-looking brief can hide stale information, an unsupported inference, or a source that should not have been used. The buyer needs to understand both what the system knows and what it does not know.

Request a live account brief that lists each source, retrieval timestamp, and the specific input used in the recommendation. Ask the team to show missing-data behavior: does the system state uncertainty, omit the point, or fill the gap with a guess? Ask for coverage limits and a correction path that lets an authorized user fix a fact, identify the correction, and prevent the same issue from recurring. Inspect a recent example, not a prepared ideal case.

2. Does research become a testable buying hypothesis?

Personalization is not automatically relevant. The useful chain is signal to hypothesis to message angle: a source-backed event or condition suggests a business problem, which suggests a specific and testable reason to contact the buyer. That chain helps a team distinguish an informed message from generic references to a company page.

Request several examples that display the original signal, the resulting buying hypothesis, and the message angle. Then request rejected examples. The team should be able to show weak, outdated, or ambiguous evidence that was excluded and explain why. Ask how the system handles a hypothesis that cannot be supported by the available research. A safe answer is not a more elaborate claim. It is a narrower message, a request for review, or no outreach.

3. Can we define qualification rules before outreach begins?

Qualification is an operating policy, not a preference that should live only in a kickoff deck. Your ICP, exclusions, territories, thresholds, disqualification rules, and account ownership constraints determine who enters the queue. If those rules cannot be changed and checked, the organization inherits a black-box audience decision.

Request the editable rule set. Inspect how it represents ICP requirements, named-account or customer exclusions, geographic territories, employee or revenue thresholds where relevant, and explicit disqualification logic. Ask for a sample queue that shows why each record was included or excluded. Finally, request an exception log that captures rule conflicts, manual overrides, and edge cases. The ability to see exceptions matters as much as the default path.

4. Can we set hard writing controls?

Outbound copy can create legal, brand, and trust problems even when the target list is correct. Writing controls need to do more than suggest a tone. They must constrain claims that may be made, language that is prohibited, voice requirements, and context that must be included before a message can move forward.

Request a live policy configuration showing approved claims, prohibited claims or phrases, voice guidance, required context, and approval gates. Ask the team to change a policy and generate a before-and-after example from the same account inputs. Inspect configuration and version history: who changed the rule, when, and what content was affected? A static brand document is helpful, but it is not proof that the operating system enforces it.

5. Can a human inspect and override every message path that matters?

Human control is practical only when review and intervention work at the speed of an active campaign. Teams need to review a draft, edit it, pause a queue, escalate a questionable case, and understand what happened after an override. A nominal approval button is not enough if the audit trail is unclear or a stop action is hard to reach.

Request a draft-to-send walkthrough. Have the operator edit one message, reject another, pause a campaign or segment, and escalate a case. Inspect the audit log for who changed what and when. Ask which paths can proceed without review, which require approval, and how those settings are altered. Access controls, logging, and audit review are also reasonable procurement criteria under CISA Secure by Design.

6. How does it protect sender reputation and deliverability?

Deliverability is an operating responsibility, not a dashboard number. Outreach can affect domain and mailbox reputation, so buyers need clear controls for sending behavior, list hygiene, suppression, bounce and complaint handling, and remediation when health changes.

Request the sending-control configuration, including volume limits, schedules, and any account or mailbox guardrails. Inspect how suppressions are received, applied, and preserved across connected systems. Ask to see a bounce or complaint workflow and sender-health reporting that a real operator uses. Get the name and role of the remediation owner, whether that person is internal or external, and the escalation path. Do not accept vague statements that deliverability is “handled.”

7. What data enters the system, and how is sensitive data handled?

An evaluation should map data before integration makes the answer harder to unwind. The relevant questions are minimization, permissions, retention, access, separation of customer data, and deletion. Security leaders need enough detail to determine whether the proposed use fits their own policies and risk review.

Request a data-flow diagram showing collection, processing, storage, connected systems, and outbound destinations. Ask which fields are necessary for the workflow and how sensitive data is prevented from entering prompts, outputs, or logs when it should not. Inspect role-based access controls, permission boundaries, retention settings, access-review process, and a deletion procedure. Request evidence of tenant or customer-data separation where applicable. Secure defaults, MFA, SSO, and logging should be examined as concrete controls, not assumed from a security questionnaire.

8. What protects the agent from unsafe instructions and unsafe actions?

AI sales agents may encounter untrusted web content, emails, CRM text, and instructions embedded in documents. The evaluation must cover prompt injection, sensitive-information disclosure, excessive agency, and misinformation. These are useful categories from the OWASP GenAI Top 10 2025, not reasons to accept a generic assurance.

Request a threat-model summary and examples of adversarial tests. Ask how untrusted instructions are separated from approved operating policy, what tool permissions are available, and which actions require human approval. Inspect action allowlists, output checks, autonomy limits, and the escalation behavior when content is suspicious or evidence is weak. Ask the team to demonstrate a scenario in which the system declines an unsafe instruction rather than merely describing the expected behavior.

9. Who owns the workflow after launch?

A pilot can succeed through unusual attention and still fail as an operating model. Ownership must be explicit for research rules, copy policy, queues, integrations, change control, deliverability response, and incident escalation. “The vendor manages it” or “marketing owns it” is not a RACI.

Request a RACI that names accountable and responsible roles on both sides. Ask for the onboarding plan, escalation path, change-request process, and operating cadence for reviewing quality and risk. Inspect what happens when priorities conflict, such as sales requesting more volume while deliverability or qualification quality declines. The NIST AI Risk Management Framework provides a useful rationale for tying governance, context mapping, measurement, and management to named owners.

10. Can it fit our systems without creating a shadow process?

The workflow should strengthen the CRM and existing revenue process, not create a second system that only one team can see. A shadow process produces duplicate records, unclear handoffs, missing activity history, and disputes over which system is the source of truth.

Request an integration map that identifies every system, data direction, source of truth, field owner, and handoff. Inspect CRM activity logs, synchronization behavior, and deduplication rules. Ask for a failure-and-retry example: what happens when a sync fails, who sees it, and how is the record reconciled? Confirm what reaches the CRM, when it arrives, and whether sales can see the context behind an outreach action. For operating context, review how AI sales agents for B2B outreach differ from standalone drafting tools.

11. Can we measure quality, not just volume?

A growing activity count is not a quality system. Buyers need definitions and raw events for research accuracy, qualification precision, message quality, deliverability, reply classification, meetings, and pipeline contribution. Without definitions, metrics can conceal changes in list mix, attribution, or workflow behavior.

Request the metric dictionary, raw-event visibility, cohort views, baseline method, and a workflow for diagnosing a decline. Ask how a team distinguishes a research issue from a qualification issue, a message issue, or a sender-health issue. Inspect whether edits, overrides, suppressions, replies, and CRM outcomes can be traced to the relevant account and cohort. The NIST AI RMF Playbook is a useful reason to ask for concrete actions and retained evidence rather than high-level performance assurances.

12. Can we exit cleanly if the system is not the right fit?

Exit readiness protects the organization before a contract is signed. It clarifies whether you can recover the work product, remove access, stop outreach, meet retention obligations, and transition without losing control of domains, data, copy, or CRM records.

Request an offboarding runbook and inspect a sample export. Ask how access removal, campaign shutdown, retention and deletion, and transition support work in sequence. Confirm contractual treatment and ownership of sending domains, data, copy, configurations, research artifacts, and CRM records. Request the deletion-confirmation process and who receives it. A clean exit is not a sign that you expect failure. It is evidence that ownership boundaries are understood.

How to run the scorecard with a buying committee

Assign revenue, demand generation, RevOps, and security leaders to score the same evidence independently, then reconcile differences in a short review. Record the artifact shown, date, scenario, score, owner, and open question for each criterion. Do not average away a serious objection. If a security control, operating owner, or offboarding path is absent, document the gap and the condition required to close it.

This process also makes a pilot more useful. Turn low-scoring items into explicit acceptance tests with a named owner and deadline. For broader rollout questions, compare the workflow with an AI SDR guide, the operating tradeoffs in AI SDR versus human SDR, and B2B SaaS-specific outbound considerations in AI SDR for B2B SaaS outbound. If a finalist can show the evidence, controls, and operating boundaries your team needs, the next conversation can be specific: Book a demo.

FAQ

What is the best way to evaluate an AI SDR?

Evaluate it against the actual work it will perform: research, qualification, writing, human control, deliverability, data handling, integrations, measurement, and offboarding. Use a 0 to 2 evidence score for each area and require live, inspectable proof rather than relying on claims or prepared slides.

What score should an AI SDR need to pass?

There is no universal passing total because risk tolerance and workflow scope vary. Use the total to compare finalists, but set non-negotiable gates for safety, ownership, and clean exit. A missing control in one of those areas should trigger a remediation requirement, not be offset by strong scores elsewhere.

Should security review happen before or after a pilot?

Conduct enough security and data review before the pilot to define permitted data, access, integrations, and autonomy limits. Use the pilot to test those controls in the intended workflow. Do not treat a pilot as an exception to data handling, access, or incident-response expectations.

What evidence is stronger than a demo?

The strongest evidence is a live, inspectable configuration or record tied to a realistic scenario: a source-backed account brief, editable policy, audit log, suppression workflow, integration failure record, raw event view, or export and deletion procedure. A demo becomes decision-grade when buyers can inspect the controls behind it.