AI Lead Generation: A 4-Layer Multipoint Verification Workflow

AI-generated records may include information from various sources that may not always be current or consistent. It could be that someone is with the wrong company. Or someone’s title doesn’t match their current role.

This article is about the verification layer, after AI-powered prospecting and enrichment. It describes what needs to be verified in an AI lead generation process, what data points to verify at each step and how multipoint verification reduces the chances of erroneous records reaching sales.

AI lead generation is the application of artificial intelligence to find potential prospects and enrich their records with information. These tools can help you find companies, suggest contacts, find job titles, enrich company info and provide contact info. This reduces the manual work involved in prospecting research. But the information generated still needs to be checked before sales teams use it.

AI-generated contact data should be treated as a starting point for verification.

AI may find the right company but match it with the wrong person. The job title might be out of date, the email address might not be valid or the phone number might not be with that person anymore.

AI-generated records can come from various sources, some of which might not be updated. These can be checked via a verification tool before they get into your CRM or outreach process.

AI is able to generate and revamp a large volume of leads in a short span of time. But as the lead volume increases, it gets harder to verify the accuracy of AI-generated leads, as each record needs to be verified for identity, company, role, email and phone data.

At lower volumes, teams may manually review these fields before a lead moves to the next stage. That approach becomes more difficult to sustain as volume grows. Outdated role, wrong company, invalid email, wrong phone number can get into the CRM without a review from anyone.

That’s one reason why AI-generated leads need to be vetted before they’re sent into downstream workflows. AI errors can propagate through lead enrichment, segmentation, routing and outreach as fast as accurate records.

A structured AI lead enrichment workflow adds checkpoints before those records hit sales or outreach. Each checkpoint can validate a specific piece of the lead and flag missing, conflicting or risky data for further review.

Multipoint verification is the process of checking an AI-generated lead against several layers of verification before it can enter your CRM or outreach workflow.

AI is able to create a record that appears complete, but there are many different ways different fields can fail. Sometimes it’s the wrong company, the wrong role, an invalid email, or a phone number that no longer matches the record.

A multipoint workflow checks the lead at following stages:

Each layer is looking for a different risk before the lead can go to the next phase.

The first layer checks the technical usability of the AI-generated or AI-enriched contact details.

AI emails may look legitimate but be fake, disposable, a role-based email, or tied to a catch-all domain. The phone number might be complete but invalid, related to an unexpected line type, or not matching any available telecom data.

This is where separate email and phone verification is necessary. Clearout Email Verifier checks email addresses with 20+ real-time checks and flags conditions like invalid, disposable, role-based and catch-all. ClearoutPhone checks if a phone number is valid and identifies line type, carrier and location. If the basic phone number data isn’t enough, Smart Validation helps with a deeper telecom check.

These checks are to address a specific problem in AI lead generation: they test if the contact information is valid for the channel of outreach before that information is put into a sequence.

The technical contact validation does not verify the correctness of the data for the person, company and role.

AI tools are trained on a variety of sources, some of which may have conflicting or out-of-date information. For instance, an AI-generated record could find the right person but assign them to the wrong company or job title.

This is waterfall enrichment by cross-referencing key fields against other sources.

Waterfall enrichment does this by cross-referencing key fields against other sources. If sources disagree, take the information that is more certain and indicate the disagreements.

For instance, before you send an AI record to outreach, if it is about a VP in Company A, you should verify that position on their professional profile or from a company source.

A contact could be correct but not ready for active outreach.

Verify the person, company, role, email and phone. Compare the account to your ICP and current signals you have at your disposal. They could be hiring, funding events or conducting research in a relevant category.

A verified decision maker at a target company may still need to stay in nurture if there’s no current signal that justifies active outreach.

This layer adds an extra check to the AI lead generation workflow: Does this verified contact correspond to a contact now?

Not all records are required to go through automated checks.

If there are leads with high value or company/role information, they may be reviewed manually before any outreach. The reviewer can verify that the person is still in the stated position or that the information can be used safely.

A different type of risk is removed at each step before the record advances.

AI can help you discover prospects and fill in their details in the record. But it is unverified until the correct checks are made.

So, what kind of checks are needed and how to automate them? Let's dive in!

You can run email addresses through Clearout Email Verifier, while phone number verification can be done via ClearoutPhone. Email Verifier helps you verify that the email is valid, invalid, disposable, role-based and catch-all. ClearoutPhone validates a phone number, shows what kind of line type it has, the carrier and location as well.

Make sure the person is real and their identity can be confirmed using sources such as LinkedIn or the company's website. If any of these checks are not met, mark this record for review rather than making it a valid contact.

Compare validated fields to each other.

For instance, if a person uses a company domain, then their email domain should be the same as company’s domain. If there's conflicting information, this indicates that it's time to review the information manually or otherwise verify it.

Sort out the verified contacts from the records to be reviewed. If the same hiring manager appears under two email addresses or company records, merge the useful details into one record to avoid contacting them twice. If someone has moved to another company, don't automatically discard them. Check whether their new role is your ICP and update the record accordingly.

Where possible, keep the verification status to let the sales team know which contacts have already been verified.

Below we have listed why you need phone and email verification if generating leads through an AI tool:

Monitor metrics like:

These metrics show where lead enrichment accuracy and AI prospecting accuracy need to be addressed. They can also track these over time to show if your AI lead generation verification process needs to be altered or if older records need to be re-verified.

AI can increase lead generation speed but you might not have contact records in the list that are ready for outreach and sales. So, you must confirm if the person, their role, company, phone and email details are valid.

To verify email address, you can run it through Clearout Email Verifier as it can see email validity and risk signals for the contact details. Additionally, ClearoutPhone can help in identifying phone validity, line type, carrier and location. Re-check this information if you are reusing old records or their contact information has changed.