How to Use AI to Re-Engage Your Cold Leads

What if you could improve your pipeline with leads you generated last year?

Traditionally, these leads would either sit untouched or require manual re-engagement efforts that rarely pay off. But using AI for cold lead engagement, you can easily take old leads that were metaphorically collecting dust in the CRM, and add them to your pipeline.

AI can re-engage your cold or aged leads automatically, qualify them, and deliver warm conversations straight to your sales team.

If you’re ready to use AI for re-engagement, this blog is for you. We’ll talk about how to follow up with cold leads using AI, how AI differs from automation, real-life use cases, and best practices.

Why cold lead engagement matters

Cold lead follow-up, also called lead re-engagement, refers to reaching out to leads that didn’t convert at first. These leads can be old: think 30 days to multiple years.

While these leads often just sit in the CRM, untouched, a small percentage will convert if effectively re-engaged.

Verse data on over 5 million leads shows that 5.2% of cold leads respond, and 1.3% qualify.

However, cold lead engagement is often deprioritized for several reasons. Compared to new leads, old lead engagement generally has very low conversion rates. 

This doesn’t mean they cannot be a source of revenue, but sales teams always want to focus on opportunities that have the highest chance of converting. Cold lead outreach is time-consuming and many leads will not reply. 

This logic makes sense; sales teams should prioritize new leads. At the same time, completely neglecting old leads effectively leaves money on the table. Just because a lead wasn’t ready or didn’t convert at first, doesn’t mean it can’t convert later.

This is where AI comes in to support sales teams and drive revenue through re-engagement.

How AI works for cold lead engagement

AI, specifically AI SDRs (artificial intelligence sales development representatives) work very well for cold lead engagement. An AI-powered re-engagement strategy requires little to no effort from your team, and is highly scalable for any amount of aged leads sitting in your database. This makes it a highly cost-effective solution for re-engagement.

When following up with cold leads, AI acts as a real SDR would, reaching out to every single lead with personalized messaging.

The difference is that AI:

• Never misses a lead
• Works at any scale
• Qualifies leads automatically
• Uses consistent messaging
• Does not burn out

AI SDRs for cold leads work by automatically reaching out to these leads via SMS or email with customizable scripts. If the lead replies, the AI SDR can then qualify them based on your criteria such as interest, budget, timeline, etc.

From there, the AI SDR can pass engaged and qualified leads to your human sales team.

AI SDRs differ from traditional email automation, or even SMS marketing campaigns, because they can have two-way, interactive conversations with cold leads. This is a more humanized, engaging approach than sending one-way SMS or email blasts. Not only can AI SDRs answer questions that the lead may have, they can intelligently qualify the lead right away.

How to follow up with cold leads using AI

When using an AI SDR (like Verse) to re-engage cold leads, there are a few necessary steps:

• Identify cold leads in your CRM that you want AI to reach out to. For example, you could take a set of leads that haven’t replied in 30 days.
• Segment leads based on last interaction, source, or product interest to help the AI use more personalized and context-aware messaging. The more personalized the AI can be, the more likely you are to get responses.
• Create lead qualification criteria for the AI so it understands what kinds of leads you want passed to your sales team. AI solutions like Verse can qualify leads through conversation. 
• Build re-engagement scripts that the AI can follow. It’s best to test different types of messaging to see what works best. 

From there, the AI will attempt contact with every old lead you select, using your qualification criteria as a guide to pass good leads to your sales team.

Benefits of AI for cold lead engagement

AI for sales, including AI SDRs, should be used as a tool to support sales teams rather than a replacement. An AI SDR conducts outreach as a traditional SDR would, with multiple benefits for your entire human sales team.

Re-engagement can be painful because the engagement and conversion rate is often low. AI for re-engagement spares your human sales team from wasting time on unqualified or unresponsive leads. 

In addition, AI for re-engagement:

• Improves follow-up consistency (no leads fall through the cracks)
• Works 24/7 to re-engage leads instantly, whenever they are available
• Personalizes messages based on lead data and behavior
• Saves human time, boosting team efficiency
• Reactivates aged leads your team forgot about

Cold leads are tough to win back. They’re often less responsive, harder to read, and more skeptical of generic outreach. That’s why they need more context, more relevance, and more responsiveness, and this is exactly what AI is built for.

AI can reopen the conversation in a way that feels personal and timely, without wasting your sales team’s time on leads that aren’t ready.

AI vs. traditional automation for re-engagement

When it comes to following up with cold leads, most sales teams have used some form of traditional automation. This might be drip email campaigns or templated SMS workflows. However, using AI for re-engagement has several advantages over traditional automation.

While traditional automation generally relies on static, one-way email drips, AI enables two-way, real-time conversations.

Traditional sales automation tools work by sending pre-written messages to leads at set intervals. These tools are useful for maintaining some level of contact with cold leads, but they lack the ability to adapt or respond.

On the other hand, conversational AI both responds and adapts quickly, while working at any scale. 

AI-driven lead engagement is basically an upgraded version of traditional automation. Instead of sending one-size-fits-all messages on a timer, AI uses conversational capabilities to re-engage cold leads in two-way conversations.

Comparison: AI vs. traditional automation

Use cases of AI for re-engagement

AI-powered re-engagement can transform how teams across industries manage their cold leads. Here are some real-world examples of how AI can revive lead conversations.

1. Reactivating cold inbound leads

As we’ve talked about, a common use case for AI SDRs is reaching out to leads that filled out a form weeks or months ago but then went silent.

For example:

A home services company receives thousands of form submissions per month, but about 30% of those leads ghost. Rather than letting them sit idle, they use an AI SDR to re-engage these leads after 7–14 days of inactivity. The AI follows up via SMS, answers basic questions, and qualifies any re-engaged leads before handing them off to a rep.

2. Re-engaging aged leads

Most CRMs are full of old leads: from past months, quarters, or from years ago. You can use AI to easily re-engage these leads and gauge their interest. This can add new pipeline from old leads, saving money and time.

For example:

A mortgage lender launches an AI campaign to re-engage all leads over 90 days old. The AI reaches out via text, asks if they’re still in the market, and gathers updated information (e.g., credit score range, loan type). Qualified leads are routed to the right loan officers.

3. Bringing back leads lost in the funnel

Some prospects stall at critical points: for example, ghosting your sales team, or failing to submit an application.

Through integration, AI can monitor leads to see if they fall off during the buyer’s journey. If engagement stops, the AI can re-trigger outreach at these points, using customized messaging to revive interest and push them toward the next step.

For example:

An insurance provider notices that many prospects start a quote online but drop off before completing it, often right before entering more detailed information like vehicle history or personal data.

To recover these leads, the company implements conversational AI. When a user stalls for more than 24 hours, the AI sends a personalized message via SMS, like:

“Still thinking about coverage? We noticed you didn’t finish your quote—let us know if you have questions or need help picking the right plan.”

4. Taking over delayed lead outreach

Some teams don’t want AI reaching out first. However, if your team can’t make contact within a certain period of time (for example, a few hours or a few days), conversion rates drop off. With delayed lead outreach, AI can take over trying to make contact at this time. Your team can continue to focus on the highest-conversion opportunities.

For example:

A real estate agency has their sales team reach out to incoming leads within an hour. However, any lead that doesn’t respond to outreach within 24 hours is less likely to convert. These leads are passed to an AI SDR, which reaches out after 24 hours.

5. Nurturing unready leads

Not every lead is ready to buy right away, but that doesn’t mean they should be ignored. AI can periodically check in with long-term leads to keep your brand top-of-mind. This can also be automatically scheduled based on previous conversations (see below).

For example:

A solar provider uses AI to re-engage leads. If a lead replies that they are not ready and they will be ready come summer, the AI will automatically reach out at that time to check back in, via SMS, ask if their situation has changed, and update the CRM accordingly.

Best practices for using AI for cold lead engagement

To ensure that your AI solution is working as effectively as possible with minimal risk, your team should implement AI best practices.

• Use human-in-the-loop. With any AI solution, human oversight is necessary for risk mitigation and quality assurance. A real person should always be auditing the AI’s behavior and be available to step in and take over the conversation. At Verse, our fully-managed AI comes with human-in-the-loop.
• Customize and improve scripts. By monitoring performance over time, you can compare messaging in order to improve the AI’s scripts and make it even more effective. At Verse, our dedicated scripting team helps with this.
• Re-engage at the right times. As with any sales outreach, it’s important to contact leads at the right time of day, week, and even year to heighten your chances of getting a response.
• Use past data to personalize outreach. It’s important that the AI is context-aware, especially with leads that were previously engaged. For example, the lead had almost purchased, the AI should not speak as if the lead has never heard of your brand before. In fact, it should try to find out why they backed out of a purchase.
• Follow up over time. Re-engagement is tricky because response rates are low. Follow-up is key to getting as many replies as possible. Create a follow-up schedule and be sure to spread it out so you’re not inundating your leads.

Key takeaways: How to follow up with cold leads using AI