Let’s be real—most AI outbound agents sound the same.
But when you actually put them to work, the results can be wildly different. The secret? It’s all in the data, the messaging personalization, and how easy it is for your team to use.
This checklist is here to help you cut through the buzzwords and figure out which AI agent will actually drive pipeline for your team—not just add another tool to your stack.
Step 1: Evaluate Data Accuracy
The success of your initiative depends on the data that the AI agents use, such as: 3rd party data like signals, your CRM data, product usage data, etc.
If the data isn’t accurate, AI agents won’t be effective, or worse, they could damage your brand & reputation. As the saying goes, garbage in, garbage out.
To assess the vendors’ data accuracy:
- Identify 1-3 specific campaigns you want to start immediately with the AI agent. The results will be more relevant and real for your team. And once you pick a vendor, you can turn them on immediately, reducing the time to impact.
- Provide each vendor with the same list of accounts and contacts so that you can compare apples to apples.
For example:
- List of Customer accounts
- List of Target accounts
- List of Customer contacts
- ICP criteria
- Persona criteria
Template to Evaluate AI Vendors' Data Accuracy:
Step 2: Evaluate AI messaging
Ask each vendor to provide you with 20-30 sample emails for the campaigns to review.
Even though messaging is subjective, you can assess for the following:
- Does the AI articulate the pain points I'm solving, the persona-based messaging, and any industry-specific messaging well?
- Are the emails truly personalized?
“Bad AI” personalization is simple scraping output -- like, if [this] then [that] without considering the holistic context around that account or buyer. That’s why they sound robotic or completely irrelevant.
“Good AI” personalization:
- Combines all the data points (signals) of what is happening at that account and buyers and any existing or past relationships you have with them
- Knows which data points (signals) are more important
- Can reference back to its previous messaging to the buyers so that it sounds natural & human-like
For example:
Template to Evaluate AI Messaging:
Step 3: Evaluate usability
One of the key factors for any new initiatives is strong user adoption. For AI initiatives, usability isn’t only for the end-users (i.e. SDRs and AEs) but also for the business users & admins (i.e. SDR/marketing/sales managers and Ops)
Depending on the level of AI and technical skills of your existing team, look for solutions that most of your teams can use, maintain, and scale.
Overly technical solutions could cause:
- Challenges in hiring & backfilling the technical resource
- A bottleneck for the existing technical resource (or dependencies on technical agency)
- Your team spending time tooling & bug fixing instead of focusing on the business outcomes (i.e. generating more pipeline)
Template to Evaluate AI Solutions' Usability:
Other Considerations
Choosing the right AI outbound agent doesn’t have to be a gamble. By focusing on real data accuracy, human-like messaging, and usability across your team, you’ll avoid adding just another tool—and instead, drive real pipeline impact.
If you're ready to see what a signal-based, high-precision AI outbound agent can actually do, check out UserGems. We built Gem-E to make your best campaigns run themselves—accurately, contextually, and at scale.
👉 Request a demo to see it in action.
