What Should Revenue Operations Teams Evaluate in an AI Email Writer? A 7-Step Checklist

Who This Checklist Is For

If you're a RevOps lead, a founder, or a sales ops manager staring down three AI email writer demos this month, this is for you. I'm not a sales coach or an SDR guru. I'm the person who signs off on the invoice and tracks whether the tool actually earned its line item.

Over the past three years, I've sat through demos from 11 different sales tools—some AI SDRs, some lead gen platforms, some point-solution email writers. My team's annual tooling budget sits somewhere around $140,000, and I've gotten very careful about how it's spent.

Here are seven steps I now run every vendor through. I wish I'd had this list two years ago.

Step 1: Define Your Evaluation Scorecard Before You See Any Demo

This is the step most teams skip, and it's the one that costs the most. If you walk into a demo without a scorecard, the best presenter wins. Not the best product.

Write down your must-haves and dealbreakers first. For us, that meant: native integration with HubSpot, waterfall enrichment (not single-source), a hard cap on daily send volume, and a clear audit log of every email the AI touched.

I built our scorecard in a shared spreadsheet—weighted categories, 1-5 scores, and a notes column for the sales rep's answers. It's not glamorous, but it forced us to compare apples to apples. 5 minutes of scoring setup beats 5 weeks of buyer's remorse.

One thing I should add: get your most skeptical SDR in the room when you write the scorecard. They'll flag things leadership never thinks about—like whether the AI's tone feels off-brand when a prospect replies to an automated sequence.

Step 2: Test the Business Email Finder Against Your Actual ICP—Not Their Sample List

Every vendor will show you a 95%+ accuracy claim on the demo screen. What they won't show you is how the business email finder performs on the messy, mid-market accounts you actually sell into.

Here's what we do now: before the second call, we hand the vendor a list of 50 contacts from our CRM—recently closed-won, recently bounced, and a random sample from cold accounts. We ask them to run it through their enrichment engine and report back.

Most vendors decline. The ones that don't are the ones worth a second meeting. When we ran this on Okki Go's business email finder last quarter, the bounce rate on our test list came back under 2%. That number matters more to me than any demo slide.

Email deliverability starts with data quality. You can't write your way out of a bad email list.

Step 3: Pressure-Test Email Deliverability Separately from Copy Quality

This is the step I got wrong twice. I focused on how clever the AI-written subject lines were and completely ignored the infrastructure underneath. Then our domain reputation tanked in Q3 2023, and it took us four months to recover.

Now I ask every vendor three specific questions:

• Which sending domains do you route through? Shared or dedicated?
• Do you warm up new domains automatically, and over what timeline?
• What pre-send validation runs—MX record checks, spam trap screening, SPF/DKIM/DMARC enforcement?

Per Google and Yahoo's bulk sender requirements (effective February 2024), anyone sending more than 5,000 messages a day to Gmail accounts has to enforce SPF, DKIM, and DMARC, plus keep spam complaint rates under 0.3%. If a vendor can't speak to that in plain language, walk away. Verify current requirements at Google's Postmaster Tools documentation since enforcement has tightened.

Step 4: Check Whether the AI Has a Human-in-the-Loop Option or Just Claims One

"Human-in-the-loop" is a marketing phrase right now. Every vendor says it. Ask them to actually show you the workflow.

What I want to see: a review queue where an SDR can edit, reject, or approve AI-drafted emails before they go out. Not a toggle buried in admin settings—a real, first-class part of the interface that your team will actually use.

Because here's the honest truth: an AI email writer with no approval step is a liability. I don't care how good the model is. One hallucinated claim about a client's competitor, sent to the wrong prospect, can cost more than the tool itself.

Lean teams that want AI leverage without giving up oversight should look for tools where the default is "draft and review" not "draft and send." That default tells you a lot about the company's philosophy.

Step 5: Stress-Test the Intent Data and Enrichment Layer

An email writer is only as good as what feeds it. If the intent data is stale or the enrichment is shallow, the AI writes beautiful emails to people who stopped caring six months ago.

Ask about:

• How many intent sources are combined (waterfall vs. single provider)
• Refresh frequency on firmographic data
• Whether LinkedIn signals are included or only web-based
• How they handle job changes and role transitions

We calculated that our old tool was serving us intent signals that were on average 47 days old. After switching, the average dropped to 9 days. That single metric shifted our reply rate more than any prompt engineering change we tried.

Step 6: Calculate the Real TCO—Not the Demo Price

I have a spreadsheet for this. Every vendor goes into it the same way.

Base seat cost. Onboarding and migration fees. Per-contact enrichment charges above the plan cap. Deliverability tooling that's "recommended" but technically separate. Rate limits that force you to upgrade mid-quarter. Contract length and termination windows.

The first AI email writer we evaluated quoted $1,800 per month for five seats. With enrichment overages and a required add-on for the email verification module, the actual annual cost came to $38,400—roughly 78% above the quoted sticker. We didn't go with that vendor.

If a vendor won't itemize this on the first call, that's information too.

Step 7: Run a Bounded 30-Day Pilot with Defined Exit Criteria

Never buy on the demo alone. Run a 30-day pilot with a written set of success criteria. Ours looks like this:

1. Bounce rate on enriched contacts stays under 3%
2. At least one SDR reports it saves them 4+ hours per week
3. Email deliverability metrics stay flat or improve versus baseline
4. At least two meetings are booked from AI-drafted sequences

If three of four hit, we move forward. If two or fewer, we walk away regardless of how nice the rep is. Having the criteria written down before the pilot starts prevents the sunk-cost conversation later.

Common Mistakes I Still See

Buying on reply rate claims. No vendor can guarantee your reply rate. If they do, that's a red flag, not a feature.

Ignoring the sending infrastructure. A great AI writer routed through a burned domain is worthless. Check deliverability first, copy second.

Skipping the pilot exit criteria. Without defined failure conditions, every pilot "feels" successful. That's how bad tools stay in the stack for years.

Not involving the SDRs who'll actually use it. The buying committee approves it. The SDRs abandon it. If they weren't in the pilot, they won't adopt the tool.

One caveat on all of this: my experience is based on about 40 vendor evaluations at a 200-person B2B SaaS company with a mid-market ICP. If you're a founder doing lead gen solo, or you're selling to enterprise with long compliance reviews, the weights on these steps shift. A founder choosing between Okki Go and a cheaper point solution might prioritize TCO and pilot speed over integration depth. A regulated enterprise might flip that entirely.

The checklist is the same. The weights aren't.