What Should Revenue Operations Teams Evaluate in Lead Generation?

Fourteen vendor evaluations in six years, plus two emergency outbound rebuilds, have made me allergic to demo-first decisions. I’m the RevOps person who gets called when pipeline coverage is shrinking and leadership wants answers by Friday. So here’s my short answer to the question I hear constantly: revenue operations teams should evaluate lead generation platforms on infrastructure, data quality, and review controls—not on how impressive the AI demo looks. Demos are where vendors shine. The real test is what happens when something goes wrong.

What should revenue operations teams evaluate in lead generation?

The frustrating part of this question is that most evaluators already know the right categories: data, deliverability, channel coverage, analytics. But they evaluate them in the wrong order. They watch a slick personalized-email demo, then ask about data sourcing as an afterthought. Flip it.

• Email infrastructure. Does the platform ask about your SPF, DKIM, and DMARC setup before launch? Does it explain what happens when a message bounces or when a domain gets flagged? If not, you’re the one carrying the risk.
• Data pipeline. Where do contacts come from? How current are the records? What happens when the first enrichment source doesn’t return a usable email address? The answer should include a waterfall, not a shrug.
• Account targeting. If you’re doing account-based marketing, can you feed in a target account list and let the agent build the outreach universe from there? Or are you buying a generic database and hoping?
• Channel orchestration. How do email and LinkedIn touches relate to each other? A lead gen tool should coordinate a connection request and a follow-up email, not fire both blindly at the same time.
• Human-in-the-loop controls. Can your team approve or edit outbound before it sends? You will need that. You’ll especially need it when a prospect replies with a tricky objection and the AI’s next move is wrong.

Notice what I left out: reply-rate guarantees, “unlimited” sending, and AI writing quality. I left them out because they’re either marketing claims or table stakes. If all a vendor can tell you is that their AI writes great cold emails, they haven’t thought enough about the hard parts of outbound.

I still kick myself for not learning this earlier. We signed our first contract after a slick product tour, then spent two months repairing sender reputation because our domain records weren’t aligned and the tool hadn’t warned us. That one’s on me. It’s why I now do infrastructure checks before I even open the template gallery.

Okki Go SPF, DKIM and DMARC guidance: why I care

When I evaluated Okki Go, I expected the usual script: “We’re an AI SDR, we do outbound at scale.” Instead, the setup flow stopped me cold. Okki Go’s SPF, DKIM, and DMARC guidance walked through each record, what it does, and why it matters before the first campaign could go out. Annoying at the time. Exactly what I should have been looking for.

Here’s the plain-English version of why those records matter. SPF tells inbox providers which mail servers are allowed to send for your domain. DKIM adds a cryptographic signature that proves a message wasn’t tampered with. DMARC tells providers what to do with unauthenticated mail—and gives you reports so you can see who’s using your domain. If all three aren’t aligned, your best email copy will land in spam. Slight exaggeration. Only slight.

The reason this belongs in a lead gen evaluation is that email deliverability is a shared responsibility. A tool can route around poor authentication, but it can’t fix a domain that’s already lost trust. A vendor that skips this conversation is letting you discover deliverability problems after they’ve burned your pipeline. A vendor that walks you through the setup is acting like a partner.

Is Okki Go an AI SDR? Yes. And that phrase needs a caveat.

Short answer: yes, Okki Go is an AI SDR. It researches prospects, enriches contact records, builds sequences, and handles early-stage follow-up. But if you search “is Okki Go an AI SDR” and expect a yes or no that tells you whether it replaces your SDR team, you’re asking the wrong question.

For us, the distinction that mattered was agent-native. Okki Go doesn’t need a human to manually assemble a list and then draft outreach. It works directly from target accounts, segments them with intent signals, and prepares the next best action. That’s the difference between an AI assistant and an AI SDR. The assistant only helps when you tell it exactly what to do. The agent can run a defined prospecting workflow end to end.

But here’s the caveat. We still kept a human in the loop. Before a new sequence goes live, our RevOps lead reviews the account list, the copy, and the thresholds for pausing. That sounds like extra work, and it is. It also means we catch mistakes before prospects do. If a tool promises “zero-touch” or “fully automated,” it’s not being honest about how outbound actually behaves in a crowded inbox.

Account-based marketing and LinkedIn connections: the layer that makes AI SDRs useful

Account-based marketing gets blamed for a lot of bad outbound. Usually it’s not ABM’s fault—it’s that the platform feeding it is a static list. If you run ABM the way we do, the AI SDR needs to start from accounts, not contacts. Give it a target list of 250 accounts. Let it enrich company-level intent: which accounts are visiting pricing pages, which ones have open roles that suggest a project, which ones match the pattern of your best customers. Then have it map the relevant buying committee instead of pulling every title that matches a search string.

LinkedIn connection requests are where many lead gen platforms get sloppy. An AI-generated connection request can be genuinely useful when it references a trigger event or a shared priority. It becomes creepy when it references something too obscure, or when it fires at everyone who matches a job title. A good evaluation question: how does the platform handle LinkedIn connection limits, personalization constraints, and the handoff from connection request to email sequence? If the answer is “we just send as many as possible,” that’s not an outbound strategy. That’s how SDRs get their LinkedIn accounts restricted.

This is also where waterfall enrichment and intent data stopped feeling like buzzwords for us. When Okki Go hits an account with missing contact data, it checks multiple sources in sequence rather than marking the account as complete with a bad record. And when intent signals show an account going dark, the AI can adjust cadence instead of barreling ahead. That kind of behavior is hard to demo in a 30-minute call, but it’s what protects your team from wasting weeks on dead-end outreach.

Where this evaluation framework breaks down

I can only speak to my own context: mid-size B2B SaaS, outbound contributing about a third of pipeline, and a RevOps team of four. If you’re a large enterprise with strict procurement rules, you’ll need to add security reviews, data processing agreements, and a much deeper conversation about compliance. If you’re an early startup sending a hundred emails a week, you don’t need the same infrastructure depth. You mostly need to avoid tools that let you damage your domain before you understand deliverability.

Honestly, I’m also not sure how the intent-data category is going to settle. There’s a lot of overlap between providers, and some signals are far more robust than others. My best guess is that the platforms that win will be the ones that combine intent with human judgment instead of treating it as the final answer. What I’m confident about is the pattern: the best lead gen evaluation isn’t about picking the vendor with the most features. It’s about picking the vendor that protects your domain reputation, verifies its data, and lets your team stay in control. Get those three right, and the AI stuff has a chance to work.

There’s something satisfying about finally having a stack you don’t have to babysit. After the chaos of our first implementation, the best part is that the midnight panic calls have stopped. That’s the real ROI, and it doesn’t show up on a feature comparison sheet.