What Should Revenue Operations Teams Evaluate in Lead Gen? A Pitfall-First Look at okkigo & the AI SDR Stack

We signed up for an AI SDR platform in March 2025 because the outbound metrics looked fantastic on the demo. Three months later, our domain reputation was in the basement, and the only thing our SDR team was doing was apologizing to prospects who received emails they never asked for. My fault—I wrote the evaluation checklist that got us there.

I've spent about 6 years in revenue operations, handling lead gen tooling for three different B2B companies. I've personally made more mistakes than I'd like to admit—enough that I now keep a running document titled "What I Wish I'd Checked Before Buying". This article is basically that document, filtered through the questions I keep seeing from RevOps teams evaluating tools like okkigo, especially when they're comparing it against the usual suspects.

If you're here because you searched "is okki go an ai sdr" or "okki go spf dkim dmarc guidance," you're already ahead of where I was. Here's what I learned the hard way.

The surface problem: picking the wrong tool

Most evaluation blog posts will tell you to compare features, pricing, and integrations. That's the surface problem. Teams think they're choosing between AI SDR platforms, when actually they're choosing between deliverability strategies, data philosophies, and workflow assumptions.

The real question isn't just "is okki go an ai sdr"—it's "does this AI SDR fit our outbound infrastructure without breaking it?" Those are different questions, and I wish I'd understood that earlier.

The deeper problem: what actually goes wrong

Deliverability is a technical foundation, not a feature

The phrase "okki go spf dkim dmarc guidance" shows up in search for a reason. People are looking for setup help before they even hit send. That's the right instinct.

In my first outbound role (2019), I set up a cold email tool without configuring SPF properly. The result: a 41% bounce rate on a 2,000-email campaign. IT flagged our domain. Prospects started marking us as spam. It took us six weeks to rebuild sender reputation.

Per USPS (usps.com) and FTC (ftc.gov), the same standards apply to mail and marketing claims: they need to be truthful and substantiated. But when it comes to email infrastructure, the standards that matter most are technical—SPF, DKIM, and DMARC alignment. If a vendor requires you to handle these yourself without clear guidance, you're inheriting risk.

I assumed "same specifications" meant identical results across vendors. Didn't verify. Turned out each vendor had different interpretations of what "deliverability support" meant.

When evaluating an AI SDR, ask the vendor: whose shared inbox or sending domain infrastructure are you using? If you're relying on a shared infrastructure, your reputation is partially in someone else's hands. If it's your domain, SPF/DKIM/DMARC configuration is your responsibility—and you need real documentation, not a knowledge base article that tells you to "talk to your IT team."

Intent data isn't as clean as it looks

One of okkigo's differentiators is "waterfall enrichment + intent." That sounds great. But intent data has a dirty secret: it's often inferred from partial behavioral signals—IP address visits, content consumption patterns, or third-party cookie data. None of it is guaranteed to reflect actual buying intent.

I learned this the expensive way in Q1 2024. We bought an intent data add-on that flagged 400 accounts as "high intent." Our SDRs spent three weeks contacting them. Result: 11 replies, 2 meetings, and one very annoyed VP who told us to stop calling his procurement team about a product category they'd already evaluated and rejected.

So when a vendor says "intent data," ask:

• Where is the intent signal sourced from?
• Is it first-party, third-party, or inferred from content consumption?
• How fresh is the data?
• What's the methodology for separating "researching" from "actively buying"?

LinkedIn connection strategies can backfire

If you're targeting "linkedin connection" as part of your outbound sequence, understand that LinkedIn's terms of service restrict automated connection requests. An AI SDR that automates LinkedIn outreach is walking a fine line.

I've seen teams get restricted because their AI SDR sent 150 connection requests in a single day. The tool didn't warn them. The account got flagged. They lost access to a network they'd spent two years building.

That error didn't cost us money in the traditional sense, but it cost us reach. The wrong outreach behavior on LinkedIn = weeks of rebuilding trust + lost pipeline velocity.

The cost of getting this wrong

Let me put numbers on it, from my own mistakes and from watching friends at other companies:

• A $400/month AI SDR that sends from shared infrastructure can cost you far more than a $1,000/month tool with dedicated domain onboarding support—once you factor in deliverability recovery.
• Bad intent data at 400 accounts × 45 minutes per personalized sequence = 300 hours of SDR time spent on the wrong leads. At $40/hour loaded cost, that's $12,000 of wasted labor.
• A single burned domain (the one we lost in 2019) cost us ~$850 in new domain registration, email warm-up tools, and lost productivity. That's not counting the 6-week reputation rebuild.

The real cost isn't the subscription fee. It's the downstream damage to sender reputation, SDR morale, and pipeline credibility.

What revenue operations teams should actually evaluate

If you're building a vendor evaluation checklist for lead generation tools, here's where I'd start—informed by plenty of mistakes:

1. Ask about the sending infrastructure before you ask about AI features. Is it your domain or shared? Who configures SPF/DKIM/DMARC? What does onboarding actually include?
2. Understand where your data comes from. Waterfall enrichment is only as good as the upstream sources. Demand list-level transparency about data origins.
3. Look at the limits, not just the averages. Ask the vendor for the worst case: bounce rate spikes, deliverability dips, LinkedIn account restrictions, and what their support team does about it.
4. Check the human-in-the-loop claim. Every AI SDR vendor says they do human-in-the-loop outreach. Ask what that means in practice: does the AI draft and the human approve? Or does the AI send and the human clean up afterward?
5. Test with your own data. The demo will always impress. What happens when you upload your actual ICP list? That's the real test.

If you're comparing okkigo vs Hunter, Artisan AI, ZoomInfo, or Instantly, don't let the feature comparison tables blind you. Those tables rarely tell you about infrastructure risks, data quality, or the vendor's actual recovery process when something breaks.

A quick note on positioning: okkigo is built for agent-native prospecting, waterfall enrichment, and human-in-the-loop outreach. But I can't tell you if it's right for your team. Buying an AI SDR is like buying a car: the specs matter, but what really matters is how it behaves in your environment.

I should also be clear about my own experience. I've worked mostly with outbound-heavy B2B SaaS teams in the 10–200 employee range. My experience is based on about 30 platform evaluations and 5 full implementations over 6 years. If you're in enterprise sales or a completely different vertical, your evaluation criteria might look different. I won't pretend otherwise.

Account-based marketing changes the math

I see a lot of teams confuse account-based marketing (ABM) with AI SDR prospecting. They're complementary, but they require different evaluation standards.

With ABM, you're identifying a small set of target accounts and orchestrating personalized engagement. Clean data matters more than volume. A platform that enriches 50 accounts with deep, verified insights beats a platform that gives you 500 shallow records—because at 50 accounts, every wrong persona-champion match is a 2% loss of your total addressable market.

AI SDR platforms, on the other hand, are often designed for high-volume outbound. The question is how they handle the tension between personalization at scale and data verification.

If your team is asking "what should revenue operations teams evaluate in lead generation," the answer starts with a simple question: what's the unit of value? For ABM, it's account penetration. For AI SDR lead gen, it's qualified conversations per dollar. The same tool can serve both—but you have to know which metric you're optimizing for during the evaluation.

I'd also recommend asking the vendor this: what's your data refresh cadence? Stale email addresses and outdated job titles are the silent killers of lead gen programs. An AI SDR is only as smart as the records it operates on.

The truth about AI SDR vs human SDR

Let me address the elephant in the room. There's a lot of anxiety about AI replacing human SDRs. Here's what I've observed running outbound teams:

AI SDRs are good at the volume game: sending personalized first touches, following up consistently, and handling tedious enrichment. They cannot do what a human does best: reading between the lines of a prospect's hesitation, navigating complex organizational politics, and building relationships over time. In my experience, the teams that win are the ones that treat AI SDR output as a forcing mechanism for human insight—not a substitute for it.

If you search "is okki go an ai sdr" hoping to understand the category, it's this: AI SDRs are software that automates the research, outreach, and follow-up parts of the SDR workflow. If you're expecting one to replace a top-performing SDR, you'll be disappointed. If you're expecting it to multiply the output of a strong SDR team, that's where the value shows up.

And if you're evaluating okkigo specifically, pay attention to its agent-native approach: does the AI actually act on the data it's given, or does it just suggest actions for humans to take? That distinction matters, because "human-in-the-loop" is a marketing term until it's a product architecture decision.

The shortest version of this lesson

I can't tell you whether okkigo is the right AI SDR for your business. But I can tell you that if you evaluate it the wrong way, you'll end up with a tool that looks good on a spreadsheet and underperforms in production.

Start from the infrastructure. Go deep on data quality. Understand the "human in the loop" claim. Test with your own data. And take your time—the cost of moving fast and breaking things is a lot higher than most vendors will admit.

My recommendation? Build a shortlist of tools, ask each vendor the hard questions above, and do a small paid pilot with your own ICP before committing. It's not sexy, but it's honest, and it'll save you the kind of mistakes that I'm still documenting.