Okki Go Human Review Workflow: A RevOps Checklist Before You Turn On Your AI SDR

I've been running RevOps for a B2B SaaS company for five years. That means I'm the person who picks, connects, and sometimes apologizes for the tools our SDRs use. I've personally made and documented five significant mistakes in that time, totaling roughly $42,000 in wasted budget. When we started our Okki Go rollout in January 2026, I wrote a checklist before I let anyone touch the CRM. This is that checklist.

Here's the uncomfortable truth from those mistakes: the AI model isn't the risky part of an AI SDR rollout. The risky part is everything around it—the human review workflow, the API connection, what we mean by 'enriched,' and how the tool writes back to the CRM. When I first started with AI SDR tools in 2021, I assumed the model was the product. Three failed rollouts later, I realized the model doesn't create bad leads. It just scales them faster.

If you're a RevOps lead, an SDR manager, or an outbound agency owner about to turn on Okki Go, this checklist is for you. It assumes you're not expecting AI to replace your SDRs. You're expecting it to handle repeatable prospecting work while people handle judgment calls. Six checks, in order. The first two are the ones everyone plans to do. The middle ones are where our team kept getting burned.

Step 1: Put the Okki Go human review workflow at two gates, not one

Before we had Okki Go, we used an AI SDR tool where 'human review' was my job and only when I remembered, which was never consistent. Imagine 400 AI-drafted emails waiting every morning. Reading them all is a second full-time job nobody wants, so after two weeks you turn review off and accept the risk. That's the wrong way to design a human review workflow.

Here's what we changed with Okki Go. A human review workflow should route decisions to a person at the point where the cost of a wrong decision is highest. For us, that's two gates:

Gate 1: before a contact enters an active sequence. We don't want an SDR reading every draft of an outreach email from scratch. We want them looking at a queue of proposed contacts and answering simple questions: Is this person real? Does this domain belong to the account we're targeting? Do we have 'do not contact' notes on them from previous campaigns? Approve, edit, or reject, with a reason attached.

Gate 2: before a positive reply becomes 'meeting booked.' This is the gate most teams skip, and it's the one that saves you from the worst kinds of embarrassment. An AI SDR can recognize a reply that sounds positive. But it can't know that this particular prospect is a current customer who just emailed our CEO, or that the 'great fit' company is one we politely asked to stop contacting us in November. A human reads the conversation and makes the call.

In our first week, Gate 1 flagged 34 proposed contacts that looked fine on paper. Eleven had do-not-contact records. Two belonged to a customer company under a different domain. Gate 2 stopped a meeting request from someone whose company was already on our 'we are not a fit' list. That's what the Okki Go human review workflow is for. It keeps the AI from acting on data the AI can't see.

Step 2: Test the Okki Go API integration like it will break (because it will)

The Okki Go API integration sounds like a 15-minute setup: connect, map fields, done. Treat it like a two-day project. Every CRM has custom fields, validation rules, and old data. The API will happily show you what it can sync; it won't tell you that your Salesforce org has two fields named 'Company' and one of them is a legacy text field nobody uses.

Before we connected Okki Go to production, we rebuilt our test steps:

1. Create a sandbox that mirrors your production fields.
2. Add 20 test contacts with edge cases: the same email in lowercase and uppercase, a contact with a 'do not contact' note, a domain that changed when the company rebranded.
3. Map one field at a time. Check whether the integration overwrites populated values with blanks.
4. Define the deduplication key before you sync anything. Email alone isn't enough if your CRM stores contacts under multiple email fields.
5. Run at least 100 records through, then inspect the result in the CRM, not in the API logs.

I learned this the hard way. In December 2023, I connected an AI prospecting tool without checking deduplication. Three weeks later, we had 167 duplicate leads. Not because the tool was stupid. Because I didn't tell the API how to identify an existing record. The Okki Go API integration has the same failure mode if you skip the mapping work. Don't trust the field labels in the dropdown. Verify against your real data.

Step 3: Define CRM enrichment as a waterfall, not a single lookup

The phrase 'enriched' caused our most expensive miscommunication. I said the list was enriched. The SDR team heard 'ready to send.' Those were not the same thing. We discovered the gap in September 2024, when 26% of a supposedly enriched list bounced. We had added titles and company sizes to contacts, but the emails themselves had never been verified.

Okki Go describes its approach as waterfall enrichment + intent, and I like that framing because it forces you to think in layers:

• First, match the account and the company domain. No domain, no send.
• Then find and verify a person-level email. A guess or a pattern is not verified.
• Only after the email checks out do intent signals matter.
• If the waterfall can't verify an email, the contact stays marked 'unverified' instead of quietly becoming 'ready to send.'

A couple of things to check before you turn on CRM enrichment:

1. Decide which fields the enrichment can update. I don't let any tool overwrite a phone number or a LinkedIn URL with a blank.
2. Keep three clear states in the CRM: verified, enriched but not verified, and raw.
3. After the first sync, sample 100 records and compare them to what your SDRs already know. In our first Okki Go sync, this caught a field mapping issue in 12 records before anyone sent anything.

No tool we've tested gets every record right. If a vendor promises 100% accuracy in CRM enrichment, that's a red flag. Plan for errors, catch them in review, and your data gets better over time.

Step 4: Treat the LinkedIn email finder as a discovery tool, not a quality check

Okki Go's LinkedIn email finder is great at one thing: finding an email address from a LinkedIn profile. That's it. It doesn't tell you whether the address is active, whether the person left the company last month, or whether the mailbox is monitored. Those are separate questions.

This step is short because the rule is simple. Use the LinkedIn email finder to source addresses, then run those addresses through email verification before they enter a sequence. If a contact was found only through LinkedIn and no verified email exists, don't let the AI SDR send to them. Leave them in the queue for a human to work later.

The compliance side matters here too. I'm not a lawyer, but per the FTC's CAN-SPAM guidance (ftc.gov), commercial email has to include a truthful header, an accurate subject line, a valid physical postal address, and a working opt-out that is honored within 10 business days. That applies to AI-assisted outreach as much as manual email. We configured our sequences to remove anyone who replies with 'unsubscribe' immediately.

The LinkedIn email finder is a multiplier. It finds addresses for the top of your funnel. It can't replace verification, and it definitely can't replace a human who checks whether the person is actually in your ICP.

Step 5: What should revenue operations teams evaluate in visitor tracking?

If you've ever sat through a visitor tracking demo, you know the dashboard is kinda hypnotic. Logos of companies that visited your site, pages they viewed, the works. Five years ago, that was enough to justify the purchase. In 2026, 'who visited' is table stakes. The question revenue operations teams should be evaluating is what actually happens after the visit is detected.

Here's the evaluation checklist I use now:

• Match rate: Out of 100 tracked visitors, how many can the tool match to a real account in your CRM? If 70 of those are small companies outside your ICP, the alert is noise, not pipeline.
• Contact-level identity: Account visits are interesting. A known person from the account is actionable. Does the tool resolve to a person, and is that person in your target list?
• CRM behavior: What does the tool create in your CRM? If it creates a lead for every anonymous visitor, it will pollute your database in six weeks. I want account-level notes and tasks, not phantom person records.
• Timing: Is the visit signal delivered in near real time, or does the report update tomorrow? By tomorrow, the SDR's day has moved on.
• Privacy and data source: Ask what data the tracker uses, how long it retains it, and what legal basis it claims for processing. Run that past your counsel, not the vendor's sales engineer. This is especially true if you're sending visitor data from the EU into your CRM.

The most ignored criterion is the one about CRM behavior. We paid for visitor tracking in 2023 and the alerts were impressive for about a month. Then we realized most of the 'visited your pricing page' accounts were students, competitors, and companies with 10 employees. The tool had also created hundreds of leads from anonymous traffic, so our lead hygiene got worse. If you're setting up Okki Go with intent data, don't bolt on visitor tracking that undermines the clean data you just built.

Step 6: Build a 30-day review loop before you launch

No workflow survives contact with reality unless someone reviews it. The Okki Go human review workflow shouldn't be a one-time configuration. It's a feedback loop.

In the first 30 days, we schedule:

• Week 1: a daily 15-minute queue review with the SDRs. Talk through every rejected contact and the reason code. If SDRs keep rejecting the same kind of record, update the criteria before it reaches them.
• Weeks 2 through 4: a weekly data review. We look at bounce rates by email source, how many contacts got flagged for do-not-contact, and whether Gate 2 caught anything.
• Day 30: decide which changes become permanent. This is when we tune the sequences based on actual conversations, not dashboard numbers.

This is also where our team documented its best catch. In week three, the queue flagged a contact at a huge account. The SDR almost approved it. But the contact's company domain was a subsidiary that our product doesn't support in their region. That's not a data quality failure. That's the human review workflow working.

Mistakes I hope you don't repeat

If you take nothing else from this, take these three warnings.

Don't turn off the human review gates after the first month. That's when the novelty wears off and the bad data starts to feel normal. Keep the gates, or re-evaluate them deliberately—don't just disable them because a sequence seems to be performing well.

Don't save money by disabling verification. We saved about $250 in one month by turning off email verification on a tool. The resulting 26% bounce rate cost us roughly $3,200 in wasted spend plus three weeks of sender reputation repair. That was the definition of penny wise and pound foolish.

Don't let 'enriched' and 'ready to send' mean the same thing. They don't. Define both words before the first sync. Your SDRs will thank you.

A final limitation: I've only run this with a 12-person SDR team, English-speaking outbound, mid-market B2B. If you're rolling out Okki Go at a 100-person outbound agency or across five languages, the volume and the review gates will look different. This reflects what I've learned as of April 2026, and the tool landscape changes quickly. Verify the current setup before you copy it.

Since January, this checklist has caught 41 potential errors before they reached a prospect's inbox—wrong contacts, do-not-contact records, old domains, one meeting request from a company we'd asked to stop emailing. That's why I still maintain it. The AI isn't the risky part. The messy data and the missing workflow around the AI are. Fix those first.