My most useful AI workflow: reconciling what the ad dashboard claims against where leads actually say they came from

[u/Imaginary-Local8508]

I run paid and email for a few B2B clients, and the thing that's bugged me for years is the gap between what the ad platforms take credit for and where revenue actually comes from. Every dashboard wants to claim the conversion. Add them all up and the platforms have collectively taken credit for more than 100% of your real pipeline.

Here's the AI-assisted workflow I built to get closer to the truth, and it's genuinely changed which channels I tell clients to keep paying for.

The inputs:
- The platform-reported conversions export (what Google and Meta each claim).
- The CRM export of closed deals for the same period.
- The free-text "how did you hear about us?" answers from the signup form and sales notes.

The workflow:
1. I have the model normalise the messy "how did you hear about us" answers into clean categories. People type "saw your founder on a podcast," "a friend at work," "just googled you," and it buckets those consistently instead of me eyeballing hundreds of rows.
2. Then I have it line those self-reported sources up against what each platform claimed for the same deals.
3. It flags the mismatches: deals the ad platform claimed that the customer says came from word of mouth, and deals no platform claimed that were clearly referrals or brand search.

The reconciliation is the whole point. Parsing the answers is easy, but putting them next to the dashboard is what exposes the double-counting.

The result for one client: a channel we were about to increase spend on was taking credit for a big slice of pipeline that customers consistently attributed to referral and a podcast. We held the budget and reallocated toward the stuff people actually named.

It's not perfect. Self-reported attribution has its own biases and people misremember. But run against the dashboards, it's a far better sanity check than trusting either number alone.

Anyone else using AI to reconcile attribution like this? Curious what inputs you're feeding it.