Brian LaManna demos Gong's AI Data Extractor, which automatically pulls structured CRM data from calls, meetings, and emails and writes it directly to your fields. One customer used it to extract competitor mentions and confirm metrics, which fixed their stage progression and made forecasting reliable again.
Chapters
I'm incredibly excited to share what might be gong's most game changing AI agent to date. AI Data Extractor. This will fundamentally transform how revenue teams operate. Now reps imagine never filling out a CRM field again. You heard that right Leaders Rev Ops imagine never chasing down reps, asking them to update the CRM again with their subjective inputs. Now imagine at scale running your revenue or on actual customer conversations instead of just what reps remember to log or decide to tell you. That is exactly what our world class product team built over here at gong. And AI Data Extractor now captures every critical insight from your calls, web meetings, emails and populates your CRM with zero manual work, clean data, accurate forecasts, no rep bias. And this is the future of revenue operations. So what I want to show you is one of the best early customer stories and how they set this all within minutes in GONG through our agent studio. So GONG customers, you know where you have access to this today? In the bottom left in your admin center you will go over to the Agent studio tab and and within that you'll have access to AI data extractor down below. Now what happened was a RevOps team noticed that deals were slipping late in the quarter. When they look closer they realized reps weren't consistently filling in competitor fields in CRM. And that meant leaders were getting blindsided late stage by competitive pressures across the pipeline. When in fact it was being brought up and discussed earlier in conversations. It just wasn't being brought to their attention. So the solve was using gong's AI data extractor. You can add any new field here, I'll show you this competitors field. It's a very simple. You write a prompt in here which is the question you want GONG to look for across the data set. You can provide additional context, instructions or real examples. You decide if you want this to look across deals or accounts. And then you can also choose the type of CRM field if you want an open text, a yes, no or even a pick list. Now what you can do when you create one of these AI fields is you can actually test it. And that's exactly what this Rev Ops team did is they wanted to say hey, we want to make sure we run this the right way before we publish it across the entire go to market team so they can look at it across certain opportunities. In this case Bitforge and a recent $500,000 opportunity where they want to understand hey, we're competitors. Mentioned across this deal you can see the output looks correct. It's transforming that unstructured data into an actual text field as Alchemax was mentioned as a competitor across bitforge, but this team wanted to take it a step further. They created three more AI fields that detect whether success metrics a Champion and business objectives were truly confirmed. They mapped each one of these to a checkbox within CRM and then added a simple workflow. If all three are true, automatically move the deal for a wrap from qualification stage to discovery. The result was their pipeline finally reflected the reality. Stage progression became consistent, competitive dashboards became trustworthy and leadership didn't have to rely on memory anymore. I'll show you what this looks like within CRM. So within the same bid forge opportunity you can see the Gong AI shield wrote back was mentioned as a competitor and then these three checkboxes were all checked. Since metrics were confirmed, business objectives were confirmed. Champion was also confirmed. Now as leaders they operate out of GONG to run their pipeline reviews and their forecast within our deal pipeline inspection. So within GONG as well, Jessica Lee and her team is able to look across these opportunities and understand hey, for my commit deals in quarter, where are these not true? As an example with Fundex, Gong doesn't believe yet we have a champion confirmed now she knows exactly where to pressure test in within her most important commit deals for the quarter to close it out with Gong's AI data extractor. Reps love it because it removes manual work ops loves it because the data is finally clean. That's what AI extractor does. It takes what's said in conversations, it turns it into structured data and it keeps your CRM accurate without reps having to lift a single finger. If you want to explore different use cases like identifying renewal risks or tagging expansion signals, just let us know in the comments. There's tons you can do with it.
