CONTEST: What’s Your #MyGongSecret with AI Agents?

Here is the full list of Gong AI Agents:
https://help.gong.io/docs/understanding-ai-agents 

 ​@Gijs Bos! ​@Travis Chichester, ​@tooliodan, ​@John Machak, ​@Matt Gardner​​​@Leigh Oxley, ​@Rae Guimond, ​@Shahina, ​@Alexandra, ​@TLee McNabb, ​@Beatrice Dobre, ​@Rebecca, ​@Michelle Sanchez, ​@Bangi, ​@Kerry Heilskov, ​@Jason Dailey, ​@Chris Reiling, ​@jbrown2024, ​@Lenny Goh, and ​@Oliver Wood  

@Justin Benton, @Ian Gwynne, @Isabella Grandchamp @John Machak @Delila House @Viktorija @David Roi @Kayla Kuzer @Jacob Walker @SaaS Revenue Guy @Alex Heller @Ezz24  @Susan Kilcline @Jeff Smith @Matt Gardner @Christina Mowry @Laura Bailey and @Annie Jackson 

AI Tracker - Higher value sales

I use the AI Tracker to ensure my sales approach stays focused on value. At RX, we aim to sell through Value-Based Selling (VBS), and the AI tracker helps me stay aligned with that goal and provided me a way to review this.

After each call, I review the points of interest and the Smart Trackers, specifically looking for those connected to VBS. This helps me identify what resonated with the client and what I may have missed, so I know exactly what to build on in our next conversation.

II also review the Insights Initiatives page to see which elements I have or haven’t been including in my conversations over the past month. This helps define my focus for the month ahead. 

I’ve noticed that deals where these trackers appear often result in a better, more personalized fit for the client, and frequently lead to higher revenue as well. That’s because VBS conversations naturally open the door to more Value Added Services (VAS) that support the client’s objectives.

Overall, the AI Tracker helps me reflect on my VBS habits and identify where to adjust my focus for upcoming conversations and future opportunities.
 

​@Nisha Baxi This if my FAVORITE topic of conversation 🙌🏻

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AI Agent: AI Builder – Win/Loss Pattern Analysis (2025)

The Use Case:

As our team looked deeper into 2025 performance, we realized our CRM win/loss fields weren’t reliable enough to identify real patterns on why we win and why we lose. We wanted to understand what themes influenced wins vs. losses directly from buyer conversations.

To solve this, we used the Gong AI Builder to analyze closed won and closed lost deals and extract patterns directly from calls and emails.

The Process:

1. Define the dataset
We filtered Gong deals for new logo (i.e. our Sales Manager’s Team) Closed Won and Closed Lost opportunities (respectively) from 2025 to ensure the analysis was focused on relevant buyer conversations & behavior.

2. Build a custom AI prompt in Gong AI Builder
We created a prompt asking the AI to analyze all conversations associated with these deals and identify:

• Common themes influencing wins / losses

• Signals tied to buying momentum (budget, urgency, champion engagement, etc.), following our SPICED methodology 

• AE related actions that may have influenced the deal one way or another, even if not directly documented 

We specifically instructed the AI not to summarize each deal individually, but instead to focus on aggregate patterns across all deals. We also asked Gong to include quotes from calls/emails and examples that illustrate each identified pattern to make sure the analysis was grounded in real buyer language. 

3. Review the aggregated insights
The AI output gave us a structured summary of:

• Top source identifiers mentioned by buyers

• Recurring reasons for wins & recurring drivers behind losses (respectively) 

• Emerging signals related to budget, timing, and expansion potential

The Outcome:

This workflow allowed us to cross check incomplete CRM data with real buyer conversations and quickly identify patterns we would have otherwise missed. We mapped these against reported win/loss reasons and started using these insights to:

• Strengthen discovery and qualification frameworks

• Provide real examples of buyer language for sales training

• Inform marketing & demand generation strategy to pad ToF with ICP-specific prospects 

Instead of manually reviewing dozens of deals, Builder allowed us surface these patterns in minutes.
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What’s Next?

We’ll start doing this analysis at the end of each quarter, and split per product line to help us identify trends for specific products and at different time points in the year. Instead of using this as a lagging indicator (e.g. all of 2026 data), we’ll use it real-time to make changes in our enablement & training strategies for our team.

Excited to see how other teams are using this!