Thank you to everyone who joined today's AI Builder Meetup!
30+ Visioneers. Three brave volunteers to kick us off. A chat full of real prompts and use cases. And we got to do it all on the same day Gong launched Mission Big Dipper. Perfect timing!
As promised, I documented everything so you do not have to dig through your notes. Here it is, all in one place.
Huge thanks to our three kickoff volunteers: Jaime Council at Sharecare, Justin Manni at Averhealth, and Sydney Tress at Wolters Kluwer, our recent Gong AI Agents contest winner! And a special shout out to Itai Sagi and Katie Geoffroy, who both dropped full prompts into the chat. Those are published below exactly as they shared them.
Everything is organized into three buckets:
- AI Builder Workflows — what people built in AI Builder
- Enablement Ideas — driving adoption, coaching, and ramp with Gong
- Prompts and Other Workflows — copy and paste ready prompts plus everything else
BUCKET 1: AI BUILDER WORKFLOWS
Workflows people built directly in AI Builder
WORKFLOW 1: Top-performing call feedback loop for sales playbooks
Shared by: Jaime Council, Sharecare (Director of Business Enablement)
Gong feature: AI Builder + Scorecards + Streams
What it does: Turns your highest-rated customer conversations into role-specific playbooks automatically, so reps ramp on the best calls instead of starting from scratch.
Step by step:
- Build out scorecards that define what good looks like across discovery and demos
- Create streams off those scorecards to surface the right calls
- Use AI Builder to focus on calls that align with those scorecards and earned a rating of 4 stars and above
- Add filters for specific call participants so you can narrow to a team or a person
- Pull the suggested framework AI Builder generates from your real conversations, including how reps overcome objections and quotes to pull from
- Build role-specific playbooks from it, for account management focused on upsell and retention, or go to market focused on net new logos
- Have reps use the guidebook to develop and practice their own pitch, then get certified to speak on customer calls
The outcome: Ramp time dropped from the historical 3 to 6 months down to inside four weeks. One example: a CSM moving into a sales role used it to shift from operational, front-line talk tracks to executive-level pain points, with AI Builder pulling the framework straight from real customer conversations.
Tip: Treat scorecards as iterative, not set in stone. Partner with managers to agree on the framework, then tweak the prompts that are not driving good feedback as more calls get captured.
WORKFLOW 2: Public sector forecast accuracy scorer
Shared by: Justin Manni, Averhealth (Chief Revenue Officer)
Gong feature: AI Builder + MEDDIC
What it does: Scores deals against the real buyer behaviors of your segment so forecasts reflect how the deal will actually close, not just how enthusiastic the prospect sounds.
Step by step:
- Start from your sales methodology. Justin's team runs MEDDIC.
- In AI Builder, layer in the buyer behaviors specific to your segment. For Averhealth that is public sector: budget cycles, appropriation timing, procurement requirements.
- Build a scorecard of about 12 questions the rep and their manager work through, like: has the rep identified the procurement pathway, which pathway applies to this deal, is budget confirmed and appropriated
- Let AI Builder apply MEDDIC principles to help complete the picture
- Use the scored output to forecast more accurately and coach reps on what they are missing
The outcome: A more honest forecast. Reps were marking government deals as ready to buy without accounting for gov timelines. Now both reps and leaders get a clearer read on whether a deal really closes this quarter. Justin built the first version in about 10 minutes.
Tip: Justin also built AI Builders for early-stage discovery, a linear path that gets prospects to share their story, and for objection handling. Reps are getting a lot of use out of both.
WORKFLOW 3: Win/loss and product launch analysis
Shared by: Sydney Tress, Wolters Kluwer
Gong feature: AI Builder + Smart Trackers + Call Streams + AI Assistant
What it does: Turns product launches and field conversations into a win/loss analysis you can hand straight to product and marketing leaders, grounded in real quotes.
Step by step:
- Create a Smart Tracker for the product name
- Set up call streams tied to that tracker so product owners and managers get notified when it comes up, with an email pointing to exactly where in the call
- Drop into flagged conversations and use the AI Assistant to dig deeper
- On a 30/60/90 cadence after a launch, share the insights with product and marketing leaders
- For the quarterly win/loss, use AI Builder to generate an executive summary, overall market segment sentiment, customer feedback (what customers liked, common objections and concerns, feature requests and gaps), competitive insights on how you compare in market, and pipeline indicators showing where deals are stalling and why
- Let AI Builder pull hyperlinks to the actual conversations and real customer quotes into the report
- Close with next steps product can action, like evolving the product, driving training, or adding resources
The outcome: Product and marketing leaders get field reality on a regular cadence, backed by real calls and real quotes. This is the workflow that won our AI Agents contest, and several attendees asked to build it alongside Sydney.
Tip: Sydney also runs a low scorecard call stream so managers get a weekly notification on calls that scored low against the methodology. A great built-in coaching signal.
WORKFLOW 4: Win-back campaign from lost deals
Shared by: Adam Bathe, Qualifax (RevOps Systems Admin and AI Specialist)
Gong feature: AI Builder
What it does: Finds every deal you lost for one specific reason, then hands you the re-engagement talk track to win them back.
Step by step:
- Pick the specific reason or missing capability you want to investigate. For Adam it was a service they did not offer until a recent acquisition.
- In AI Builder, search across the last year of calls for deals that were lost, dropped, or stalled because of that specific item
- Let AI Builder pinpoint every one of those calls and conversations
- Ask it to generate the best re-engagement talk tracks for reaching back out, tuned to be exciting and relevant
- Run a campaign to contact every one of those prospects and let them know the gap is now closed
The outcome: A ready-made win-back list with messaging, built entirely from deals you already lost. Adam's team turned a new acquisition into an instant re-engagement campaign.
Tip: This works any time something changes on your side. New feature, new integration, new pricing. Go find the deals that stalled on the old limitation.
BUCKET 2: ENABLEMENT IDEAS
Driving adoption, coaching, and ramp with Gong
New Enable features to try:
- AI Builder for Scorecards. Generate coaching criteria straight from your top-performing calls in minutes. Pairs perfectly with Jaime's feedback loop in Bucket 1.
- Dry Run. Have reps practice an upcoming meeting against real account context before they ever get on the call.
- AI Coach. Personalized guidance after every AI Trainer session, so coaching scales without adding hours to your week.
Adoption and change management
This was one of the best threads of the day. How do you actually get reps to use Gong? Three takes from the room:
- Mandate it, then build a rhythm. Justin (Averhealth) mandated Gong for his team of high quota carriers and runs a forecast call every Friday where RevOps joins to hold everyone accountable. Once the cadence is set, reps feel ownership and it stops feeling forced.
- Lead from the top and the bottom. Jaime (Sharecare) layered Gong into the team operating rhythm with leadership reinforcing it from the highest levels, then worked bottom up, reaching out to the biggest skeptics to show what is in it for them. One rep who kept booting the bot came back around after losing the proof of a customer commitment.
- Name a Gong champion per team. Itai (Palo Alto Networks) gave one champion per sales and CSM team early access to learn the system and win over their peers. Every enablement session answered one question: what is in it for me. He added light gamification too, like a Slack channel and points or a small bonus for the most recorded calls.
Scorecard build tips
- Do not ship the first AI draft. Jaime found AI-built scorecards were too easy at first, everyone scored five stars. Start from the AI Builder framework, then modify it with what you know about your business. AI plus human is the winner.
- Use ranges, not yes/no. Vincent Gerard switched scorecards from yes/no to a 0 to 3 range. Better results, and you can watch a rep climb from a 1 to a 3 over time.
AI Builder vs Agents: when to use which
Susana Klotz asked a great question: when do you reach for AI Builder vs build an agent? Two takes worth saving:
- Sydney: think of AI Builder as the roadmap, the analysis, the job aid, the scorecard output. Something you sit down and read. Agents are for action, like a deal inspection agent, an account planning agent, or the call follow-up composer that drafts your emails.
- Katie: it is a two-pronged approach. Use an agent to identify (her raving fans agent surfaces advocates), then use AI Builder to create the asset (quote cards, customer stories) once you have found the signal.
BUCKET 3: PROMPTS AND OTHER WORKFLOWS
Copy and paste ready prompts plus everything else
Itai Sagi's AI Builder prompt library
Itai shared a full set of AI Builder prompts built around a simple framing: assume you are a brand new CEO and you want to know everything about your customers. These are published exactly as Itai shared them. Copy, paste, and adapt.
The starting prompt
Assume I'm the new CEO, tell me everything I need to know about our customers.
CEO Weekly Executive Brief (Highest Priority)
Purpose: A 5-minute overview every Monday.
Analyze all customer-facing calls from the previous 7 days. Create an executive summary for the CEO.
Include:
Top 5 themes discussed by customers
Emerging opportunities
Top customer concerns or risks
Competitive trends
Product feedback trends
Market sentiment (positive, neutral, negative)
Strategic recommendations for executive leadership
Quantify each theme with a number of mentions and example accounts.
Customer Voice of the Market
Purpose: Understand what customers are actually saying.
Identify recurring customer feedback from calls.
Categorize feedback into:
Product strengths
Product weaknesses
Feature requests
Security concerns
Pricing feedback
Implementation challenges
Support experience
Rank by frequency and business impact.
Competitive Intelligence Dashboard
Purpose: Help the CEO understand market positioning.
Review all calls mentioning competitors.
For each competitor:
Frequency of mention
Reason competitor was discussed
Win factors for our company
Loss factors to competitor
Customer perception
New competitive trends
Highlight any emerging competitive threats.
Executive Escalation Radar
Purpose: Surface issues before they become board-level problems.
Identify accounts with significant risk indicators.
Flag:
Executive dissatisfaction
Budget concerns
Competitive displacement
Delayed projects
Support escalations
Renewal concerns
Explain why the account was flagged and assess severity (Low, Medium, High).
Board-Level Strategic Themes
Purpose: Information the CEO can take directly to board meetings.
Analyze all customer-facing interactions from the past quarter.
Extract:
Major market shifts
Customer investment priorities
Security trends
AI-related opportunities and concerns
Regulatory concerns
Competitive changes
Summarize as board-level strategic insights rather than tactical sales observations.
New CEO Listening Tour Summary
Summarize executive conversations involving senior customer stakeholders.
For each call:
Customer business priorities
Strategic initiatives
Security challenges
Expected outcomes
Relationship health
Recommended executive follow-up
Focus on information relevant to a newly appointed CEO.
Katie Geoffroy's Raving Fans agent prompt
Katie, from our Customer Advocacy team, shared the agent she uses to surface raving fans, the customers showing real, genuine enthusiasm for Gong. It is a great example of the two-pronged approach: the agent identifies the advocates, then AI Builder turns them into quote cards and stories. Published exactly as she shared it.
You are analyzing customer conversations from Gong calls and account interaction data to identify raving fans — customers who express strong, genuine, and consistent enthusiasm for Gong.
Scope: Analyze all calls from January 1, 2026, through today. This agent runs on an ongoing basis — each time it is run, it should assess the YTD period up to the current date.
What to look for (customer-side only):
- Unprompted praise or excitement about Gong features, outcomes, or ROI
- Statements of strong business impact (e.g. time saved, pipeline visibility, coaching improvements)
- Expressions of loyalty or advocacy, such as recommending Gong to others, offering to be a reference, or comparing Gong favorably to competitors
- Emotional language indicating delight, not just satisfaction (e.g. "love", "game-changer", "can't imagine without", "transformational")
- Repeated positive sentiment across multiple calls or touchpoints within the YTD period
What to ignore:
- Polite, neutral, or transactional responses
- Positive sentiment expressed only by Gong reps (focus on the customer's words)
- One-off compliments without context or follow-through
For each raving fan signal identified, output the following:
1. Account name
2. Industry
3. Contact name and role (if available)
4. Date and call title
5. Direct quote or close paraphrase of the positive sentiment expressed
6. Sentiment category: Impact/ROI, Product Delight, Advocacy, or Loyalty
7. Confidence level: High, Medium, or Low — based on how clearly and genuinely the enthusiasm was expressed
8. Recommended next step: Case study conversation, Speaker discussion, or Monitor for follow-up
Present results as a structured list, ordered from highest to lowest confidence. If no raving fan signals are found in the available data, state that clearly.
More from the open floor
- Early-stage risk trackers. Shashank Mishra is building Smart Trackers for legal risk, internal alignment risk, and budget risk to surface deal risk early, then push it to managers, leadership, and reps with auto-generated follow-up emails. He has run churn-risk tracking before. First of its kind in the group, so stay tuned!
- Win/loss build-outs in progress. Lance Lew and Shashank both want to build win/loss and are connecting with Sydney. Great example of peers helping peers.
- Marketing and voice of customer. Rachael Taft is expanding into VOC and wants to pull themes and customer language out of calls. If your marketing team uses Gong, Katie would love to compare notes.
- Submit your feature requests. Two gaps came up today: sharing and collaborating on AI Builder prompts across a team. Both are great candidates for the feedback form. Drop requests here: bit.ly/GongFeatureRequests. Itai's tip: if you have a dedicated CSM, ask them to tag your call as a feature request in their Gong app.