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AI Data Extractor is an AI agent that turns customer conversations into structured, reliable data and reduces the need for manual data entry. It automatically pulls details from calls and saves them in the CRM fields you choose, so you can rely on accurate, consistent data.
Instead of asking reps to remember every competitor, risk, or reason for churn and update it by hand, AI Data Extractor keeps fields up to date based on what customers actually said. This improves data quality, supports better reporting, and frees reps to focus on selling.
What are some use cases?
AI Data Extractor gives you reliable, structured data on key topics without adding work for your team. Common examples include:
Track account and deal details: Make sure details specific to the account or deal aren’t missed, such as the number of requested seats, the discount offered on multi year deals, the name of the CRO, the expected closing date.
Track competitors: Capture which competitors were mentioned in deals or accounts so you can analyze win rates, pricing pressure, and positioning across segments.
Measure methodology adherence: Populate fields that track whether reps followed your sales methodology, asked key questions, or covered required topics.
Identify decision makers and influencers: Extract who is involved in the deal, what their roles are, and how engaged they are, then write this into deal or account fields.
Capture product interest and use cases: Record which products, use cases, or business problems were discussed, so you can analyze demand and tailor follow-ups.
These are just examples. Any data point you can define clearly as a question and store in a CRM field can become an AI field.
What is an AI field?
An AI field is a custom attribute that Gong AI calculates from customer conversations. AI Data Extractor works by filling AI fields
Each AI field is made up of three main parts:
Question: What you want Gong AI to answer based on conversations. For example: “Which competitors were mentioned in this deal?”
Additional instructions (optional): Extra context that helps Gong AI answer in a consistent way. Detailed instructions significantly improve the quality of the AI Field’s answers. For example, you can explain how your company defines a specific term or methodology, or what to prioritize when multiple answers appear.
Target object and data type
Target object: Either deals or accounts.
Data type: How the answer is stored. AI Data Extractor can return:
Yes / No
Free text
Single select picklist
You can choose more than one data type for the same AI field. For example, you might ask “Which competitors were mentioned?” and store:
A Yes / No value to indicate if any competitor was mentioned
A text value listing the competitors detected
A single select picklist value with the primary competitor
How AI Data Extractor works
AI Data Extractor runs behind the scenes, using Gong’s understanding of conversations together with your CRM setup. AI Data Extractor runs on published AI fields only. You can publish up to 20 AI fields per workspace.
Here’s how it works:
Admin defines an AI field in Agent Studio: A business admin (for example, enablement or RevOps) creates an AI field and configures:
The question and any additional instructions
The target object (deal or account)
One or more output data types (Yes / No, text, single picklist)
AI field is mapped to CRM fields: For each selected data type, the admin chooses an existing CRM field of a compatible type.
You can only choose a CRM field that is already imported into Gong.
Gong does not create new fields in the CRM, AI fields must be mapped to existing CRM fields.
If you don’t select a CRM field for the answer to be stored in, the AI result for that output is not stored anywhere.
Gong analyzes conversations: After the AI field is published, Gong checks accounts and deals with recent customer activity and analyzes their calls over a rolling historical window. Gong AI extracts answers to your question based on what was actually said as follows:
Gong only calculates AI fields for deals or accounts that had a call in the last month or received an inbound email.
For those deals and accounts, AI fields are calculated according to call data from the last six months. Data from other activities such as emails are not used to generate AI fields.
AI Data Extractor updates CRM fields automatically: Once a day, Gong recalculates AI fields for active accounts and deals. When the AI finds new or better information, it overwrites the previous value so your data stays current.
Because AI Data Extractor runs automatically, reps do not need to do anything for the fields to be updated. The agent keeps working in the background as new conversations come in.
Where you can see extracted data
AI Data Extractor writes into the CRM fields you choose, so you can use the results anywhere those fields are available. For example:
In Gong:
Deal boards, as columns on your boards
Account pages, as part of the account details
Other Gong views that include imported CRM fields
In your CRM:
List views
Reports and dashboards
Automations and workflows that depend on field values
From the perspective of your team, the fields behave like any other CRM field. The difference is that they are filled and maintained by AI rather than by manual data entry.
Create an AI field
Hover over AI Data Extractor and click Settings to open the AI Data Extractor management page.
Click + Add new field or
> Edit to edit an existing AI field.In Field concept, enter the question you want the AI field to answer.
In Provide context, instructions, or examples to help the AI find the right information, add detailed instructions to improve the quality of the answers. See Tips for writing effective AI field questions.
In Target object, select whether you want to save the answer in the Deals or Accounts object.
In Data type, mark the types of answers you want the data extractor to give. You can mark multiple data types. Options are:
Yes/No
Text
Single-select picklist
Select the CRM field to store the data extractor’s answer in.
Click Save if you are not ready to publish the AI field and want to test the AI field and make more changes.
Test the AI field
Test the AI field to see that you are getting accurate answers to your question before publishing.
Click Test it.
Select the account or deal you want to test the AI field on.
Check the results. The results include a value for each data type selected, together with an explanation of why the result was chosen.
If necessary, refine the question or context and click Run test again. You may have to do this several times before being ready to publish.
Click Change to run the test on a different account or deal.
Click Publish once the tests return the best answers.
Tips for writing effective AI field questions
AI Data Extractor is designed to extract specific, concise answers to questions that should have an objective correct answer. The types of questions you ask and the guidance you provide directly affect the quality of the results.
General:
Use natural language, ask don’t prompt: Write the way you’d speak to a new colleague with no prior context. Example: “Which competitors did the customer mention during this deal?”
Be specific: Ask clear questions about the conversation. Example: “Did the customer request a proof of concept or trial?”
State your intention: Explain the goal behind the question. The same question can have different intended outcomes. Example: “Which competitors were mentioned? Focus only on companies that were considered as an alternative option for conversation intelligence"
Clarify terms: Define terms that are ambiguous or jargon.
Questions:
Ask one question per AI field. Results will be less accurate if the question is compound or attempts to answer multiple things at once.
Only ask questions that can be answered from the content of a conversation. Questions that rely largely on metadata won’t be answered accurately. Example: “What stage is the deal currently in?” Stage is a CRM field. It isn’t spoken in the conversation, so the AI has no way to infer it reliably.
Provide context, instructions and examples:
For picklist fields:
Provide guidance and definitions on when each option should be chosen, particularly if the options use company/industry specific language that may not be specifically found in a conversation. This helps the model differentiate between similar options and understand nuances that may not be specifically obvious
When providing extra context, include common variations or alternate phrasing that might refer to a specific picklist option.
If there is an “Other” option you can map a text field for this AI Field and then collect those outputs there. The text field is updated even when the answer isn’t Other, so your reporting logic should only consider the text field if the picklist is set to “Other”. You won’t be able to constrain the Text field output to a single/limited word output so you may not be able to aggregate this field but it can be used to identify new options to add to the picklist
For Boolean (Yes/No) fields:
Include guidance for when each option should be chosen
Provide the appropriate default answer
For questions seeking confirmation of a completed action or a positive state for example, "Was the payment successful?", the default is false.
For questions seeking confirmation of an ongoing status or the absence of a negative state, for example, "Is the user still eligible?", the default is true.
The model will likely not adhere to any guidance given on output formatting
What you need to get started
To start using AI Data Extractor, you need:
A connected CRM with imported fields: AI Data Extractor uses CRM fields that are already imported into Gong, such as fields from Salesforce, HubSpot, or Microsoft Dynamics. Make sure the fields you want to populate exist in your CRM, are imported into Gong, and support write access from the Gong integration user.
A business admin to configure AI fields. This includes:
Deciding which questions to ask
Choosing the target object and output types
Mapping each AI output to the correct CRM field
Once your first AI fields are published, AI Data Extractor starts analyzing conversations, updating CRM fields, and giving your organization richer, more reliable data without extra work for your reps.