Currently, intelligent triage is available as an early access program (EAP) for organizations who meet the following requirements:

You cansign up for the intelligent triage EAP here. If you don’t meet the current requirements, you can still sign up and be placed on a waitlist.

When you enable intelligent triage, the system adds four new fields to your tickets:

• Intent: A prediction of what the ticket is about. To see the possible values, you cango to theTaxonomytabof the settings page.
• Intent confidence: How likely it is that the intent prediction is correct. Possible values areHigh,Medium, andLow.
• Language: A prediction of what language the ticket is written in. To see the possible values, you cango to theTaxonomytabof the settings page.
• Language confidence: How likely it is that the language prediction is correct. Possible values areHigh,Medium, andLow.
• Sentiment: A prediction of how the customer feels about their request. Possible values areVery Positive,Positive,Neutral,Negative, andVery Negative.
• Sentiment confidence: How likely it is that the sentiment prediction is correct. Possible values areHigh,Medium, andLow.

These fields are populated via ticket updates by Zendesk’s machine learning model based on the content of the first message of a ticket when it’s submitted. Agents canchange the Intent, Language, or Sentiment fieldsif necessary.

When the fields are populated, tags are also automatically added to the ticket to help you build triggers, automations, and reporting. These tags reflect the values in theIntent,Language, andSentimentfields, and are structured as follows:

• intent__value of Intent field
• language__value of Language field
• sentiment__value of Sentiment field

When you enable intelligent triage, the system automatically fills out theIntent,Language, andSentimentfields, but these fields are visible in tickets by default only if you have a single ticket form.

• If you have multiple ticket forms and you want agents to be able to see and change these fields in tickets,edit your ticket formsand add the new fields by dragging them into the form.
• If you have a single ticket form but you don’t want agents to be able to see and change these fields in tickets,edit your default ticket formand remove the new fields by dragging them off the form.
• If you want to use the intelligent triage field values only for Explore reporting or API use, you don’t need to add them to any ticket forms.

If you opt to let agents see theIntent,Language, andSentimentfields in tickets, they can update the values in these fields if they feel they’re not correct.

Remember that Zendesk’s machine learning model is intended to detect the language, intent, and sentiment of a ticket’s first message only. Agents should understand that any updates they make to these fields should still be based on the first message.

For theIntentfield, the machine learning model used for intelligent triage is currently trained to identify intents for e-commerce and software organizations specifically. Additionally, intent and sentiment detection works only for the following languages:

• English
• French
• German
• Italian
• Portuguese
• Spanish

For theLanguagefield, intelligent triage can detect approximately 150 different languages.

You can see all possible intent and language values on theTaxonomytab of the intent or language settings page.

To see all possible intent and language values

Sentiment is a prediction of how positive or negative a customer feels about the request they’re submitting. The prediction is made based on the text of the customer’s first message and is grouped into one of the following categories:

• Very positive: The message likely contains strong positive words (like “brilliant” or “perfect”), positive words modified by intensity adverbs, or multiple positive sentences.
• Positive: The message likely contains phrases expressing gratitude, or one to two positive sentences.
• Neutral: The message likely contains factual statements without additional negative quantifiers (like “any” or “always”), or a mix of positive and negative statements.
• Negative: The message likely contains phrases expressing frustration, complaints containing negative words, or repetition of the same dissatisfaction.
• Very negative: The message likely contains strong negative words, capitalized text, multiple exclamation marks, or multiple negative phrases.

Zendesk’s machine learning model is specifically calibrated for customer service. This means that a ticket isn’t assigned a negative sentiment just because a customer has an issue with their order, can’t find the information they need, or some other similar “negative” situation. Instead, the model is tuned to analyze sentiment with the assumption that the customer is contacting customer service because they have an issue that needs to be addressed.