Reviewing and implementing AI recommendations to automate tasks

Explore AI-powered recommendations to automate tasks and optimize ticket
workflows. Review suggestions for triggers, autoreplies, and unused business
rules based on ticket intent, sentiment, language, and entities. Improve
auto assist setups and review AI-generated procedure drafts. Take action
or dismiss recommendations, and track changes in the recommendations
archive. Enhance efficiency by reducing manual tasks and improving response
times.




As you begin to use Copilot in your account, a list of AI-powered
recommendations are provided and updated weekly on the Recommendations
page in Admin Center,
in
Admin Center Home,
and contextually on relevant Admin
Center pages.


These recommendations provide actionable guidance on how you can automate
tasks and
optimize your ticket workflows by creating triggers and autoreplies based
on tickets’ intent,
sentiment, language, and detected entities, as well as guidance for reviewing
unused business
rules and macros. Recommendations also provide guidance on improving
your auto assist set up
and alert you to when there are new AI-generated procedure drafts to
review and publish.

This article includes these sections:


Understanding Copilot recommendations


Taking action on recommendations


Viewing the recommendations archive


Examples of recommendations


Related articles:


Monitoring and optimizing effective AI
setup in your account




Understanding Copilot recommendations


Copilot recommendations proactively identify opportunities to automate
repetitive tasks, improve ticketing workflows, clean up unused business
rules, and tailor
Zendesk AI to your needs.


Depending on your ticket data, your personalized recommendations
may
include:


Suggestions for new or updated triggers and autoreplies based
on tickets’ intent,
sentiment, language, and detected entities.
Intelligent triage
must be turned on and configured in your
account to see these recommendations.




See
Examples of recommendations
below for more information.



Suggestions to review unused triggers, automations, and macros.

These
recommendations help you manage your business rules and
macros that build up over
time.






Guidance for improving your auto assist setup, including
recommendations for
reviewing procedures with a high takeover rate.

For example, say you have a procedure
where agents took over in 42% of interactions. Copilot
may make a recommendation to
review the procedure.






Procedure recommendations to help improve auto assist quality
and reduce the time you
spend writing procedures. Each recommendation may contain
up to three
AI-generated procedure drafts
per brand.




Procedure recommendations are based
on your top ticket intents or common customer support
issues, existing relevant solved
tickets, and existing knowledge articles.





Before implementing a recommendation, you can view information and
supporting
insights, including:



What actions the recommendation will perform.

Recommended
triggers can include actions for routing to an agent or group,
or updating the ticket
type, priority, form, or status. Recommended autoreplies
include actions for sending
automatic responses.



How your resolution time could improve, how you can reduce
time spent on certain tickets, or how the recommendation will
help agents.


How many tickets in a certain period of time had the detected
intent, sentiment, or entity, what percentage of those tickets
had a certain action taken,
and how much time was spent manually performing this action on
average.

When a business rule or macro was last used.


If you want to take action on a recommendation, click the link to
navigate to the relevant page.





If you choose not to act on a recommendation, you can provide
feedback about why you dismissed it so that your future recommendations
are
more accurate and useful over time. You might want to dismiss
recommendations that aren’t relevant, are already automated, or that
need
different logic.


As an admin, you always have full control over what recommendations
are implemented in your account. Any decisions and actions about
recommendations require
your approval. You can view all accepted and dismissed recommendations
from the
recommendations archive.




Taking action on recommendations


Relevant recommendations are updated weekly. All Copilot customers
will be able
to see the Recommendations page, but you might not have recommendations.


See
Examples of recommendations
to
view examples of the types of recommendations you may see in your
account.


To take action on a recommendation



In Admin Center, click
AI in the sidebar, then select Admin copilot > Recommendations.


To quickly find a recommendation type, click
Filter.


In the Feature menu, select one
or more options, then click
Apply
filters.





To learn more and take action, click a recommendation to expand
it.

Depending on your
ticket data, the options available to you will differ:



To create a recommended trigger or autoreply, click
Review trigger.




The Create
ticket trigger page opens with prefilled trigger
information.


Review the
prefilled information and enter a
Trigger name and
Trigger category.
Click Create trigger.



To review an unused business rule or macro, click the
button to navigate to the
relevant page.




Review the
unused trigger, automation, or macro and delete it
if necessary.



To improve an auto assist procedure, click
Edit procedure.




Edit the procedure
as needed and click
Publish.



To review an AI-generated procedure draft, click the
name of one of the procedures
in the recommendation to open it in Knowledge admin.

Alternatively, click
Review
procedures
to open your list of procedures in Knowledge admin.







You can review and edit the procedure draft and must
publish it before it's
available to auto assist. See
Reviewing and publishing AI-generated procedures for auto
assist.



When you're done, return to the recommendation.


Open the Actions menu at the bottom
of the recommendation and
select Mark as done to remove it
from the list.




If you don’t
want to take action on a recommendation, select
Dismiss to remove the
recommendation from the list.


You can optionally give feedback about a
recommendation you dismiss.





It's important to share your feedback
to help improve the accuracy and relevance of future recommendations.






Viewing the recommendations archive


Recommendations that are marked as done or are dismissed are added
to your recommendations
archive for you to review as needed.


Viewing your recommendations archive


In Admin Center, click
AI in the sidebar, then select Admin copilot > Recommendations.


Click View archive.
Your Recommendations archive opens.




Click an archived recommendation to learn more.





Examples of recommendations


The examples in this section describe the different types of recommendations
you may see in
your account.



Intent-based trigger recommendation example











Recommended action



Route specific tickets to group: Support





Expected improvement



Resolution time could improve by 1h





Description and rationale




Tickets with some intents tend to be routed
to the same agent group. Automate
this action to reduce manual triage and help
improve resolution time.






Supporting insights





167 tickets (88%) had the intent: Graduation ceremony date.

Most of these tickets (16.7%) were
routed to the same group:
Support.


On average, it took 1h to manually
route each ticket. This could be
reduced through automation.











Intent-based autoreply recommendation example











Recommended action




Send an autoreply to tickets with intent:
Transaction failed (+4 more)






Expected improvement



Resolution time could improve by 1h





Description and rationale




Tickets with some intents tend to be answered
with the same reply. Automate
this action to reduce manual work and help
improve resolution time.






Supporting insights






167 tickets (88%) had the intent:
Transaction failed, Battery
charging issue, Rate lock request
accepted, How to enroll in employee
benefits, Sending late arrival form.


Most of these tickets (16.7%) were
answered with the same
reply.



On average, it took 1h to manually
route each ticket. This could
be
reduced through automation.












Sentiment-based recommendation example











Recommended action




Automatically set ticket priority to High
or Urgent for tickets with negative
sentiment






Expected improvement



Improve agent response times and reduce escalation risk





Description and rationale




Negative-sentiment tickets are usually urgent.
By increasing ticket priority
automatically, these cases can be addressed
right away. It’s a way to improve
customer satisfaction and prevent churn.






Supporting insights






In the last 30 days, 23 tickets (12%)
were assigned negative
sentiment.


28% of escalations came from delayed
responses to these
tickets.



On average, it took 6 hours per
ticket to update priority manually.
This
could be reduced through automation.












Entity-based recommendation example











Recommended action



Change ticket type for entity: Account error





Expected improvement



Resolution time could improve by 1h.





Description and rationale



Tickets with some entities tend to have their
ticket type changed to the same
one. Automate this action to reduce manual triage
and help improve resolution
time.





Supporting insights





167 tickets (88%) had one of these entities: Account error

Most of these tickets (16.7%) had
their ticket type changed to:
Problem



On average, it took 1h to manually
triage each ticket. This could
be
reduced through automation.












Language-based recommendation example











Recommended action




Route specific language tickets to group:
Iberia support team






Expected improvement



Resolution time could improve by 54min.





Description and rationale




Specific language tickets tend to be routed
to the same group. Automate this
action to reduce manual triage and help improve
resolution time.






Supporting insights





4370 tickets (10%) were assigned this language: Spanish

Most of these tickets (98%) were
routed to the same group:
Iberia support team



On average, it took 54min to
manually route each ticket. This
could be
reduced through automation.