{"id":62557,"date":"2026-11-12T03:03:37","date_gmt":"2026-11-12T11:03:37","guid":{"rendered":"https:\/\/newrcblog.wpengine.com\/us\/en\/blog\/?p=62557"},"modified":"2026-11-12T12:02:38","modified_gmt":"2026-11-12T20:02:38","slug":"how-to-set-up-ai-agents-alerts-holiday-rush","status":"publish","type":"post","link":"\/us\/en\/blog\/how-to-set-up-ai-agents-alerts-holiday-rush\/","title":{"rendered":"How to set up AI agents and alerts for the holiday rush"},"content":{"rendered":"

It\u2019s the Monday after Black Friday, and many customers want to know the same thing: where is my order? Agents answer that question over and over again, while customers with billing disputes and other urgent issues wait their turn.<\/p>\n

In the first edition of our holiday readiness series, we explored how to build the forecast and schedule that put the right agents in place for days like this. Even so, a well-staffed team can only answer so many repetitive questions before everyone else starts waiting longer.<\/p>\n

That\u2019s where AI agents can help. They can take on many of these routine requests, as long as customers still have a clear path to a human agent when they need one. Shoppers rank automated systems that won\u2019t connect them to a person as a top frustration<\/a> with customer service.<\/p>\n

Getting the balance between automation and human support right takes preparation. We\u2019ll walk through how to set up AI agents and escalation rules before the holidays, then cover the real-time alerts that help supervisors spot frustrated customers and the adherence dashboards that help WFM analysts catch schedule gaps as they happen.<\/p>\n

<\/a>Step 1: What were your customers\u2019 most common holiday questions last year?<\/h2>\n

Before deciding what an AI agent should handle, look at what customers asked about last season. Pull contact reasons from the holiday period and rank them by how often they came up. For retailers, order status and return requests can rank near the top of the list. Billing questions can follow close behind, especially when customers spot a duplicate charge or a promotional discount that didn\u2019t apply at checkout.<\/p>\n

As you review, note which channels customers used for each type of request. Customers checking on an order may have favored SMS, while customers with billing questions mostly called.<\/p>\n

With that list in hand, you\u2019ll know which knowledge content to update first and which conversations are good candidates for AI agents.<\/p>\n

<\/a>Step 2: How should I update my knowledge base before the holiday rush?<\/h2>\n

Start with the issues customers had to contact you about more than once last season. Low first contact resolution (FCR) can point to a gap in the knowledge base, such as an outdated article or an answer human agents had to piece together from several sources.<\/p>\n

Closing those gaps before the holidays helps both human and AI agents resolve more requests on the first try. From there, add content for anything new since last year, like product launches or policy changes, so all agents can reference that information during a conversation.<\/p>\n

Ideally, human and AI agents work from the same knowledge base. When both draw on one source, customers get consistent answers no matter who they\u2019re speaking to, and teams only have to update content in one place.<\/p>\n

Once the content is current, you\u2019re ready to decide which requests an AI agent can resolve on its own.<\/p>\n

<\/a>Step 3: Which contact center issues can an AI agent handle during the holidays?<\/h2>\n

The best candidates for an AI agent are low-risk, frequent requests that follow a predictable path to a firm answer. Checking an order status is a good example. An AI agent connected to your order management system can verify the customer\u2019s identity and share the latest tracking details without involving a human agent.<\/p>\n

Initiating a return works the same way when your policy has clear rules. The AI agent can confirm whether an item qualifies, where the item can be sent, and issue the customer a return label. Keep requests that require judgment, like a payment dispute or a complaint about a damaged gift, with human agents.<\/p>\n

Begin with a small set of use cases and test them well before peak season. Review conversations from the test period to see where the AI agent resolved the request and where customers asked for a person, then refine the setup before adding more request types.<\/p>\n

<\/a>Step 4: What escalation rules should an AI agent have?<\/h2>\n

Even a well-designed AI agent will need to hand some conversations to a person, and escalation rules tell it when.<\/p>\n

Customer sentiment is a natural first trigger. If a customer repeats the same question or uses frustrated language, route the conversation to a human agent.<\/p>\n

Sensitive topics are another trigger. Send conversations about suspected fraud or a missing high-value gift straight to a person, even when the AI agent could handle part of the request.<\/p>\n

Escalation rules also help prevent doom loops, where a customer gets stuck repeating the same steps with an AI agent and can\u2019t get through to a person. Give customers a clear way to ask for a human agent at any point, like typing or saying \u201cagent\u201d or repeatedly pressing zero. When the AI agent hands off a conversation, it should pass along what the customer already shared, including their verified identity, reason for reaching out, and a summary of what has transpired so far so the customer doesn\u2019t have to start over.<\/p>\n

<\/a>Step 5: What real-time alerts should contact center supervisors set up?<\/h2>\n

Escalation rules tell an AI agent when to hand off, but supervisors also need to know when a conversation needs their attention, whether the customer is speaking with an AI or human agent.<\/p>\n

Real-time trackers can flag critical moments as they happen, like a customer who mentions canceling their account, mentions a competitor deal, or threatens to post a complaint publicly. Supervisors can then coach the agent through the conversation or join it directly.<\/p>\n

Set up alerts for the issues your business is most likely to face during the holidays. A retailer might track mentions of a gift that missed its delivery date, while a company with new subscription products might track questions about billing changes.<\/p>\n

<\/a>Step 6: How do I track schedule adherence for temporary holiday agents?<\/h2>\n

Even a properly built schedule only works if agents are logged in and available when customers need them. Schedule adherence measures how closely agents follow their assigned schedules throughout the day, and temporary agents may need extra support staying on track while they learn different processes during their first few weeks.<\/p>\n

Before peak season, a WFM analyst can build out a custom holiday adherence dashboard within their WFM solution that provides a real-time view of adherence across different teams. Separating views makes it easier to spot patterns early, especially when working with temporary agents. If several new agents at one location consistently return late from breaks, for example, analysts can review those agents\u2019 adherence in more detail and adjust break times or alert their supervisor to take additional action.<\/p>\n

Analysts can also set thresholds that notify them when a team falls below its adherence target for a specific interval, giving them time to adjust coverage before average customer time in queue increases. The contact volume and staffing data from this season then becomes part of the history that helps AI forecasting improve for next year.<\/p>\n

<\/a>What comes next after your automation and alerts are in place<\/h2>\n

With AI agents handling repetitive requests like order status checks, human agents spend less time answering the same questions, a common source of burnout, and focus on the conversations that need their expertise instead. Clear escalation rules get customers to a person quickly whenever they need one.<\/p>\n

Supervisors and WFM analysts can also catch problems early, whether it\u2019s a frustrated customer or a team falling behind schedule. Live alerts help supervisors quickly identify difficult conversations, while adherence dashboards can show analysts where staffing adjustments need to be made before customers are affected.<\/p>\n

Once peak season begins, teams need to run the plan day to day and adjust as they learn. In the next edition of our series, we\u2019ll cover how to use daily standups and same-day conversation analysis to keep both AI agents and human agents performing well through the holidays.<\/p>\n","protected":false},"excerpt":{"rendered":"

It\u2019s the Monday after Black Friday, and many customers want to know the same thing: where is my order? Agents answer that question over and over again, while customers with billing disputes and other urgent issues wait their turn. In the first edition of our holiday readiness series, we explored how to build the forecast …<\/p>\n","protected":false},"author":1145,"featured_media":62558,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-62557","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-customer-experience"],"acf":[],"yoast_head":"\nHow to set up AI agents and alerts for the holiday rush | RingCentral Blog<\/title>\n<meta name=\"description\" content=\"It\u2019s the Monday after Black Friday, and many customers want to know the same thing: where is my order? 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