What AI agents can resolve end to end, where human expertise matters, and how to measure the impact on cost, customer effort and agent experience.

In our last post, a late order turned into three contacts: a chat, an email and finally a call. Here’s the same order, handled once.

The shopper explains the problem in one message. An AI agent takes it from there and arranges a replacement that arrives before Friday. Nobody has to chase anyone.

Meanwhile, your most experienced agent isn’t reading out a tracking update. They’re helping the customer with their order, and where their judgment matters most. Multiply that across thousands of orders and the math of peak season changes:

  • AI agents absorb the volume spike by resolving routine requests end to end.
  • Repeat contacts fall.
  • Customers spend less effort (CSAT).
  • Agents spend more time on meaningful work, which lifts agent satisfaction (ESAT).
  • For brands, cost per resolution goes down.

Here’s how agentic AI customer service makes that possible, which tasks it can take over and what to measure to know it’s working.

What is agentic AI in customer service?

Agentic AI in customer service uses AI agents that take action to complete a request, not just answer questions. Gartner describes these systems as able to act autonomously on a customer’s behalf. In practice, AI agents work within your connected systems, business policies and defined permissions, and bring in a person when a decision needs human judgment.

How does agentic AI work in a retail contact center?

Here’s how agentic AI resolves requests end-to-end:

  • Understand the request. Confirm the customer’s identity, the affected item and the delivery deadline.
  • Check the available options. Review order history, inventory, delivery estimates and replacement policy.
  • Act within its authority. Arrange an eligible replacement, or ask a specialist to approve an exception.
  • Verify and confirm. Check that the new order was created, share the details and record the action.

Which peak season tasks can AI agents take over?

The best candidates are frequent requests with clear policies and reliable system access. At peak, that usually means:

  • Order tracking and delivery problems.Check carrier updates and offer an authorized replacement, refund or pickup option when available.
  • Proactive delay alerts. Use delivery signals to identify affected orders and contact shoppers with useful options.
  • Order changes. Update an address, change an item or cancel an order while fulfillment rules still allow it.
  • Returns and exchanges. Check eligibility, initiate the return, send a label and arrange an approved exchange or credit.
  • Promotions and pricing. Validate an offer and apply an eligible adjustment within agreed limits.
  • Account and loyalty requests. Retrieve points balances, resend gift receipts and support approved account-recovery steps.

In Gartner’s August 2026 survey, 58% of customers who used GenAI said they had already used it to complete a task on their behalf. Shoppers are ready to hand things off. Your job is to make that handoff dependable and give human specialists a clear role: approving exceptions, advising on sensitive cases and taking over when needed, with a record of what the AI checked and what’s still open.

What changes for customers, agents and the business?

  • For customers, the benefit is less work: no repeating the story, no chasing for updates and issues resolved on first contact. That is what protects loyalty. PwC found that 52% of consumers have stopped buying from a brand after a bad experience. Every problem solved the first time is one less reason to leave.
  • For human agents, fewer routine requests mean more time for the cases that need judgment, and AI assistance helps with those conversations too. When AI agents resolve routine requests on their own, the same “where is my order?” question stops coming back to the queue. With lesser repeat contacts, human agents spend their time on cases that need judgment, which makes the work more meaningful and helps reduce burnout and attrition during peak.
  • For the business, the opportunity is to handle more demand without increasing staffing at the same rate. Fewer repeat contacts mean a lower cost per resolution, better CX, ESAT and lower churn. Taken together, better customer experience, more engaged agents, lower operating costs and less churn make the case for agentic AI to handle peak season volume.

Which contact center KPIs should retailers track?

KPI What to check
First contact resolution Did the issue close in the initial interaction without a repeat contact within your defined measurement window?
Repeat contact rate Did the customer return about the same issue, including through another channel?
Time to resolution How long did the whole request take, including approvals and follow-up?
Speed to answer and abandonment Did human queues improve, and could customers reach specialists when needed?
Cost per resolution Include AI, system and human support costs when calculating the cost of completed requests.
CSAT and customer effort Ask whether the problem was solved and how much work the customer had to do.
Agent workload and retention Track effort and engagement during peak, and review attrition over a longer period.

Where should retailers start before peak season?

Choose one or two contact reasons from your own volume data. Connect the systems needed to complete them, define approval limits and agree on when specialists should consult or take over.

Test realistic demand spikes alongside failed workflows, unavailable stock and policy exceptions. Start with a limited rollout, review outcomes daily and expand when the service is reliable.

The real test comes after Cyber week: does the shopper get the replacement arranged, the refund confirmed or the order corrected without having to chase anyone? Build your peak season plan around those completed outcomes.

Frequently asked questions

1. What is agentic AI customer service?

It uses AI agents to complete customer requests from start to finish, such as arranging a replacement or processing a return, within your policies and permissions. A person steps in when a decision needs judgment.

2. Which retail requests should AI agents handle first?

Frequent, rules-based requests with reliable system access: order tracking and delivery problems, order changes, and returns and exchanges.

3. Will agentic AI replace human agents?

No. It takes routine work off their plate so experienced agents can focus on exceptions, sensitive cases and customers who need a person.

See how [24]7.ai can help prepare your contact center for peak season. Talk to us.