Black Friday, the holiday shopping season, and major sales campaigns can put an online store to the test in just a few hours. As order volumes rise, so do questions about product availability, delivery changes, returns, complaints, and other requests that customer service and internal teams need to handle. What seems manageable during normal operations can quickly become a bottleneck.
This is where artificial intelligence can make a difference. AI can take over routine customer interactions, retrieve order information, and help employees process incoming requests. However, its adoption also raises an important question: which tasks can safely be handed over to AI, and when should decisions remain in human hands?
The answer depends on more than technological capabilities. Customer expectations, data quality, and the rules governing individual processes are just as important.
Customers Are Using AI More Often, but They Still Want Control
Artificial intelligence is gradually becoming a regular part of online shopping. Customers use it to search for products, compare offers, and find suitable gifts, but they are not yet willing to hand over every decision.
According to the Deloitte Holiday Retail Survey 2026, 39% of surveyed US consumers use generative AI as their primary tool for at least one holiday shopping activity. However, only 16% of respondents would allow AI to complete a purchase on their behalf.
A similar attitude can be seen in customer service. An August 2026 Gartner survey involving 3,566 B2B and B2C customers found that half of respondents consider customer service interactions easier when companies use generative AI. At the same time, 87% believe it is essential to have the option of speaking to a human representative.
The findings of both studies suggest that customers are open to new ways of interacting with businesses and appreciate the convenience they offer, provided they can still intervene or request personal assistance when needed.
For online retailers, this means thinking beyond how many requests can be automated and considering how automation affects the customer experience.
Which Tasks Can AI Take Over During Peak Shopping Periods?
AI delivers the greatest value in tasks that are repetitive, involve processing large amounts of information, or place unnecessary pressure on employees. When implemented effectively, automation can significantly simplify these processes and free up team capacity when online stores are under the greatest pressure.
1. Repetitive Questions and Order Information
Where is my order? When will it arrive? Is the product in stock? Can I change my delivery address?
These are the kinds of questions customer service teams handle every day, and their volume naturally increases during peak shopping periods. Although the questions themselves are often straightforward, answering them takes time, especially when employees need to retrieve information from several different systems.
An AI assistant connected to order management, inventory, and logistics systems can retrieve the necessary information and provide customers with answers without employee involvement. This allows customer service teams to focus on more complicated cases that automated solutions cannot resolve.
However, reliable system integration is essential. If AI cannot access the current status of an order, it cannot confidently tell a customer when their package will arrive. An incorrectly promised delivery date can cause far greater problems during the holiday shopping season than a delayed response.
2. Sorting Requests and Preparing Them for Resolution
Automation does not necessarily mean that AI must handle every request from start to finish. It can save employees considerable time simply by identifying the problem, gathering relevant information, and preparing the next steps.
Imagine a customer who needs to change their delivery address. AI can first identify the order, check its current status, and determine whether the change is still possible under the company’s rules. If the package has not yet been handed over to the carrier and the customer has completed the necessary verification, an automated process can update the address.
If the order has already been dispatched or unusual circumstances arise, the system can forward the request to a customer service representative. The employee receives the relevant information and does not have to investigate the case from scratch.
The same approach can be applied to product returns, complaints, and order modifications. The benefits go beyond faster customer communication, as AI can also help reduce administrative work that consumes a significant portion of employees’ time during peak shopping periods.
3. Monitoring Operations and Detecting Problems Early
Beyond customer communication, AI can also support the analysis of operational data. For example, if inquiries about delayed deliveries begin to increase unusually quickly, the system can alert responsible employees to a potential problem before it affects a larger number of customers.
Similarly, companies can monitor unexpected increases in canceled orders, changes in demand, or workloads across different parts of their operations. Combined with historical data, these insights can also help businesses plan inventory levels and staffing requirements.
Generative AI is not always necessary for these tasks. Some can be handled reliably using conventional automation, analytics tools, or predefined alerts. The right choice depends primarily on the nature of the process and which solution can deliver the desired outcome at a reasonable cost.
When Should Humans Still Make the Decisions?
Although AI can process large volumes of requests and work with extensive datasets, certain situations still require individual judgment, practical experience, or clear accountability for the decisions being made.
Unusual Complaints and Individual Solutions
Imagine a customer receiving a damaged Christmas gift just a few days before the holidays. A standard response linking to a returns form is unlikely to help much when the customer needs to know whether a quick replacement or another solution is available.
AI can retrieve the order history, check the availability of a replacement product, and prepare an overview of possible solutions. However, if the situation requires an exception to standard procedures or individual compensation, the case should be handled by an employee who can assess all the circumstances.
This does not mean that every complaint must be handled manually. Straightforward cases with clearly defined conditions can be automated. For more complex situations, however, it is better to use AI to support employees rather than allow it to make decisions independently.
Decisions with Financial Consequences
Refunds, special discounts, and exceptional compensation can directly affect an online retailer’s financial performance. Even in these areas, however, automation does not need to be ruled out entirely.
For example, a company can set a maximum compensation amount that the system is authorized to approve independently or define the conditions under which a refund can be processed automatically. If a request exceeds the specified limit, contains conflicting information, or requires individual assessment, it can be forwarded to an authorized employee.
This creates a process in which technology handles routine tasks while employees focus their judgment on cases where it is genuinely needed.
Situations Where Customers Lose Trust
Customers’ willingness to interact with AI has its limits, as confirmed by the Five9 Consumer CX Report 2026, published in October 2026.
The survey of 3,000 consumers from the United States, the United Kingdom, and Germany found that 55% of respondents trust AI to resolve their problems when they have the option of speaking to a human. Without that option, trust drops to just 26%.
Another interesting finding is that 92% of respondents are willing to interact with AI for less than ten minutes before asking to speak to a human representative. Nearly half of those who abandon automated conversations also cite repeated failures to understand their requests as the main reason.
Online retailers should therefore make it easy for customers to reach a human representative whenever an automated solution fails to meet their expectations. Ideally, the employee should receive the previous conversation and all relevant information so that the customer does not have to explain the entire problem again.
How a system handles these situations can have a greater impact on the customer experience than the speed of its automated responses.
Even the Best AI Agent Needs Reliable Data and Clear Rules
When introducing artificial intelligence into e-commerce, companies often focus on choosing the right tool, while the real challenges may lie in the processes that the new system is supposed to support.
Order management systems, warehouses, CRM platforms, and customer service departments do not always work with the same information. If reliable integrations are missing or some data is outdated, implementing AI alone will not solve the problem.
A typical example is a product that an online store still lists as available even though the actual inventory has already run out. AI relying on outdated information may then confirm availability and promise a delivery date that the retailer cannot meet.
Before implementation, companies therefore need to assess not only data quality but also how the entire process works, where the system obtains its information, and what permissions it will have. The design should also include rules for handling personal data, modifying orders, and escalating unusual cases to employees.
The more autonomy AI receives, the more important it becomes to maintain visibility into its actions and retain the ability to intervene when something starts going wrong.
How Can Businesses Tell Whether AI Is Delivering Real Value?
The number of messages handled by a chatbot or the volume of automated requests may initially look like a positive result. However, if customers repeatedly contact support because of inaccurate answers, the actual savings may be considerably smaller than expected.
Demonstrating financial value remains one of the challenges businesses face. According to a July 2026 Gartner survey, only 24% of customer service leaders were able to demonstrate positive financial returns across their AI initiatives. The research was based on responses from 1,303 leaders across various industries.
This does not automatically mean that the remaining projects were unprofitable. It does, however, highlight that implementing AI is not in itself proof of financial value.
Online retailers should therefore look beyond the number of automated requests and monitor factors such as average resolution time, the proportion of successfully resolved cases, repeat customer contacts, and customer satisfaction. To properly assess the return on investment, they also need to account for integration, operation, maintenance, and ongoing quality control costs.
Only by comparing these metrics with performance before implementation can companies determine whether automation is delivering the expected improvements or simply shifting work from one place to another.
Preparing for Peak Shopping Periods Starts Well in Advance
Black Friday and the holiday shopping season are not ideal times to experiment with untested solutions. Under heavy operational pressure, a single mistake can affect a large number of customers.
A better approach is to start early, select a few specific processes, and identify where automation can deliver the greatest benefit. This might include answering order status inquiries, sorting customer requests, or preparing documentation for complaints and returns.
After the initial implementation, companies should continuously evaluate results and gradually extend automation to other areas. It is equally important to test situations in which AI lacks sufficient information, encounters an unusual request, or cannot safely complete a task.
These scenarios are critical in determining whether a system will remain reliable when request volumes increase several times over.
Finding the Right Balance Between AI and Humans in E-Commerce
Peak shopping periods reveal how well online retailers have organized their internal processes and where unnecessary delays occur. Artificial intelligence can help address many of these challenges, particularly by reducing repetitive administrative work, improving access to information, and enabling faster responses to customer requests.
Its value, however, depends on how well it fits into the company’s operations. Without reliable data, connected systems, and clearly defined rules, even a technologically advanced solution can create more problems than it solves.
For e-commerce leaders, the most important question is not how much work AI can take over, but which tasks are worth automating and where human judgment is still essential.
A well-designed division of responsibilities can help businesses manage peak shopping periods with less operational pressure while maintaining the quality of customer service. Ultimately, that should be the measure of successful automation.