How AI for customer service can support peak season in e-commerce

It can automate repetitive customer enquiries, support agents with faster access to knowledge, reduce manual work, analyze larger volumes of interactions and help identify issues generating additional contacts. 

The objective is not to replace agents during peak season. It is to use human capacity where it is actually needed and automate or support the work that does not require the same level of human involvement. 

Key takeaways

• AI can reduce how much additional headcount is needed during peak season.

• Conversational AI for customer service can be used to automate predictable interactions, not simply high-volume ones.

• Generative AI tools can support agents with knowledge, QA and routine operational tasks.

• Analytics can identify and reduce the causes of avoidable contacts.

• AI should be implemented and tested before peak volumes begin. Peak season is not the time for technology experiments.

Adding agents solves only one part of the problem

Scaling the team increases customer service capacity, but it also increases the workload around the operation. 

Additional agents need to be recruited, trained and supported. More interactions mean more quality monitoring, reporting and after-contact work, while team leaders have larger teams to manage. 

At the same time, peak season generates high volumes of repetitive enquiries about order status, delivery, returns, payments or product availability. 

This creates two opportunities for AI: reducing the number of interactions that require an agent and reducing the operational workload associated with the interactions that still do. 

That is much more direct and also sets up the rest of the article: customer-facing automation + AI supporting the operation behind it. 

Automate predictable, repetitive interactions

High-volume, repetitive enquiries can consume significant agent capacity during peak season. Order status, delivery information, appointment confirmations or basic account enquiries can often be handled by a chatbot or voicebot when the required information is available and the process follows a predictable path. 

However, high volume alone does not make a process suitable for automation. Some frequent enquiries still require judgement, access to information across multiple systems or an understanding of the customer’s individual situation. 

The key criterion is therefore predictability: can the interaction be resolved correctly without human involvement? 

Automating the right contact types reduces the volume that needs to reach agents and allows additional human capacity to be focused on more complex cases. 

You can leverage AI voicebot when you have capacity problem

At Axendi, this is one of the roles of Primebot, our voicebot and chatbot solution. It can automate selected customer interactions and processes, while transferring cases that require human involvement to an agent. In peak-season operations, this allows automation to absorb part of the volume increase without extending automation to interactions that are better handled by people. 

Support agents with faster access to knowledge

Peak-season teams often include newly recruited agents who need to become productive within a short period. They may know the core processes but still need support with less familiar products, procedures or customer scenarios. 

AI-supported knowledge tools can give agents faster access to relevant information during customer interactions, reducing time spent searching across systems or asking supervisors for help. 

This can shorten the learning curve for new employees and reduce the operational pressure created by rapid team expansion. 

AI knowledge aggregate can be a very useful tool for your agents at the peak times

We developed Gutenberg to support agents with access to internal knowledge during customer interactions. Instead of searching through multiple documents or relying on a supervisor for routine information, agents can find the relevant knowledge faster.

This becomes particularly useful when peak-season teams include newly recruited employees who are still building familiarity with the operation. 

Reduce the support workload for experienced teams

New agents increase capacity, but they also require support. During rapid scaling, recurring questions and escalations can create significant additional workload for team leaders, trainers and experienced agents. 

AI-supported internal tools can resolve some routine questions without involving a supervisor. They can also analyze what employees are asking about most frequently. 

Recurring questions can reveal gaps in training, unclear procedures or missing knowledge, giving operations teams information they can use to improve support during the peak. 

You can learn a lot from the questions agents are asking

Agner, another tool used in Axendi operations, analyzes internal support requests from agents. It helps identify recurring questions, areas where employees frequently need assistance and potential gaps in available knowledge. During rapid scaling, this gives operations teams visibility into where additional training, clarification or knowledge updates may be needed. 

Scale quality monitoring with interaction volumes

Peak season creates more interactions at the same time as companies are often onboarding large numbers of new agents. This makes quality monitoring particularly important — and more difficult to scale manually. 

AI-powered quality assurance analytics can analyze a much larger share of customer interactions than traditional manual sampling and identify recurring errors, compliance issues, customer complaints or areas where agents are struggling. 

QA teams can then focus their attention on the interactions and patterns that require human review, allowing quality monitoring to scale alongside contact volumes. 

You can use AI analytical solutions when volumes multiply

At Axendi, Deming applies AI-supported analytics to quality management, enabling operations to analyze customer interactions at a scale that would not be possible through manual sampling alone. This allows QA teams to identify patterns, recurring issues and interactions requiring closer review while keeping human expertise at the centre of quality management. 

Identify what is driving customer contacts

AI customer service automation can also help reduce peak-season demand by identifying patterns across large volumes of customer interactions. 

A sudden increase in questions about delayed deliveries, for example, may indicate a problem elsewhere in the customer journey: unclear communication, late tracking information or incorrect delivery expectations. 

Conversation analytics can identify recurring contact reasons and emerging issues, helping companies address their underlying causes. 

This shifts the focus from handling more enquiries to preventing avoidable enquiries from being generated in the first place. 

During peak season, when even a small process issue can quickly generate thousands of additional contacts, this can have a significant impact on customer service capacity. 

You can find out in no time why contact volumes are suddenly increasing

Tools such as Rosetta, Axendi’s text analytics solution, can analyze large volumes of written customer interactions to identify recurring topics and emerging issues. This can help operations understand not only how customers are contacting the company, but what is generating those contacts in the first place. 

Implement and test AI before peak season

AI should be part of peak-season preparation rather than introduced when volumes are already increasing. 

Automation requires tested customer journeys and escalation paths, reliable system integrations and structured knowledge. Agent-support tools need accurate information, while analytics should be configured around relevant operational and customer service indicators. 

Historical contact data provides a practical starting point. Companies can analyze previous peaks to identify: 

• the most common contact reasons,  

• repetitive and predictable interactions,  

• processes generating unnecessary contacts,  

• activities consuming the most agent time,  

• recurring questions escalated to supervisors,  

• areas where quality deteriorated as volumes increased.  

This helps determine where AI can realistically create additional capacity before the next peak begins, rather than introducing technology without a clearly defined operational use case. 

The goal is not an AI-powered peak season

I don’t think that should be the ambition. 

The goal is an operation in which customer demand and human workload do not have to grow at exactly the same rate. 

AI can automate selected interactions, give agents faster access to knowledge, reduce routine escalations to supervisors, and help QA teams prioritize the interactions that require attention. And some customer contacts can disappear completely because the underlying problem has been identified and fixed. 

You will probably still need more people during peak season, but AI can reduce how much additional human capacity is required by automating predictable interactions, supporting agents and reducing operational workload. 

This creates a more scalable model in which human capacity grows where it is genuinely needed, rather than in direct proportion to every increase in customer contact volumes.