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Ecommerce teams are under unprecedented pressure from C-Suites to implement AI into customer service to both cut costs and automate predictable customer interactions. As brands face rising order volumes, shrinking margins, and customers who expect instant answers, AI shows potential to alleviate numerous challenges.
Gartner predicts that by 2029, AI systems could autonomously resolve up to 80% of common service issues. Yet that future remains unevenly realized today. The divide between AI’s potential and its actual deployment becomes especially glaring during the holiday period, when customer expectations spike and operational weaknesses become impossible to hide.
My organization recently analyzed more than 10 million customer service interactions, comparing the peak holiday season surge versus the rest of the year. During the holiday shopping period, customer service agents handle an additional 22% of customer sessions per week, and volume is only the tip of the iceberg.
As eCommerce Volume Climbs, Operations Start to Buckle
As customer sessions per representative increase, it’s not surprising that customer service agents need to become more efficient in their interactions and reduce their Average Handle Time (AHT) per ticket.
Composing the answer to customer queries is often the most significant part of AHT time, alongside reading the customer’s initial message and researching in other systems, such as ERP. So, perhaps not surprisingly, the time spent composing responses drops by 4% during the holiday season as customer service agents rush to handle the surge in support requests.
More revealing, though, is the shift in how agents work. Their overall time thinking (not actually writing) decreases by 17% during the holiday period, and their time typing increases by 14%. Customer service agents turn their attention to responding during the holidays, and a lot of the service items they are dealing with are not unique. There isn’t a need to find unique answers to tickets, so time shifts to writing responses for more customers. For instance, among retail and e-commerce agents, more than one-fourth of their customer interactions during the holiday period involve delivery status.
With very standard tickets, one would likely assume that the use of ‘snippets’ – predefined written responses that agents can use to reply quickly and accurately – would likely increase during this festive period. Instead, our data shows the opposite: snippets per 100 sessions drop by 27% during peak periods. Under pressure, agents abandon templates and revert to manual triage mode. This shift erodes consistency, slows response times, and amplifies cognitive strain at the worst possible moment.
Cognitive Overload Shows Up in Customer-Facing Errors
People talk about customer service like it’s a writing job. It’s not. It’s a system of micro-decisions made at high speed. Every message, even the simple ones, forces agents to decide: What is the customer actually asking? Which policy applies? Does this need escalation? What’s the right tone? Is there risk here? Is this urgent or just loud?
However, during peak periods, as overwhelmed agents end up making faster decisions on their own, typing more frantically, relying less on templates, fatigue shows up in writing interactions.
Across our dataset, “thans” appeared more than 112,000 times, “Youve” surfaced over 31,000 times, and even simple words like “the” frequently degraded into “th.” These are not careless errors; they are early signs of cognitive overload. In ecommerce, where brand perception and trust are highly influenced by the tone and clarity of service interactions, this level of strain has direct commercial implications.
Despite the errors, the good news is that e-commerce representatives are used to typing fast. In fact, e-commerce agents type 63% faster than Professional Services agents (235 vs 144 CPM). This isn’t about individual typing skill; it reflects the nature of inquiries. Even in non-peak seasons, the industry is high-volume and repetitive, whereas in an industry like professional services, situations are low volume, complex, and unique. Which leads us to the real potential for AI to help here.
AI Could Automate Nearly Half of All Customer Responses, But Adoption Lags
Even if it’s not truly agentic AI support, AI-powered writing assistance or autocomplete capabilities for customer service representatives can be extremely helpful in automating responses and cutting response times. In fact, Typewise analysis found that 46% of text responses are 1:1 predictable by AI.
Furthermore, when ecommerce teams did adopt AI-based predictive writing and response assistance in our dataset, the impact is immediate. Agents reduce typing time by as much as 35%, improve consistency, and regain more than a full day of productive time per agent per month! These gains are not theoretical future improvements; they exist today and require far less implementation effort than most leaders assume.
The reason AI adoption remains inconsistent is not due to the technology’s maturity. In many cases, it reflects organizational hesitation, fragmented tool stacks, legacy workflows, or concerns about disrupting existing processes during peak seasons.
But the opportunity for ecommerce is not to replace human empathy or nuanced problem-solving areas where human agents remain essential. Instead, AI should eliminate the mechanical tasks like repetitive written responses, which consume the bulk of agents’ cognitive energy, allowing them to remain focused on resolution, escalation, and relationship-building. The right AI systems should help ecommerce brands maintain real-time service even during surges, enforce structured communication when snippet usage declines, detect fatigue indicators before mistakes reach the customer, and integrate into existing help desks rather than requiring behavioral overhauls.
Ultimately, the holiday season is not the source of ecommerce customer service inefficiency; it is the spotlight that reveals inefficiencies that persist year-round. For ecommerce leaders struggling to keep pace with customer service demands this holiday season and already planning for the next holiday cycle, the right question is not “What will AI be able to do someday?” but rather “Why aren’t we using the AI that’s already available, especially when customer expectations, and the stakes for brand loyalty, have never been higher?”