Artificial intelligence is steadily making its way into all aspects of life, including the contact center business, which is recognized for its receptivity to new technology. We were so enthralled by AI’s promise that we began to question if it could completely replace contact center agents. If you’re looking for that answer, check out this article on our site.
But it’s gotten to the point where AI is intruding on the sacred—brace yourself—it’s taking over Average Handling Time (AHT)! You may wonder, “How did it happen? One of the major KPIs for call and contact centers!?” That’s correct. And in a few paragraphs, I’ll clarify my viewpoint. But first, let’s review some fundamentals.
What is AHT: Definition
Average Handling Time (AHT) is one of the key performance metrics for contact centers, measuring the average time an agent spends handling a single customer inquiry. AHT includes three main components:
- Talk Time – the actual time spent communicating with the customer.
- Hold Time – the time the agent puts the customer on hold to seek additional information or transfer the call to another specialist.
- After-Call Work (ACW) – the time the agent spends post-call to document notes, update CRM information, etc.
AHT Metric: A Bit of History
The history of the Average Handling Time (AHT) metric dates back to the 1960s and 1970s, a period marked by the development of contact centers as companies began systematically measuring agent performance. Growing demand for customer service across industries pushed companies to implement metrics to assess agent efficiency, impacting both service quality and resource optimization.
The introduction of AHT into contact center management standards was also linked to advancements in technology, which enabled the automatic tracking of talk time, hold time, and after-call work. This allowed contact centers to more effectively plan workloads and manage capacity, influencing the formation of modern call center KPIs that are now widely used worldwide.
Formula for Calculating AHT
The formula to calculate AHT is as follows:
Difference between AHT and ACHT
AHT (Average Handling Time) and ACHT (Average Call Handling Time) are often used interchangeably but may have some differences depending on the company’s context. In most contact centers, AHT is considered a standard term that encompasses the time associated with any inquiry channels, including phone calls, online chats, emails, etc. ACHT typically refers only to calls, excluding other communication channels, though this distinction is not universally standardized.
Why AHT May Lose Its Relevance In The Age Of AI
I should include a disclaimer here, as the following portion is speculative. Please regard this as a hypothesis and a thought-provoking viewpoint.
The work of contact centers is getting more automated. We already have chatbots, IVR systems, autodialers, call transcription, and AI-powered analytics. Consider the next step of development: automated systems may eventually handle the majority of regular or routine questions to contact centers. So, what will be left for agents?
At the recent “Contact Centers: Best Practices” conference, some interesting cases were discussed. One involved assisting a person with disabilities to open a bank card. A contact center agent from a Ukrainian bank spent several days helping a man with severe mobility and speech impairments open a card. The call and chat time added up to nearly an hour.
The conclusion is clear: when the majority of routine inquiries are transferred to automated systems, agents will be left with complex cases—difficult problems, irate consumers, and emotive appeals. These are problems that no algorithm can address; they require not only knowledge and expertise, but also empathy, imagination, and patience. Furthermore, handling such questions may frequently exceed standard AHT.
Potential AHT Issues in the Future
In a highly probable future where AI handles routine inquiries, contact centers may face adverse effects from excessive focus on reducing Average Handling Time (AHT). One of the risks is a decline in customer service quality. If agents feel pressured to reduce the handling time for complex inquiries, they might rush through interactions, leading to superficial responses and customer dissatisfaction, especially in cases where empathy and contextual understanding are critical.
Consider another example. Elderly individuals often struggle to keep up with rapid technological changes. For a senior, contacting a bank, pension fund, or even an online store can become a challenging quest. For the support agent, this is also a test: “Click here, enter your details, wait while I transfer your call to another specialist…”
Another issue is the demotivation and burnout of agents. The constant need to quickly resolve emotionally complex issues may increase stress levels among staff, as they will not have the opportunity to give clients adequate attention. This can lead to higher turnover rates and the loss of highly qualified agents, which, in turn, will reduce the quality of customer experience and result in additional costs for recruiting and training personnel.
What Should We Do with the AHT Metric Moving Forward?
Despite everything mentioned above, Average Handling Time (AHT) remains an essential metric that holds relevance in contact center management. After all, it is necessary for calculating FTE requirements in contact centers and determining the number of agents needed to ensure an adequate level of service. However, with the rapid development of artificial intelligence and automation processes, new approaches to managing AHT are emerging. These are aimed at balancing agent productivity, query handling efficiency, and customer experience quality.
Personalized KPIs
Leading contact centers are implementing personalized performance metrics for individual agents or teams. For requests that require empathy or longer conversation times, more flexible targets can be set, while AHT remains key for standard requests. This approach allows agents to focus on delivering quality service in more complex situations.
Agent Collaboration with AI
Sometimes, reducing AHT is as simple as preparing the agent with context before interacting with a client. Intelligent IVR, chat bots, and contact center analytics are examples of AI-based tools that should provide the agent with context for each interaction. For instance, an IVR with speech recognition can accurately identify the client’s issue and route the call to the relevant department or an agent with the best skills to resolve the issue.
Creating More Effective Self-Service Tools
How can AHT be reduced while maintaining service quality? By guiding customers to a knowledge portal! Detailed FAQ sections, articles, manuals, step-by-step guides, and video tutorials all encourage customers to engage in self-service. Agents will then only need to listen carefully, identify the issue, and provide a link to a comprehensive resource that addresses the client’s request.
Evaluating AHT in the Context of Customer Experience (CX)
AHT should be evaluated alongside CSAT and First Contact Resolution (FCR). Assessing AHT in combination with satisfaction metrics helps determine when reducing handling time positively impacts the customer experience and when it might diminish it. A holistic evaluation prevents scenarios where efforts to lower AHT worsen the quality of customer interactions.
Flexible Methods for FTE Forecasting and Planning
Leading customer service centers recommend giving more attention to forecasting workloads and FTE planning while also considering the specific nature and volume of requests. For peak periods, scheduling additional groups of agents can help maintain a balance between average handling time and quality service in the contact center.
Conclusion
As they say, “we can’t put the genie back in the bottle.” Artificial Intelligence is already here, and soon, we may need to radically rethink contact center performance metrics. We may need to redefine the structure and workflows of contact centers themselves.
But one thing is certain: the future of contact centers in an AI-driven world brings new challenges and opportunities. While technology continues to streamline routine processes, the training and support of agents remain critical. In an era when AI handles standard queries, the true value lies in empathy, emotional intelligence, and adaptability.
This creates a new context for AHT. As artificial intelligence reduces time spent on typical tasks, new approaches to evaluating agent productivity must be explored. In this new paradigm, average handling time will be used alongside metrics such as FCR and CSAT, as well as flexible work models that adjust performance evaluations based on the type of inquiry.