Contact centers play a pivotal role in the modern business landscape, serving as the frontline of customer interactions. However, labor capacity challenges have become a significant issue for contact centers, impacting their ability to deliver exceptional customer experiences. In this blog, I’ll delve into the current issues surrounding labor capacity in contact centers and introduce a transformative service, “Human-in-the-loop AI.” This innovative approach is designed for customer experience (CX) and contact center leaders to break free from the traditional operating model, transform agents into channel-independent task masters, and empower them to excel from the word go.

Addressing current labor capacity limitations in contact centers

Most contact centers have, for many years now, operated under a centralized model where agents are assigned to specific channels such as voice, email, or chat. This approach has led to several challenges, including:

  • Inefficient resource allocation: Agents often experience idle time due to fluctuating conversation volumes, resulting in wasted resources and suboptimal productivity
  • Limited skill utilization: Agents are trained to handle specific channels, leading to underutilization of skills and lack of versatility
  • Long training periods: New agents require extensive training before they can handle customer interactions, resulting in prolonged onboarding processes and increased recruitment costs
  • High turnover rates: The repetitive nature of tasks, lack of growth opportunities, and stressful work environment contribute to high turnover rates among contact center agents

How about AI? Is AI not addressing labor capacity issues already?

AI, or more specifically, conversational AI, is on the rise in contact centers globally. However, for the majority of contact centers, conversational AI is an afterthought and is simply injected into their existing operating model. One that looks to automate customer conversations in the front-end, provide an option to speak to an agent when the AI fails, put the customer on hold, route to an agent, and, if necessary, transfer to another agent when the customer’s issue remains unresolved. Furthermore, some contact centers tend to turn to their incumbent Contact Center as a Service (CCaaS) partners to leverage their out-of-the-box conversational AI capabilities. But most CCaaS providers bolt-on conversational AI capabilities from third-party AI services, not producing the desired levels of self-serve automation and customer experiences. As a result, the traditional operating model remains in use, where conversational AI is a mere cog in the wheel, leaving behind a significant gap (read: opportunity) in addressing labor capacity challenges.

Human-in-the-Loop AI: The key technology enabler

First, let’s address what we mean by human-in-the-loop AI and why you should care. It is a concept enabled by a combination of technology and processes that seamlessly blends artificial intelligence and human expertise. Conversational AI works well, until the customer reaches a point where human assistance is needed.This is because Natural Language Understanding (NLU, a key capability of conversational AI), fails to properly understand the customer’s intent owing to factors such as accent, background noise, slang, idioms and other ambiguities. And human language is constantly changing.

Human-in-the-loop AI for understanding

In the current contact center operating model, when the AI fails, the conversation is escalated to a human agent. Here’s where human-in-the-loop AI can come in. An agent doubling up as an “AI-helper” can intervene, and in a matter of seconds, nudge the NLU and AI forward by deciphering the ambiguity in customer language. The key is to establish an interplay between AI and humans (AI-helpers). The AI-helper isn’t exposed to or directly interacting with the customer. They’re invisible to the customer and are only working to help the AI move the conversation along. This form of human-in-the-loop AI is focused on the understanding part of the customer interaction. Understanding is fundamental but has a significant impact on AI’s ability to continue the conversation with the customer, while seeking human assistance as needed. Better understanding improves accuracy, customer self-service success rate, and thus diminishes or eliminates the need to transfer to a human agent.

Human-in-the-loop for task completion

What if you could go beyond simply understanding with human-in-the-loop AI? What if you could leverage those same AI-helpers as task masters, completing tasks when the AI fails? There are a number of scenarios where the AI is not set up for success with end-to-end transactions. It could be that the AI is not integrated to the necessary back-end systems to complete a transaction (e,g., a payment processing tool), the back-end system may not have APIs that enable integration (e.g., a home-grown or legacy CRM system), or it could be that the task at hand needs human judgment (e.g., why is my bill high this month). In such scenarios, human-in-the-loop AI for task completion provides tremendous value, including –

  • Rapid resolution: Replace live agent interactions with intelligent tasks that can be completed quickly by human agents. Customers aren’t transferred to a human agent but are assisted by human agents invisibly
  • AI-driven: Conversational AI drives the conversation with the customer and will only send tasks to an agent when it needs information it can’t retrieve or it needs approval to respond.
  • : Overcome integration barriers and high external costs to automate, including back-office integrations, agent desktop platforms, and communication channels.

Addressing labor capacity challenges with human-in-the-loop AI

Human-in-the-loop AI represents a groundbreaking departure from traditional contact center practices. Here are three key takeaways that highlight the transformative nature of this solution:

1. Breakthrough from the traditional contact enter operating model

Traditional contact centers often rely on a static agent pool, leading to overstaffing during quiet periods and understaffing during peak times. Human-in-the-loop AI disrupts this model by introducing a dynamic approach to resource allocation.

2. Going from channel-specific agent pool to channel-agnostic task masters

Traditionally, contact centers have assigned agents to specific communication channels, creating silos that hinder operational efficiency and customer experience. Human-in-the-loop AI transcends these boundaries.

3. Getting agents to hit the ground running from week one

Traditional onboarding and training processes often take weeks or even months, resulting in delayed productivity and increased frustration for both agents and customers. Human-in-the-loop AI streamlines this process. It serves as a linchpin of rapid agent onboarding by providing prescriptive tasks with all the contextual information, helping agents become proficient much faster.

The challenges related to labor capacity in contact centers are real and pressing. However, the human-in-the-loop AI approach represents a revolutionary solution that addresses the limitations of the traditional contact center model.

Ananth Srinivasan is a versatile marketing professional with a journey that reflects his evolution from a contact center agent to a senior product marketing manager at Interactions, a leader in Conversational AI. His career began in a contact center, and after earning an MBA in marketing, he ventured into technology, holding roles at leading companies such as Adobe, IBM, Accenture, Infosys and [24] Ananth’s career path has seen him excel in various domains, including marketing operations, demand generation, management consulting and go-to-market strategy. In recent years, he has focused on AI solutions for CX and contact centers. 

With a global perspective gained from extensive travel across Asia, Europe and North America, and experience living and working in Canada, Ananth brings a wealth of expertise to his current role.