Your call is important to us: Modernizing contact center operations through AI and ML

• AI and machine learning are transforming contact center operations
• Investing in business intelligence, technology integration and workforce optimization is enabling leaders to drive operational efficiency and exceptional customer service
• Targeting people, data and ROI and value as the focal areas can enact change and help ensure a smoother transition

Contact centers represent a substantial investment for businesses, with labor accounting for up to 70% of the total operational costs. Leaders of these pivotal points in the customer experience face relentless pressure to enhance efficiency while delivering top-notch service. Increasingly they’re turning to AI and machine learning (ML) to transform their operations. Globally in 2022, the call center AI market was valued at $1.4 billion—a number that’s only growing. The market is projected to grow at a compound annual growth rate of 23% from 2023 to 2030.

In ZS’s 2024 Future of Health Report, customer awareness, trust and comfort with AI were key research topics. But the report also highlighted that AI can only do so much if individuals don’t adopt or trust it. For example, contact centers have been leveraging omnichannel interactions like chatbots to address cost savings, with 71% of companies experimenting with or implementing conversational AI. A separate study found, however, that only 16% of consumers frequently use chatbots. Over one-third never use them.

Customers find chatbots frustrating due to their inability to balance human-like interaction with efficiency, preferring to speak with human call center agents about their issues or inquiries. This gap highlights the critical need for a balanced approach that leverages AI without sacrificing the personal touch that customers value. After all, there’s something oddly comforting about hearing a real person say, “Our menu options have changed.”

In contact center operations, AI and ML can be implemented to automate and improve various aspects of the service workflow. Building off industry expertise, there are three investment categories where we believe contact center leaders can generate the greatest value for their organization:

Business intelligence. AI and ML technologies are revolutionizing performance monitoring by combining predictive and causal analytics to understand drivers of customer behavior. These advancements enable contact centers to enhance customer targeting, standardize performance metrics and identify significant value creation opportunities.

For example, a contact center for a Fortune 50 company improved customer targeting by partnering with ZS to create an AI application that predicts customers’ likelihood to respond positively to cross-sell offers. This application led to projected pretax benefits of more than $3 million annually.

Technology integration. The adoption of intelligent automation and generative AI platforms are reducing contact center workloads by automating routine tasks. These tools free up agents to tackle more complex customer engagements, lower operational costs by reducing manual efforts and enable the extraction of deeper insights into customer sentiment.

A contact center for a major airline, for example, implemented a smart robotic process automation tool to navigate various systems, including sales, reservations and baggage tracking. This tool streamlined workflows and reduced handling time for agents through automated searches for lost baggage, sending automated notifications for fare filing completion and regular bot operations for data updates.

Workforce optimization. AI and ML are transforming how contact centers manage staffing and training. Advanced analytics are being deployed to optimize staffing decisions and better predict agent readiness post-training. These capabilities better inform headcount forecasting and targeted learning intervention in a way that is more agile and responsive to market, product and technology changes. This will be the subject of the next section.

The contact center of a leading Fortune 50 health insurer was grappling with significant challenges due to outdated workforce management practices. This misalignment led to instances of overstaffing during periods of low activity and understaffing at peak times, causing both increased operational costs and lapses in meeting required service levels.

Recognizing the opportunity, the company partnered with ZS to modernize their approach to workforce management. We began with an evaluation of the existing forecasting processes to understand the root causes of inefficiencies. Subsequently, we developed an AI and ML model that integrated various data sources, such as historical call data, seasonality patterns and agent performance metrics. This model was designed to predict the necessary number of agents required to meet service levels at a specified interval.

The implementation of this AI-driven forecasting solution resulted in a significant value creation opportunity. By accurately predicting staffing needs, the contact center could ensure optimal staffing during both peak and off-peak periods. The new system stabilized the service operations and resulted in an estimated annual savings of $4-$5 million. These savings were a direct result of reduced labor waste and improved compliance with service standards.

While AI and ML promise to deliver the advantages described above, their integration is far from straightforward. These technologies demand more than just technical integration; they require a strategic overhaul tailored to the unique dynamics of each contact center. It’s a bit like trying to reach a live agent during peak hours—challenging, but incredibly satisfying once you succeed.

Focusing on these three change management aspects will facilitate the implementation of AI and ML across various aspects of the contact center organization:

• People: Engage all internal customers (from operational leaders to IT), partners and agents with tailored communication, training and support to foster a culture ready for change and technological adoption.
• Data: Establish robust data management practices ensuring high-quality, accessible and frequently updated data sources, which are crucial for effective AI and ML deployment.
• ROI and value: Adopt multiyear ROI measurement methods to justify investments, allowing enough time for analytics teams to optimize new technologies and demonstrate tangible, value-driven outcomes.

Through these considerations, contact center leaders can effectively manage the transition to AI and ML technologies, ensuring a smoother modernization journey and better alignment with business objectives.

Modernizing call center operations with AI and ML offers substantial benefits by enhancing efficiency, saving money and improving customer experiences. These technologies not only automate mundane tasks but also provide deep insights and predictive capabilities that can transform traditional call centers into dynamic, proactive service environments.

By adopting AI and ML, organizations can ensure they stay at the forefront of customer service innovation. Envision a world where customers don’t just hear “your call is very important to us” as a hollow phrase but as a promise to have their inquiries resolved efficiently. Imagine a world where customers feel important. Improved planning through intelligent systems can allow your organization to realize that promise.