How AI is Transforming Call Centers and Customer Service in Unexpected Ways — And Why Operative Intelligence is Leading the Charge

by / ⠀News / December 9, 2024

Artificial intelligence (AI) has become synonymous with technological progress, but in few areas has its impact been as profound as in customer service. Once relegated to basic automation tasks, AI is now taking on complex roles in call centers, reshaping how businesses interact with customers and how service operations are run. Yet, while chatbots and other AI tools promise faster and more efficient service, the reality for most companies has been quite different. Many AI implementations, particularly chatbots, have underperformed, frustrating customers rather than delighting them.

But there’s a new wave of AI innovation underway, with companies like Operative Intelligence (OI) leading the charge. Instead of simply automating responses or routing queries, OI is applying sophisticated, large language models (LLMs) to solve some of the most pervasive challenges in customer service: understanding why customers are reaching out, predicting their needs, and using that information to create actionable insights that drive better service. This transformation is not only changing the face of call centers but also providing a blueprint for how AI can elevate rather than diminish the customer experience.

The Chatbot Paradox: Why AI-Driven Service Has Stalled

AI in customer service has largely been dominated by chatbots, which promise a more streamlined experience but have fallen short. For instance, a 2024 study by Worrklife Ventures found that 69% of customers still prefer speaking to a human representative over a chatbot. AI chatbots, which are often designed to handle only basic queries, frequently frustrate users who are seeking nuanced assistance. These bots focus on the “what” of customer service—answering queries, following scripts—but not the “why,” the underlying reasons why people are contacting customer support in the first place.

The result? A degraded customer experience. From banks to airlines, companies across industries have implemented AI chatbots that miss the mark. Instead of improving satisfaction, these bots are amplifying frustrations, often leading to increased call volumes as customers ultimately seek human intervention. But there’s a deeper issue: many of these AI systems fail to flag systemic problems that could be easily resolved if caught early.

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A recent case involving a major U.S. bank (which remains unnamed) highlights this perfectly. When a glitch with two-factor authentication protocols arose, customers flooded the bank’s call centers and chatbots. While the chatbots performed their tasks and successfully helped users log in, no system was in place to identify the underlying issue or escalate it for resolution. It wasn’t until the problem persisted that engineering teams were looped in. The incident cost the bank valuable time and resources, illustrating a key flaw in today’s customer service AI systems: they lack the intelligence to identify, understand, and act on root causes.

Enter Operative Intelligence: An AI that Listens to Customers

Operative Intelligence represents a fundamental shift in how AI can be applied to customer service. Co-founded by brothers Peter and James Iansek, OI is built on their 25 years of experience working in call centers. Unlike many AI solutions that focus on automating individual tasks, OI aims to provide businesses with the intelligence they need to proactively solve customer service challenges.

“At its core, customer service is about understanding the customer, but for too long, companies have lacked the tools to do this at scale,” says Peter Iansek, CEO of Operative Intelligence. “We designed OI to not only identify the immediate problem a customer is calling about, but also to capture and analyze the ‘why’ behind the interaction.”

OI’s platform operates in the background of call centers, listening to and transcribing every call. But rather than simply generating summaries, the system goes a step further: it identifies trends, flags systemic issues, and provides actionable insights. For example, if a surge of customers is calling about a specific product, OI will recognize the pattern, quantify it, and suggest operational changes to resolve the root cause.

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This level of granular insight is a game-changer for call centers. Instead of relying on anecdotal data or time-consuming manual analysis, businesses can now access real-time intelligence about customer needs and service challenges.

Operative Intelliegence’s customers span some of the world’s largest airlines, health systems, and customer service-centric industries. One customer is Humm Group, a leading financial services firm in Australia, whose customer experiences — and operational efficiency — have improved considerably since installing OI’s solution in their customer support center.

Real-Time Analytics for Real-World Problems

One of OI’s key differentiators is its ability to provide real-time analytics. Traditional customer service metrics—such as average handle time (AHT) or first call resolution (FCR)—offer a limited view of what’s actually happening in a call center. They measure efficiency but not effectiveness. OI’s platform changes that by giving businesses a 360-degree view of customer interactions.

Take, for example, an airline dealing with a flood of refund requests due to a weather-related cancellation. In a traditional call center, agents would handle each call individually, and while some overarching data might be captured, most of the granular insights would be lost. OI’s system, however, would track these conversations in real-time, flagging the specific issues being raised, predicting what might come next, and suggesting solutions—like automating certain requests or offering targeted compensation.

For some of the world’s largest airlines, healthcare companies, and insurance providers who have adopted Operative Intelligence, the benefits are immediate. The platform helps them make better decisions, faster—whether it’s reducing customer churn, improving agent performance, or identifying areas of friction that need addressing. This capability is particularly valuable in high-volume industries like airlines and financial services, where even small improvements in customer satisfaction can lead to significant cost savings and revenue boosts.

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A Billion-Dollar Opportunity

The global customer service industry represents a $100 billion opportunity, with call centers forming the backbone of this sector. Yet despite the vast sums spent on customer service, many companies are still using outdated systems that rely on inefficient processes and underperforming technologies. OI’s solution aims to change that by becoming the “customer intelligence layer” that powers enterprise decision-making for contact centers.

“We’re helping companies unlock the potential of every human interaction they have with their customers,” says James Iansek. “That’s something most AI systems haven’t been able to do until now.”

The Future of Customer Service

As AI continues to evolve, the future of customer service is poised for a major transformation. While basic chatbots may still play a role in automating simple tasks, the real value will come from AI systems that can listen, learn, and act on the complex needs of customers. Operative Intelligence is at the forefront of this shift, offering a solution that not only improves the customer experience but also drives business efficiencies at scale.

For businesses struggling to keep up with the demands of modern customer service, the message is clear: the future lies not in automating the “what” but in understanding the “why”—and Operative Intelligence is the key to unlocking that future.

About The Author

Brianna Kamienski

Brianna Kamienski is a highly-educated marketing writer with 4 degrees from Syracuse University. With a comprehensive understanding of communication theory, she's able to craft meaningful work that conveys what clients want to say to their clients. Brianna is the proud mother of two boys, Chase and Cooper.

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