Redefining The Customer Service Capacity Equation

Forbes

30-06-2025

Priya Vijayarajendran is the CEO of ASAPP.
Generative AI is redefining what's possible in customer service. Offering more than incremental improvements, it unlocks a fundamentally new way to scale, personalize and optimize support at every level. From absorbing massive spikes in demand to elevating the role of human agents, this technology creates the space to rethink how we serve customers and what kind of experience we want to deliver.
For decades, contact centers have operated under a cost-capacity tradeoff, trying to meet growing demand with limited resources, outdated systems and high turnover. This tradeoff often resulted in subpar customer experiences, gaps in customer service and low customer satisfaction. Generative AI offers a way out of that equation. In my experience developing generative AI for contact centers, I've seen that it can create net-new capacity by automating complete interactions with speed, accuracy and empathy.
Maintaining The Human Touch
Most companies have moved beyond asking whether to adopt generative AI. The focus now is on how to implement it without losing the human connection that defines a great brand experience. AI can be trained to embody the tone and care of a company's best agents, but it takes thoughtful design and collaboration between people and technology. Human agents aren't being replaced; they're becoming the trainers. They guide the AI, provide feedback and step in when the situation demands emotional nuance or deeper judgment.
When done right, AI helps scale the complexities that human agents can leverage. The goal isn't to separate the machine from the workforce, but to build an inclusive system where the two operate together, reinforcing each other's strengths.
Elevating The Role Of The Human Agent
One of the biggest challenges in contact centers today is churn. McKinsey reports the average annual turnover rate for call centers is roughly 60% (though, in my experience, annual turnover can be as high as 80%), which leaves teams caught in a constant cycle of hiring, onboarding and retraining new hires, driving up costs and eroding consistency.
In companies that successfully implement generative AI in their contact centers, one of the most meaningful outcomes is what it makes possible for human agents. When done correctly, gen AI allows agents to focus on complex, high-value conversations, while also training and guiding AI to reflect the company's voice, tone and brand values, instead of being stretched thin across repetitive tasks. That's a powerful evolution: from customer service as a reactive function to a more strategic, insight-driven role.
Scaling Service When Demand Surges
Whether it's five thousand or five million interactions, your generative agents will respond in real time. They adapt to sudden spikes—weather disruptions, viral campaigns or platform outages—without missing a beat. You should select a system that is designed for responsible scaling so that it knows when to bring in a human to preserve judgment and empathy where it matters most. The result: consistent SLAs, fewer bottlenecks and better customer outcomes at scale. Generative AI can allow call centers to operate faster and more reliably, regardless of the volume.
Proven Use Cases And How To Apply Them To Your Business
Generative AI is already transforming industries with high, unpredictable and urgent service demands, such as airlines, telcos, financial services and retailers. These are brands we interact with daily, and they've embraced AI to keep up with volume and to raise the bar on service quality.
Take airlines, for example. During weather disruptions, customer service centers used to be overwhelmed by millions of calls from passengers needing to check flight statuses or rebook. Today, generative agents handle that surge effortlessly, integrating with backend systems, offering real-time updates and providing rebooking options without the dreaded hold music.
Or consider a major power outage for a telco. A tenfold spike in customer inquiries can crash traditional systems. But with AI, brands are now answering questions, resolving issues and maintaining a seamless experience across millions of interactions without overwhelming their human teams.
So, how can other organizations follow suit? Start by identifying your most common and costly service bottlenecks, moments where volume spikes or resolution slows. These are ideal entry points for AI. Then, design around both automation and escalation: let AI handle the predictable, and have humans ready for nuance and complexity. And don't forget training. Your best agents can teach AI how to speak your brand's language, tone and values.
Transforming Your Contact Center With Generative AI
Once you've made the decision to integrate generative AI into your contact center, the next step is rethinking how your systems support scale, adaptability and brand consistency. For years, contact center strategies have been constrained by staffing: hiring fast enough, reducing turnover and boosting agent efficiency with training or tools. Generative AI allows you to move beyond that reactive model.
Start by identifying where legacy systems are holding you back. Use the breathing room AI creates to modernize infrastructure, connecting siloed channels, upgrading outdated platforms and automating high-volume interactions that previously consumed valuable agent time.
Next, shift your focus from workforce headcount to system performance. Ask: How well can your architecture adapt to spikes in volume? How consistently does it reflect your brand voice and values? How quickly does it learn from feedback and improve over time? These are the metrics that matter in an AI-first model.
Your agents also play a key role in the transition. Think of them not just as users, but as trainers helping train AI to handle nuanced interactions, ensuring it responds with empathy and stepping in when human judgment is needed.
Done well, this shift positions your contact center as a modern experience engine capable of delivering reliable, intelligent service no matter the complexity or scale. With self-learning systems that improve with each interaction, you're no longer chasing capacity. You're building a foundation for long-term service excellence.
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