Call centers today face a seemingly insurmountable challenge: cutting costs while improving customer experience (CX). The pressures are immense, and the stakes couldn’t be higher…businesses that fail to adapt risk becoming the next Blockbuster in a Netflix world—bankrupt and left behind by competitors that embrace innovation.
Real-time AI is the game-changer call centers need.
Unlike the AI assistance that many of us are familiar with today that operates with a lag – like chatbots that provide pre-scripted responses or customer sentiment analysis tools that generate insights after the call has ended – real-time AI delivers recommendations and assistance in 300 milliseconds or less. For example, giving agents instant, contextually accurate insights while speaking with customers or automatically updating CRMs. This is a big leap from batch-processed AI, where insights arrive post-call and offer little to no immediate benefit.
While not fully autonomous just yet, real-time AI is already showing the potential it holds for redefining customer interactions. Those not embracing AI will need to invest 2.3 times more in agents – a risk that businesses in an $92.93 billion industry can not afford to take. But what does this shift towards AI mean for the future of human agents, and how can businesses prepare for this change?
While cost pressures and rising customer expectations are critical challenges for any call center to address, it’s actually obstacles related to the workforces that have become the biggest hurdles to overcome. This makes sense given that staffing is the highest budget item (accounting for as much as 95% of call center costs), and also the #1 driver of customer satisfaction.
The most challenging aspects impacting the workforce today include agent turnover, productivity/efficiency, and labor shifts.
Call centers struggle with notoriously high agent turnover, often exceeding 30%-40% annually. The repetitive nature of the job, long hours, and low wages often drive agents to leave. Real-time AI helps alleviate these burdens by automating mundane, time-consuming tasks and empowering agents to focus on meaningful, high-value work. This leads to better job satisfaction and reduced attrition.
Every second matters in a call center. Yet, agents often spend up to 90 seconds manually inputting names, addresses, and other details during a call. These inefficiencies add up, impacting both costs and customer satisfaction. Real-time AI eliminates these bottlenecks by automating data entry, enabling agents to resolve calls faster and focus on delivering exceptional service. Think back to your own experience with a call center: how much time could be saved with instant, accurate automation?
According to major players in the call center ecosystem, companies are accelerating their workforce relocation to offshore markets. Projections show that companies will move from 5% to 30% abroad this year, with many aiming for as much as 75% next year. While this strategy reduces costs, it creates new challenges around language and cultural barriers. Real-time multilingual AI bridges these gaps, ensuring offshore agents can communicate seamlessly and deliver consistent service across geographic markets.
So, what does a future with real-time AI look like for call centers and their customers?
The transformation of call centers will center on a shift to smarter, AI-augmented systems. While the use of AI will increase, human agents will always play a role, with their function and value evolving over time.
There are two key phases of this evolution.
The first will operate as a hybrid model, where AI acts as a co-pilot to human agents. The name of the game here is productivity gains for the human agent, allowing for faster, more seamless customer interactions. Real-time AI can offer agents:
As we’ve said, this stands in sharp contrast to the asynchronous feedback and input agents get today with batch processing. Ultimately, the evolution to having real-time AI act as a co-pilot to human agents will create a smaller but more specialized workforce, where agents are empowered to deliver meaningful outcomes as conversations unfold in real-time.
Building on the foundation laid by AI-assisted human agents, the next phase involves fully autonomous AI agents. Today, AI agents are already capable of handling Tier-1 queries—routine, repetitive customer inquiries such as password resets, billing inquiries, and basic troubleshooting. These low-complexity, high-volume interactions follow predictable patterns, making them ideal for automation.
However, the real shift will happen over the next several years as AI agents become more capable of handling Tier-2 and Tier-3 queries, which require deeper contextual understanding, real-time decision-making, and problem-solving. For example, AI systems will conduct personalized product recommendations based on real-time customer behavior, predict potential service escalations before they happen, and even negotiate refunds or loyalty rewards within set business parameters.
These capabilities will be powered by a combination of text-to-speech (TTS) technology, natural language processing (NLP), and speech recognition, enabling AI agents to seamlessly interact with customers both over the phone and through chat-based channels.
But even in this second phase, humans will still be essential. There will just be fewer of them who are more focused on high-value, complex interactions. For example, consultative problem-solving for customers with highly specific needs, managing complex financial or legal cases requiring human judgment, or offering white-glove service to VIP clients. Human agents will also play an essential role in training and tuning AI models, ensuring they evolve with customer expectations and business needs.
In addition to improving agent and customer satisfaction, this two-part transformation is poised to save all major players in the complex call center ecosystem a ton of money. Gartner projects that by next year, conversational AI deployments in contact centers will reduce agent labor costs by $80 billion.
When you realize the true scale of operational demand on call center agents, with large financial institutions managing 14,000+ calls per day and multi-practice healthcare providers fielding 2,000+, these savings actually seem moderate.
The call center ecosystem is vast, spanning nimble startups building AI-powered voice agents, CCaaS and CPaaS providers, and legacy enterprises handling millions of interactions daily. Implementing real-time AI varies across organizations, but the following best practices apply broadly to ensure AI adoption enhances efficiency and customer satisfaction without disrupting operations.
AI should support, not replace, human agents by handling repetitive tasks while humans focus on high-value interactions. Clearly define AI’s role in Tier-1, Tier-2, and Tier-3 interactions, ensuring AI provides concise, relevant insights without interfering with agents’ work.
Expert tip: Secure agent buy-in through gradual adoption, training, and validation of AI outputs to foster trust in automation and promote a smoother integration.
Most call centers won’t develop their own AI infrastructure, so they must ensure that their chosen AI providers offer scalable and secure solutions. AI-driven operations require cloud-based auto-scaling, GPU management, and real-time monitoring to handle fluctuating call volumes efficiently.
Security is equally critical—businesses should look for SOC 2-compliant AI solutions, built-in PII masking, and strong data retention policies to ensure compliance and safeguard customer information. It’s equally important to have established in-house security frameworks to manage AI-driven interactions securely and prevent data exposure.
Successful AI adoption requires internal teams with skills in prompt engineering, AI guardrails, and API management to fine-tune AI responses and prevent issues like hallucinations. In our experience, 99% of success in AI-driven automation comes from effective prompt engineering. The other 1% relies on fine-tuning.
Instead of investing in costly in-house model development, leverage trusted AI providers and external tools to bridge gaps in response accuracy, contextual understanding, and adaptive learning. This allows internal teams to focus on optimizing prompts, refining AI workflows, and ensuring AI outputs remain clear, relevant, and actionable for agents and customers alike.
AI solutions must work alongside existing CRM, CCaaS, and customer engagement platforms rather than operate in isolation. Look for real-time API integrations with Salesforce, Zendesk, Genesys, and other critical systems to streamline processes. For AI-powered voice agents, real-time transcription, and automated call summaries, seamless VoIP integration is essential.
AI adoption should be incremental. Begin with Tier-1 queries—routine, high-volume interactions like password resets, billing inquiries, and appointment scheduling—before expanding to Tier-2 (context-dependent issues) and eventually Tier-3 (complex problem-solving).
Continuously monitor AI performance, gather agent feedback, and refine workflows to ensure smooth transitions and optimal collaboration between AI and human agents.
As contact centers face increasing pressures to deliver exceptional customer service while controlling costs, real-time AI emerges as a vital solution. By automating routine tasks, providing instant insights, and enhancing agent productivity, AI not only addresses key challenges but also reshapes the future of customer interactions. Embracing this technology is no longer optional but essential for businesses aiming to thrive in a rapidly evolving landscape.
Explore how Gladia's real-time AI solutions can empower your contact centers and voice agents to meet these challenges head-on by booking a call with our product experts.
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Call centers today face a seemingly insurmountable challenge: cutting costs while improving customer experience (CX). The pressures are immense, and the stakes couldn’t be higher…businesses that fail to adapt risk becoming the next Blockbuster in a Netflix world—bankrupt and left behind by competitors that embrace innovation.
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