The rise of AI in customer service has led to bold predictions about fully automated call centers, yet human agents remain as essential as ever in enterprise call centers. In fact, the industry is growing: roughly 2.86 million people work in U.S. contact centers today, and many companies are increasing (not cutting) their customer support staff. Recent surveys show 69% of contact center leaders expect to hire more agents in the next year, and 73% plan to boost call center budgets in that timeframe. Far from making humans obsolete, AI is being integrated as a powerful augmentation tool.
Since this is an obvious choice for AI, I figured we would take a look at the current state of enterprise call center staffing, what it means for digital transformation strategies, and how AI is enhancing (not eliminating) the role of human agents with recent trends and examples. The tone is research-backed and forward-looking, focusing on opportunity and strategic realignment rather than job loss.
Human Agents Remain Central in Enterprise Call Centers
Despite the buzz around chatbots and virtual agents, most customer interactions still rely on human representatives. Customers often prefer it that way; surveys confirm that people still favor talking to a human agent for both urgent and non-urgent issues.
Indeed, instead of decreasing call volumes, the proliferation of self-service tools has coincided with higher demand for human support. 61% of call center leaders report that call volumes have increased since the 2020–2021 pandemic era despite more chatbots and self-service channels. In other words, even as basic inquiries get deflected to FAQs and bots, the complex or escalated issues reaching human agents are growing, keeping call center teams as busy as ever.
AI adoption in call centers is still in early stages. According to a KPMG survey released in April 2025, 61% of large enterprises (>$1 billion revenue) have begun using AI “agents” in their call centers, typically in pilot programs or limited deployments.
This statistic, coming from KPMG’s Q1 2025 AI Pulse survey of 130 U.S. business leaders, highlights that among big companies, a majority are at least experimenting with AI-driven virtual agents to handle customer interactions.
However, full-scale implementations are rare; only 11% of those firms have deployed AI agents at scale in customer service. The vast majority are still test-drivingthese technologies.
This context is important: the “61%” figure applies to large enterprises exploring AI; it does not mean most companies have replaced their call center staff. On the contrary, it underscores that nearly 9 in 10 large firms have not fully automated their call centers, relying primarily on humans while they trial AI.
Broader industry data also show that human call agents remain the backbone of customer support operations. Gartner estimates that in 2022 only about 1.8% of customer service interactions were handled by AI, and even with rapid growth, that share is projected to reach roughly 10% by 2026.
Said differently, over 90%of interactions will still involve live agents for the foreseeable future.
It’s clear that in 2024–2025, the typical enterprise contact center is still a human-driven operation, with AI playing a supportive role in a minority of conversations.
As a further sign of health, enterprise call center headcounts and workloads continue to grow. The U.S. Bureau of Labor Statistics counts around 2.8–3 million customer service and contact center employees nationally.
Rather than shrinking this workforce, many companies are expanding it, reflecting not only rising service demand but also the recognition that human-centric service remains critical in the age of AI.
Implications for Digital Transformation and AI Strategy
The enduring prevalence of human agents carries an important message for digital transformation leaders: AI in customer service is not a simple “rip-and-replace” of people, but a strategic augmentation of the workforce.
Forward-looking organizations are approaching AI implementation with a balanced mindset. In KPMG’s study, 76% of executives said they expect AI to automate certain tasks within call center roles but not to fully replace those roles.
The dominant view is that AI will take over repetitive workloads, allowing humans to focus on higher-value work, rather than wholesale eliminating human jobs. This aligns with the prevailing strategy of using AI to assist agents and streamline operations, without losing the human empathy, judgment and relationship-building that define great customer experience.
One key implication is that digital transformation initiatives must integrate AI in a human-centric way. Customer experience leaders warn that going “all in” on automation at the expense of human touch can backfire.
In fact, 64% of customers say they would prefer companies did not rely exclusively on AI for customer support. Customers want quick answers andto feel heard and understood, a combination of speed and empathy that typically comes from blending AI efficiency with human care.
Thus, a smart AI strategy treats automation as a means to improve responsiveness and consistency, while preserving human-led resolution for complex or sensitive issues.
This means redesigning workflows so that AI handles the front-line FAQs and routine tasks, then seamlessly hands off to human agents when a conversation requires a personal touch.
Many companies already use this “tiered” approach; for example, 71% of businesses route initial contacts through digital self-service channels and reserve phone agents for escalations or complex problems. Such models ensure that AI enhances service without compromising quality.
Focusing on augmentation also means rethinking how success is measured in transformation. Instead of measuring AI success solely by headcount reduction, leading organizations measure it in terms of improved customer satisfaction, faster issue resolution, and agent productivity gains.
Notably, early results are encouraging on these fronts. One recent industry analysis found that introducing conversational AI agents cut the cost per call by 50% while simultaneously increasing customer satisfaction scoresfor those companies.
In other words, AI-driven automation can remove friction and cost without hurting (and indeed improving) the customer’s experience, a win-win if implemented correctly. However, these gains don’t happen automatically.
They require robust integration and change management. McKinsey observes that few organizations have achieved large reductions in contact center workload to date; the ones that did succeeded by fixing data integration issues, broken processes, and risk concerns that “AI cannot do by itself.” The lesson is clear: AI implementation must be accompanied by broader process improvement and training efforts. Technology alone won’t transform a call center; it must go hand-in-hand with reengineering workflows and preparing the workforce.
Crucially, workforce preparationis now a top priority in digital transformation roadmaps. Companies recognize that their call center staff need new skills to thrive in AI-enabled environments.
In KPMG’s survey, more than 60% of executives anticipated challenges in training employees to work effectively with AI agentsdue to the complexity of the new systems.
This underscores that investing in people is as important as investing in technology. Successful digital transformation for customer service involves upskilling agents to use AI tools (like AI-driven knowledge bases, real-time “agent assist” prompts, and analytics dashboards) and fostering trust in AI outputs.
It also means redefining roles; for instance, some agents may evolve into “AI supervisors” or knowledge managers who monitor and refine AI interactions, while others become specialists in handling the nuanced cases AI can’t handle.
The bottom line is that human capital strategy and AI strategy are inseparable in the contact center of the future. Leadership must communicate a vision where AI elevates the role of customer service teams, enabling them to deliver better service rather than rendering them redundant.
Trends and Examples
Over the past year, we’ve seen an explosion of new AI tools designed to support call center agents and improve customer experience. These deployments make it clear that AI’s role is largely assistive.
Generative AI “co-pilots” and chatbots are being woven into call center platforms to handle routine inquiries, provide guidance to agents, and automate after-call work. Gartner analysts predict that by the end of 2025, 80% of customer service and support organizations will use generative AI in some form to boost customer experience and agent productivity.
The emphasis here is on productivity; AI is being embraced as a tool to make agents faster and more effective, rather than a one-for-one replacement.
Agent Assistance & Coaching
Modern contact center AI can listen to calls or chats in real time and help human agents on the fly. For instance, AI systems now automatically transcribe and summarize calls, so agents don’t have to waste time on after-call reports. AI can also suggest answers or next best actions based on context, ensuring even junior agents respond with expert-level information. On the quality side, AI tools can monitor 100% of interactions for compliance and sentiment, something impossible to do manually. This is already improving operations: one CTO notes that AI can review and score every call for quality assurance, freeing managers to focus on coaching their teams instead of listening to random samples. AI is acting like a real-time assistant and trainer, handling the grunt work and analysis, so human agents and supervisors can concentrate on solving customer problems and building relationships.
Hybrid Human-AI Service Teams
Companies are deploying AI virtual agents to handle simple customer requests end-to-end, while tightly integrating them with human support for backup. A prime example is Virgin Money UK’s “Redi” virtual banking assistant, launched in 2023 and expanded in 2024. Redi is an in-app chatbot that has already handled over one million customer conversationsfor routine tasks like password resets and account queries.
Importantly, Virgin Money explicitly positions Redi as augmenting the knowledge and support of their customer service teams, not replacing them. Behind the scenes, Redi connects to live agents and internal systems when needed. It can resolve a large volume of simple inquiries 24/7, and also assist human agents by pre-fetching information and even automating parts of fraud detection workflows. The result is a “hybrid” model: the AI handles the frequent, low-complexity queries, while human agents are freed up to focus on more sensitive or complex cases that truly need a person’s touch. Virgin Money’s case illustrates how AI can act as a force-multiplier for an enterprise, improving speed and scalability, while humans handle the exceptions and high-value interactions.
Faster Training and Onboarding
Some contact centers are leveraging AI to accelerate agent training and proficiency. AI-driven simulators and coaching tools can reduce the time it takes for new agents to get up to speed on handling calls. For example, by analyzing past interactions, AI can create virtual role-play scenarios or provide real-time feedback to trainees. McKinsey reports that companies using AI in training have seen 20–30% reductions in the time required for new agents to reach proficiency. This means a more skilled workforce faster, again highlighting augmentation: AI helps build human capability.
Customer Self-Service with Smooth Escalation
Advances in AI are also making self-service more effective, which indirectly augments agents by offloading repetitive queries. AI-powered IVRs and chatbots can handle many queries end-to-end, but a critical design focus is ensuring that when the AI can’t help, the transition to a human agent is seamless. For example, newer AI chat systems automatically pass the full context and history of the self-service interaction to the live agent upon escalation. This avoids customers having to repeat information and enables the agent to jump straight to problem-solving. The strategy is to let AI serve the customer instantly for known issues, but never let the customer feel “stuck” with a bot. Notably, 61% of customers say they’d rather use self-service for simple issues, as long as it’s easy to reach a person for complex ones. Companies are capitalizing on this by offering AI self-service as a first step, which improves customer satisfaction and reduces unnecessary load on human agents.
These trends demonstrate a common theme: AI is taking over the drudgery of call center work, the repetitive queries, data entry, post-call notes, and basic troubleshooting, thereby empowering human agents to excel at what they do best.
Instead of viewing AI as a rival to humans, leading organizations see it as a team-mate that never sleeps, handles rote tasks tirelessly, and provides insights at superhuman speed. The human agents, in turn, can devote their time to empathy, complex problem-solving, and building rapport with customers, which are the aspects of service that machines can’t replicate. As one industry leader put it, the goal is an “AI-first contact center where AI agents handle the majority of conversations and fewer, better-trained human agents support only the most complex” issues, while delivering even better outcomes. We may not be fully there yet, but every incremental deployment of AI is moving contact centers in this direction of blended AI/human workflows.
Strategic Opportunities to Rethink Call Center Operations
Given this landscape, enterprise leaders have a prime opportunity to rethink their call center operating models for the digital age. Rather than framing it as a choice between AI or people, the winning strategy is AI and people, each doing what they excel at. Here are several strategic moves and considerations for leaders looking to realign their call center operations.
Automate Tier-1 Support and Routine Tasks
Identify the common, low-complexity inquiries that consume a large share of agent time (password resets, order status checks, appointment scheduling, balance inquiries, etc.) and deploy AI solutions to handle these at scale. Modern chatbots and voice bots can resolve many of these questions instantly, 24/7, reducing wait times for customers. This “tier-1 automation” not only cuts operational costs, it also frees human agents from the repetitive Q&Aso that your team can focus on more complex customer needs.
For example, telecom companies and banks have used AI chatbots to deflect simple calls and achieved significant reductions in live call volume. The key is to integrate these bots with your live support; when a bot reaches its limit or the customer requests a person, ensure a smooth handoff to a human agent with full context of the issue. Done right, tier-1 automation improves efficiency and customer satisfaction by providing instant help on basics.
Double Down on Human Agents for Escalations and Complex Cases
With AI handling the easy stuff, reposition your human agents as specialists for high-value interactions. This means recalibrating hiring and training profiles to emphasize problem-solving ability, emotional intelligence, and technical expertise. As routine tasks are peeled off, agents will be dealing mostly with complicated cases, upset customers, or unique situations, the moments that truly impact loyalty. Enterprise leaders should cultivate agents to be consultative “advisors” who can troubleshoot complex issues and empathize with customers. This might involve giving agents deeper training in product knowledge or soft skills like de-escalation and active listening. It may also involve empowering agents with more decision-making authority to swiftly handle exceptions or offer creative solutions (since AI will handle adherence to script for standard scenarios). Ultimately, the human touch becomes even more critical for differentiation when AI takes over transactional contacts. Leaders should recognize this and invest in their people accordingly, the goal is a smaller but more skilled frontline team that can deliver white-glove service when it matters most.
Leverage AI for Analytics and Continuous Improvement
Another strategic use of AI in call centers is turning the wealth of interaction data into actionable insights. AI-driven speech and text analytics can parse thousands of calls/chats to identify trends, common customer complaints, reasons for repeat calls, product feedback, and so on. This presents an opportunity for leaders to improve upstream products and policies, reducing contact drivers in the first place.
It also helps pinpoint training needs for agents by spotting where calls go off-script or where sentiment drops. Modern AI platforms can even detect customer emotion or stress in real time and alert a supervisor for intervention. By embracing these capabilities, call center operations can evolve from reactive cost centers to proactive insight centers.
For example, if AI analysis shows that 30% of calls are about a confusing billing statement, the company can fix the statement and eliminate those calls, saving everyone time. Encourage your team to treat AI as a continual feedback engine. Combine AI analytics with human judgment to iterate on support content, bot responses, and agent training. In doing so, you create a cycle of continuous improvement, where the call center gets smarter and more efficient over time. This strategic realignment, using AI not just to handle interactions but to learn from them, will differentiate enterprises that truly transform their customer experience.
In all these initiatives, communication and mindset are key. Leaders should set a tone of optimism and opportunity around AI in the organization. Frontline employees need to hear that AI is here to help them shine, not replace them. Many leading companies openly communicate that the purpose of automation is to remove drudgery and enable more meaningful work for agents. This helps overcome resistance and fear.
It’s notable that over two-thirds of executives are already training their workforce in generative AI skills and even actively hiring new talent to support AI efforts. The forward-thinking enterprise views AI fluency as a competitive advantage at all levels, from the contact center to the boardroom.
A Human-Centered AI Future for Customer Service
Enterprise call centers in 2025 stand at a transformational crossroads. The data shows a clear reality: human agents are still the heart of customer service, and they will be for the foreseeable future, even as AI capabilities accelerate. Digital transformation leaders should plan for a hybrid future, one where AI and humans work in tandem to deliver superior service.
The organizations that thrive will be those that embrace AI to augment their people, not as a blunt instrument to cut headcount. By automating what machines do well and elevating what humans do best, companies can achieve the holy grail of customer operations: higher efficiency and higher customer satisfaction.
Early movers are already reaping benefits through this augmented approach, faster response times, improved consistency, richer customer insights, all while maintaining the empathetic human touch that customers value.
On the other hand, companies that misread the tea leaves and try to replace agents wholesale with bots risk alienating customers and staff alike, as well as leaving value on the table. The most successful strategy is to view AI as a catalyst for reimagining your service model: streamline workflows, empower your talent, and redesign the customer journey around a seamless blend of automation and personal service.
In summary, the call center of the future is not a dark room of servers handling inquiries with no humans in sight. Rather, it’s a dynamic human-AI collaboration: virtual agents addressing routine needs in seconds, and human experts building relationships and solving complex issues with help from AI insights.
Enterprises that orient their strategies around this principle, augmentation over elimination, will lead in customer experience, operational agility, and employee engagement. As we move forward, the call center will not be a casualty of AI; it will be one of the greatest beneficiaries, transformed into a smarter, more flexible, and more human-centric function than ever before.