While it is increasingly clear that the contact center of the future is likely to be an AI-led environment, the pace at which companies will arrive at this future state is far less certain. Today’s leaders are faced with the difficult choice of just how far to go with their automation plans to retain the right balance between humans and AI as they build toward this AI future.
There is a lot of noise for CEOs and COOs to sift through as they decide where to invest. Many are being inundated with solutions from AI vendors amid predictions that calls requiring human agent support will virtually disappear in the next few years.
Previous technology waves, however, show that the adoption of new tools can sometimes trip up the implementation, resulting in slower-than-expected adoption. Hurdles in connecting systems and data, and change management issues—as well as human resistance—would first need to be surmounted for such predictions to become reality.
While the power of AI cannot be ignored, leaders also need to think through the benefits of traditional value levers such as outsourcing and productivity improvements before making any immediate and fundamental shifts in their strategy.
As more companies adopt AI in their contact centers, a clearer picture is expected to emerge on the likely winners and losers in this space. The winners may be those who understand the fast-changing landscape and can make fundamental shifts in their technology and capabilities—all while keeping business problem solving and a customer-centric mindset at the core of their approach.
In this article, we explore the primary forces shaping contact centers today, drawing on McKinsey analysis and interviews with customer care leaders and solution providers to demonstrate wide-ranging views on the pace at which the AI-dominated contact center might unfold.
Two forces shaping customer care: AI and the value of human contact
The future of customer care and contact centers is complex and rapidly evolving. Two primary forces are shaping this landscape: the surge of AI and its continually expanding set of capabilities (including AI agents that can act independently of humans), and the renewed value placed on human agents as their role is reshaped by this technology.
The surge of AI in customer care
Malte Kosub, CEO of Parloa, an agentic AI company in the customer care space, predicts that AI will continue to play an ever-bigger role in customer care, given the results it is already delivering in call centers. “I believe that the number of conversations will increase by an order of magnitude over the next five years,” he says. “While, thus far, companies have tried to deflect customer conversations, AI agents can now help them build truly personalized customer relationships—at scale and with significant impact on the bottom line.”
This shift of call volumes from live channels to virtual assistants has started to transform customer care. AI-driven solutions can already solve simple transactional issues through virtual voice and chat assistants, leveraging internal and external knowledge bases to deliver personalized and continuous customer service.
Furthermore, when AI is combined with internal data and systems, AI delivers impressive returns. One leading energy company has successfully reduced its billing call volume by around 20 percent and shaved up to 60 seconds off customer authentication by integrating an AI voice assistant into its back-end call workflow. The company is now planning to scale this use case across the organization. Other customer care leaders are naturally eager to invest in this technology, too, given the promised benefits of greater efficiency and productivity in an environment that generally struggles with high agent churn and the associated costs of recruitment and training.
The upsides of the technology also extend to customer and employee experience. Gen AI’s always-on ability to handle simple queries, perform live translations, and provide personalized responses makes interactions more efficient—reducing wait times and enhancing customer satisfaction. Agents themselves are seeing the positive effects of gen AI in their day-to-day duties, especially from reduced After Call Work (ACW). AI tools can summarize issues and proposed interventions, increasing agent productivity and reducing their call times.
The enduring value of humans in the contact center
The flipside of this gen-AI-driven transformation is that human-powered contact centers remain crucial, both as a form of risk control to validate AI, and to bring humans and AI together to create powerful forms of collaborative or collective intelligence.
“Identifying the best ways for AI and humans to work together to achieve collective intelligence will become increasingly important,” says Diyi Yang, assistant professor of computer science at Stanford University, in predicting the big trends for AI in 2025.1
Of course, humans remain valued for their ability to handle complex and emotionally nuanced interactions, too. While these collaborative capabilities are in the early stages of development at present, complex requests often require the empathy and judgment that only humans can provide. In fact, our survey found that 71 percent of Gen Z respondents believe live calls are the quickest and easiest way to reach customer care and explain their issues. For baby boomers (59 years old and up), this preference is shared by 94 percent of respondents.2
In today’s highly competitive market, such insight could prove invaluable. As brands strive to differentiate through personalized and empathetic customer service, ensuring that human agents are retained and supported to provide this level of service could help businesses stand out.
Crucially, many human interactions will be in moments that matter. As customer needs and expectations continue to increase, the overall volume of customer interactions is continuing to rise along with them—and 57 percent of customer care leaders told us they expect call volumes to increase over the next one or two years.3
João Cardoso, chief innovation and digital officer at Teleperformance, a business process outsourcing (BPO) company, has seen this growth firsthand, even as other channels mature and offer customers more choice. “We have been seeing an increase in volume from most of our accounts across industries,” he says, “while big investments have gone into digital, interactive voice response (IVR), and chat channels to make it faster, simpler, and better in the last ten years.”
In other words, even if AI handles a growing share of future interactions, the absolute number of cases requiring human intervention may actually continue to grow. Many businesses are migrating their contact center operations to specialized vendors that provide a blend of AI and human support, ensuring efficient scaling while maintaining a high-quality service. This hybrid approach leverages both AI and human agents, optimizing customer care and driving meaningful interactions.
All of this means that knowing how much to leverage technology—and how much human capital to retain as AI takes on a bigger share—is not easy. As we engage with customer care leaders across various industries, two distinct perspectives are emerging: Some expect that live call interaction volumes may not decline as rapidly as many are predicting, drawing on lessons from past waves of technological change. The second point of view posits that the advent of gen AI will fundamentally change the landscape this time around.
Scenario one: Human interactions remain high for the next three to five years
A decade ago, the primary reasons for calls to telecommunications companies were for directory assistance, and for banks, checking account balances. These types of inquiries have virtually disappeared, yet call volumes have steadily increased due to new and different customer needs.
While digital interactions have grown faster than assisted ones (6 percent annually since 2010, according to our research), human-to-human interactions have still grown 2 percent annually over that time frame—often because of poorly designed digital experiences or because more complex support requirements drive customers into traditional channels. To test how assisted volumes may unfold over the next three to five years, McKinsey modeled a base case fast-adoption and slow-adoption scenario—with the fast-adoption scenario showing a 2 percent reduction in call volumes versus continued growth of 2 percent in the slow-adoption scenario.
Peter Meier van Esch, senior vice president of operational excellence and innovation at Deutsche Telekom, believes some measure of pragmatism must be applied when harnessing new technology: “Generative AI will greatly enhance our contact center customer experience and efficiency in the upcoming years. Yet service complexity in telecommunications is higher than in most other industries. As society adapts to bot interactions, we must also recognize the unique value of human connections, which are vital for loyalty and a premium brand experience.
“That’s why, even with advanced AI, we expect workforce efficiencies at around 30 percent over the coming two to three years. We’re carefully balancing improved efficiency with the human touch our customers still deeply appreciate.”
There are a number of factors driving the continued importance of human-to-human interactions and a potentially slower adoption of gen AI.
Difficulty integrating the latest technologies into workflows. The introduction of new technologies takes time to perfect. The last five years have been particularly disruptive for the contact center space, with organizations experimenting with different technology solutions, often without a clear goal or guiding vision. Now organizations are learning which solutions genuinely enhance customer and employee experiences. This shift toward strategic deployment of solutions that directly impact experience is beginning to take root but may take time to proliferate.
Slow decision-making by enterprise companies. Decision-making around new technologies, especially ones that apply organization-wide, are often slow, needing buy-in from multiple stakeholders. This, coupled with many instances of dated or legacy technology, makes decision-making harder for executives—for example, whether to prioritize new and advanced technology developments or upgrade the existing technology ecosystem.
Technology challenges in addressing demand from larger channels. Voice remains the dominant live interaction channel, followed by text channels such as email and chat. Gen AI is improving in asynchronous channels (chat, email) but struggles with voice due to latency issues. Future technology advancements are likely to resolve these challenges, but this remains a limitation for now.
Slow customer adoption of new technologies and channels. With rapid and disruptive technological advances happening worldwide, customers may find themselves confused and overwhelmed. In such situations, they might prefer to pick up the phone, seeking empathetic conversations with real humans to resolve their queries.
Emergence of personal AI assistants. The use of personal AI assistants by customers to make calls to customer care on their behalf is an emerging trend that could prove influential in time. Personal AI assistants can call multiple times and are unaffected by human limitations, such as high wait times or talk times. While not yet widespread, these AI assistants could significantly alter the dynamics of customer service interaction, and many organizations are not fully prepared for this shift.
Parloa’s Kosub explains the potential impact: “Personal AI assistants will solve problems independently on our behalf—for example, by calling companies to update our information. Imagine moving to a new home; your AI assistant could automatically call your bank or insurance provider to update your address, saving you time and hassle. This will create a surge in conversation volume that can’t be handled just by humans. Companies that can’t deploy AI agents will fall behind rapidly.”
Scenario two: Gen AI takes center stage quickly
As customer needs change, the pace of development in gen AI technology including agentic AI, autonomous agents, and concierge-level services for customers, could (literally) change the face of customer care. And, as AI adoption accelerates across industries, new businesses, products, and services will drive contact volumes further still and in new ways—as seen following the widescale adoption of the internet, smartphones, and social media.
Already, human-like interactions from decision-making bots can perform simple tasks and orchestrate across technologies to resolve issues. Hyperscalers and boutique virtual assistant companies are counting on capabilities like agentic AI to usher in a true step change in traditional call volume replacement. An agentic AI concierge could independently address a significant number of customer inquiries in the future, with the total potential reduction differing across organizations based on: how quickly and how aggressively companies want to migrate their volume to a digital-only AI solution; the willingness of customers to adopt it as an alternative channel; and how the role of human agents is reimagined.
There are, of course, practical obstacles that need to be overcome to enable this future state, including issues around system integration, data quality, and the lack of documented processes and knowledge in many contact centers. Next-gen AI models would also need to address risk hurdles and prove they are better than humans at making judgment calls or avoiding mistakes.
If such barriers are dismantled, AI dominance in the contact center could happen quickly, in line with McKinsey’s fast-adoption scenario, with virtual assistants taking on a greater share of interactions, and assisted volumes decreasing by 2 percent as a result. For this to happen, however, companies would need to overcome issues around integrating disparate systems and data, addressing regulatory and internal risk challenges, and internal change management. This is not yet happening on a broad enough scale to suggest the fast-adoption scenario will, in fact, unfold, but the landscape is ever-changing.
Those who can successfully manage these challenges may be able to get ahead of their competitors—for example, by leveraging AI agents to innovate customer service through the benefits of intrasystem connectivity. AI agents can connect many systems automatically, pulling information from scheduling databases to update a call-routing strategy in real time, or reading weather and traffic data to update customer bookings—creating more intelligent, informed, and proactive customer service overall.
And, of course, as gen AI continues to advance, and the technology becomes cheaper (and its value clearer), more companies may be able to overcome the investment hurdle to deployment.
There are two key reasons why this faster-adoption scenario might unfold:
The majority of transactions are still simple. Our analysis of millions of interactions across more than 30 organizations shows that while call complexity has increased, 50 to 60 percent of customer interactions remain transactional, despite significant efforts to eliminate them. For example, at a leading bank in Europe, relatively transactional queries (such as recent transactions and bill payments) still account for around 50 percent of total call volumes. Similarly, around 40 percent of the call reasons at a telco company in North America remain transactional (for example, refund, current plan, or new device inquiries).
Improved customer adoption. Customers are increasingly familiar with gen AI technology, using AI assistants both at home and at work, whether for writing emails or performing more complex tasks. This familiarity could lead to better acceptance and smoother integration of AI-driven customer service solutions—or force more companies to leverage AI to meet customer demand for choice. Full adoption, however, may not happen organically on its own. Companies may need to actively support some customers toward adoption, understanding what is required to do so, and then rapidly building out a plan to execute on these insights.
Gen AI has already proven very powerful in multiple use cases and continues to demonstrate its worth in emerging contact center applications. With human-like automated assistance, contact centers are moving beyond traditional IVR systems, and using advanced AI assistants to provide consistent, human-like experiences across all channels, enhancing customer satisfaction and engagement.
Simulation-led agent onboarding and training are also helping address the perennial issue of high agent churn. By simulating complex customer scenarios, agents can now learn how to handle these situations effectively before encountering them in real life. From our client engagements, we are seeing improved onboarding effectiveness and 20 to 30 percent reductions in agent time to proficiency. That said, while such improvements are of real value to businesses today, the advantages of quicker onboarding may diminish over time in a fast-adoption scenario as baseline volumes decrease and fewer human agents are needed.
Gadi Shamia, CEO of Replicant, a conversational AI solution provider, expects this faster-transformation scenario to unfold—even if call volumes continue to rise—given the results companies are already seeing. “Implementing AI agents into our customers’ contact centers has driven a 50 percent reduction in cost per call,” he says, “while simultaneously increasing customer satisfaction scores.”
Shamia anticipates dramatic changes in how companies handle volumes in the future: “Over the next two to three years, we estimate that AI-driven automation will allow companies to have 40 to 50 percent fewer agents while still handling 20 to 30 percent more calls than they are today. Our vision of an AI-first contact center, where AI agents handle the majority of conversations and fewer, better trained and better paid human agents support only the most complex, is quickly becoming a reality.”
Should Shamia’s prediction materialize, businesses may need to rethink the role contact centers can play in unlocking growth, as the nature of customer care itself changes.
Moving forward, despite an uncertain future
Many organizations now find themselves at a crossroads in the debate between AI-driven and human-powered customer care. For many, a combination of the two may be the right option. Few organizations have been able to achieve a 10 to 15 percent or more year-over-year interaction decline since 2022. Those that have were able to do so by solving data and system integration issues, fixing broken processes, and addressing risk-related concerns that AI cannot do by itself.
Organizations embarking on an AI transformation of their contact center can act now to ensure their technology adoption delivers the expected results for their business and a better experience for customers. Some efforts will be a continuation of those already underway, while others will require new focus and energy.
Set a bold and cross-cutting AI ambition grounded in AI-derived insights. Any successful transformation begins with a clear vision behind which cross-functional teams can rally, and the rollout of AI is no different. The difference now is that organizations need to create a much bolder vision for transformation than they have today—one that is rooted in measurable targets and becomes a catalyst to move past promising pilots or the limited-scale deployments that hamper many organizations. Such a vision needs to think beyond technology alone and incorporate levers such as policy choices, organizational culture, and change management. AI can also be leveraged to support quicker discovery of customer and organizational pain points to inform this ambition and strategy.
Design an AI-ready operating model that challenges existing processes. The right operating model is crucial for ensuring impact from any technology investment, and AI solution deployment demands a dismantling of silos. AI transformation should not be treated as another IT project, but as something that impacts customers directly and as such, customer needs should be taken into account. Such a radical rethink of the operating model could entail changing processes, updating existing technology infrastructure, improving data quality, and supporting better collaboration between IT, customer service, data analytics, and product development teams.
Rethink and realign talent with an emphasis on problem-solving skills and empathy. With evolving technology, regulatory environments, and customer preferences, human agents may need to be hired or upskilled correctly to become “advisers.” These people will need to be empathetic, as well as proficient at using new gen AI solutions, solving complex queries that gen AI cannot easily answer.
Refocus on distinctive human qualities and customer needs. As technology expands deeper into customer care, the importance of human connection cannot be forgotten. To get the balance right, leaders could begin by asking what will make their customer care distinct in the future from both a technology and human touch standpoint. Engaging customers to embrace their vision of customer care will be important, too.
Keep evolving regulation in mind right from the start. Amid the excitement of implementing AI, organizations need to remain focused on legal requirements, especially when working with personal data. Oversight in this area can render solutions ineffective after development, so thinking about regulation early on is essential.
In all of this, of course, companies will need to be alert to the new kinds of risks that may arise in an AI-powered world, including more sophisticated scams targeting them and their customers. As cybercriminals leverage gen AI to create more convincing phishing attacks, or social engineering using deep fakes, companies may need to step up how they support their customers in staying safe—and keep current with any changing legislation designed to protect consumers.4
The future of customer care and contact centers is an AI one—though whether this will be a slower evolution or fast revolution remains unclear. What is clear, however, is that companies should prepare for a world in which AI and human assistants work side by side.
The path ahead, for now, may lie in embracing a balanced, hybrid approach that leverages the strengths of both AI and human agents, transforming contact centers from cost centers into strategic enablers of growth and customer satisfaction.
