Customer care is being redefined as an intelligent, orchestrated system where AI resolves matters while humans elevate relationships and experiences. This trend and the forces behind it were discussed by 30+ customer operations executives from leading businesses who met in Düsseldorf in July for the McKinsey European Customer Operations and Digital CX Roundtable.
Redefinition of customer care
Priorities for customer care professionals have converged over the last decade. Customer satisfaction remains the number-one priority, but improving operations, implementing the right tech, and generating revenue are all closing the gap. The pace of change is accelerating, and new forces are helping AI scale faster, raising the bar on both tech and human performance.
For example, with processing power rising, at ever cheaper cost, specialized AI chips are 1,000 times faster and more efficient than ten years ago. Customer and employee familiarity with AI is growing, with the use of chatbots rising steadily; interactions with the most popular bots number more than 1.5 billion requests per day.
With operational and investor pressure fueling AI demand (40 percent of S&P 500 companies now reference AI in earnings calls, up from 23 percent in 2023), customer care and the middle office are the AI beachhead where the next chapter of AI in business is being written. Some 35 percent of organizations plan to automate over 60 percent of inbound inquiries by 2028, while 62 percent expect authentication and call summaries to eventually be fully automated.
At the heart of this trend is the shift from measurement of operational outcomes focused on efficiency and costs, to measuring success by experience and outcome-based KPIs like resolution quality and customer satisfaction. In this world, rules-based bots that handle predefined tasks in isolation are replaced by intelligent orchestrators that connect signals across teams, systems, and moments to drive real-time decisions. AI in customer care—provided it is implemented thoughtfully and strategically—is potentially transformative, but there are still hard questions about how and where to get it started.
The importance of robust data infrastructure
Investing in robust data infrastructure is crucial for the success of AI initiatives. High-quality data ensures that AI systems can operate effectively and provide accurate, reliable outputs. Most organizations, however, are underinvested in AI, lack a clear road map, remain stuck in pilots, and face widening skill gaps on the front line. Four in five businesses allocate less than 10 percent of their overall customer care budget to AI. Half are stuck in pilot mode, 80 percent feel uncomfortable about running end-to-end operations, and 35 percent lack a clear AI road map and use-case hierarchy.
The difference between organizations leading and lagging in customer care is clear, with the top 10 percent being AI-enabled, data-driven, and digitally fluent, driving transformation and value. Banking, technology, and telecommunications appear to be the sectors furthest along in embedding AI and scaling modern customer care models. Meanwhile, the bottom 30 percent maintain consistent service operations with limited innovation. They rely on standardized training, manual processes, and legacy systems with limited channel integration or automation.
At the Düsseldorf meeting, while there was agreement that AI will be foundational to the future of customer care, there is still uncertainty and debate about the total value AI can deliver, how much should be spent on AI, what a realistic duration to scale looks like, whether customers will ever fully adopt AI, and whether efforts to transform will be sustainable across customer care. Again, leaders in the area are separating from the pack by scaling AI across customer journeys and experience. They already see more digital queries, automate more end-to-end interactions, and are more likely to treat the contact center as a growth engine.
The “hybrid workforce”
Organizations need to adopt a strategic approach to AI implementation to stay competitive and adapt to changing customer needs and technological advancements. Leaders should be anchoring their AI adoption in strategic intent rather than hype. AI initiatives should align with business goals and customer needs to ensure they deliver meaningful impact. A participant stated, “AI should be driven by strategic goals, not by the fear of missing out on the latest trend.”
Part of being strategic means adopting a unified measurement framework and integrated steering for both human and AI agents, enabling consistent evaluation. This approach ensures that performance metrics are aligned and comparable across the board. Consider humans and AI agents as a “hybrid workforce” that work together, enhancing each other’s strengths.
Leaders in the field have already taken distinct actions in the AI-for-CX (customer experience) journey, including introducing new performance management targets and KPIs, adopting new tools, changing compensation incentives, offering new training, recruiting new roles, and changing call-routing strategies. We need to think how we steer the hybrid workforce with similar metrics, set up training that fills capability gaps, and think through how to optimize the ecosystem. One attendee remarked, “We need to measure AI and human agents with the same yardstick to truly understand their impact.”
The higher-value work that CX workers can do once they are freed from routine manual tasks includes providing personalized or empathetic services, dealing with regulatory or legal compliance issues, and upselling or cross-selling products or services. Building and implementing the agents that will get organizations to that point, though, requires cross-functional teams with diverse expertise and hands-on experience, alongside new roles including prompt engineers and agent coaches.
Presenters at the Düsseldorf meeting identified five types of AI agents working today, from “basic” copilots aimed at individual augmentation, all the way through to transformative “operator” agents that work cross-functionally, reengineering product development, for example, or predicting and allocating resources. The executives agreed that organizations should prioritize AI value cases and focus on AI applications that drive real customer value and avoid implementing AI for the sake of novelty.
AI should solve genuine problems and enhance customer experience. As one executive noted, “AI should be a tool for solving real customer pain points, not just a shiny new toy.” The participants agreed that agentic AI might be the right answer in some cases, but not all, and that the reasons to introduce it must be sound.
Maintaining a balance between AI and human interaction is essential to ensure high-quality customer service. AI can handle routine tasks, but human agents are still necessary for complex, empathetic interactions. An attendee commented: “We should not do AI because we can or want to save cost. We want to do it for the customer.”