The concept of human and AI powered agents coming together to meet escalating customer expectations comes at a time when 77% of customer service reps report increased volume and more complex workloads compared to a year ago. We are now in the era of the hybrid CX workforce, in which human and conversational AI-powered agents augment and collaborate. Their mission: to work together to reduce customer wait times, standardize and improve caller experience, resolve issues faster, and bring down costs without sacrificing customer experience. However, to realize this mission, organizations need to reframe what it means to orchestrate their hybrid workforce.
Call centers employ more than 3 million workers in the U.S., each averaging about 4,500 calls per month. The volume of calls has increased substantially since 2020, and yet, only 7% of contact centers have more than 1,000 human agents on staff to field all these calls. It’s no surprise that Gartner estimates 80% of customer support organizations will implement some sort of AI to help improve customer experience and boost agent productivity by the end of this year.
When first introduced, AI in the customer care environment was designed to operate in the background as assistive technology for the human agent. As AI evolved, the early automated agents were able to solve simple transactional issues, typically through virtual voice and chat assistants.
Today, the idea isn’t just to adopt agentic AI, but to make it align more strategically with human agents to actually improve operations, and not just deflect volume. Here are six tips CX leaders can use to improve how they orchestrate AI agents and human agents:
- Position AI agents as teammates: The myth persists that AI agents are meant to replace humans in customer care, but in reality, they should be force multipliers for them. Contact center leaders should see humans and AI as teammates working together toward a common goal. Too often, AI agents are still working in isolation on a small number of very specific cases. Instead, organizations should treat AI agents as integrated team members, not tools in a silo, to improve operations and customer experience.
- Focus on creating smarter, smoother handoffs: If a customer must repeat their issue after being handed off from an AI agent to a human, it creates friction and dissatisfaction. That’s why it’s essential to design seamless transitions by ensuring context memory, intent clarity, call summary, and intelligent routing. This avoids customers repeating information. During the AI agent build, define clear handoff rules so the agentic system knows when to escalate a case to humans, such as detecting caller frustration, incoming calls from high-value accounts, or complex cases outside of the AI agents’ defined capabilities.
- Find ways to empower human agents: AI agents should handle routine tasks, freeing human agents to focus on higher-value conversations, problem-solving, and building customer loyalty. This makes human agents stronger, not redundant. Both human and AI agents have roles to play as relationship builders.
- Provide hybrid agent ecosystems access to the same pool of data: For AI and human agents to collaborate, they must operate from the same data, operations, and processes. That means sharing access to the same customer data, same call routing logic, and accountability for call resolutions. For example, a returns workflow can start with an AI agent capturing order details from the customer, validating the returns policy, and offering standard options. However, if the customer becomes confused, frustrated, or asks about a missing refund, this triggers the AI agent to hand off the call to their human counterpart, a live agent with full context, who picks up the call seamlessly.
- Rethink success metrics for agents: Given the new hybrid agent paradigm, traditional CX metrics are no longer sufficient measures of success. For example, if an AI agent’s primary role in the contact center is to serve as the first touchpoint for customer queries, a key goal for that agent is to discover new information about that customer. The more conversations, the more the AI agent learns about a customer. The more data an organization has, the more personalized the experience will be the next time that customer calls. Newer indicators like collaboration efficiency, blended resolution rate, output consistency, and agent trust in AI will become more relevant than average handle time or average speed of answer.Â
- Continuously optimize the AI/human balance: The AI feedback loop is what makes agentic systems smarter, more accurate, and more useful over time. It works as a cycle between AI systems, human agents, and customers. Contact center managers can track metrics like handoff rates, first-contact resolution, and customer sentiment to see where AI succeeds or struggles. Using these, managers and human agents can refine what stays automated and what requires a human touch. This makes orchestration an ongoing cycle, not a one-time setup.
When done well, human-AI orchestration resembles a relay race. AI takes the baton first for speed and scale, handling most customer needs, and then smoothly passes it to humans when empathy, judgment, or creativity are needed. Even during human-led voice calls, AI can be working in the background to support the agent with tasks such as language translation or suggesting appropriate responses.
By adopting these strategies, organizations can move beyond basic agentic AI implementation to create a synergistic hybrid workforce. This helps organizations gain a competitive edge and meet the rising expectations of the customer experience, but also empowers human agents, ultimately leading to greater efficiency, reduced costs, and enhanced customer loyalty. The future of customer service lies in this intelligent collaboration, where the strengths of both human and AI agents are maximized for optimal outcomes.
