Is the human in the loop a value driver? Or just a safety net?
The latest crop of AI agents for the contact center can engage in fluid conversation, use reasoning to solve problems, and take action to resolve customers’ issues. When they work in concert with humans, their capabilities are maximized. That makes the human in the loop a critical component of any AI agent solution – one that has the potential to drive significant value.
Most solution providers focus on the human in the loop as both a safety measure and a natural escalation point. When the AI fails and cannot resolve a customer’s issue, it hands the interaction to a human agent.
Many contact center leaders see this approach as appropriately cautious. So, while they steadily expand automated self-service options, they tend to keep human agents front and center as the gold standard for customer service.
But here’s the catch: It also imposes significant limitations on the value AI agents can deliver.
Fortunately, there’s a better approach to keeping a human in the loop that drives the value of an AI agent instead of introducing limitations.
The typical human-in-the-loop roles
You probably won’t find a solution provider who doesn’t acknowledge the importance of having a human in the loop with a generative AI agent. But that doesn’t mean they all agree on exactly what that human should be doing or how the solution should enable human involvement. For some, the human in the loop is little more than a general assurance for CX leaders that their team can provide oversight. Others use the term for solutions in which AI supports human agents but doesn’t ever interact with customers.
Beyond these generalities, most solutions include the human in the loop in one or more of these roles:
- Humans are directly involved in training the AI. They review performance and correct the solution’s output during initial training so it can learn and improve.
- Humans continue to review and correct the AI after deployment to optimize the solution’s performance.
- Humans serve as an escalation point and take over customer interactions when the AI solution reaches the limits of what it can do.
The bottleneck of traditional escalation
Involving members of your team during deployment and initial training is a reliable way to improve an AI agent’s performance. And solutions with intuitive consoles for ongoing oversight enable continued optimization.
But for some vendors, training and optimizing the AI is largely where the humans’ role ends. When it comes to customer interactions, your human agents are simply escalation points for when the AI agent gets stuck. The customer experience that generates is a lot like what happens when a traditional bot fails. The customer is transferred, often into a queue where they wait for the next available agent. The human in the loop is just there to pick up the pieces when the AI fails.
This approach to hard escalations creates the same kind of bottlenecks that occur with traditional bots. It limits containment and continues to fill your agents’ queues with customers who have already been let down by automation that fails to resolve their issue.
The incremental improvements in efficiency fall short of what could be achieved with a different human-AI relationship and an AI agent that can work more independently while maintaining safety and security.
Redefining the role of the human in the loop
The first step to easing the bottlenecks created by hard escalations is to redefine the relationship between humans and AI agents. We need to stop treating the humans in the loop as a catch-all safety net and start treating them as veteran agents who provide guidance to a less experienced coworker. But for that to work, the AI agent must be capable of working independently to resolve customer issues, and it has to be able to ask a human coworker for the help it needs.
With a fully capable autonomous AI agent, you can enable your frontline CX team to work directly with the AI agent much as they would with a new hire. Inexperienced agents typically ask a supervisor or more experienced colleague for help when they get stuck. An AI agent that can do the same thing is a more valuable addition to your customer service team than a solution that’s not much more than a better bot.
This kind of AI agent is able to enlist the help of a human whenever it
- Needs to access a system it cannot access on its own
- Gets stuck trying to resolve a customer’s issue
- Requires a decision or authorization by policy
The AI agent asks the human in the loop for what it needs – guidance, a decision, information it cannot access, or human authorization that’s required by policy. Once the AI agent receives what it needs, it continues handling the customer interaction instead of handing it off. For added safety, the human can always step in to speak with the customer directly as needed. And a customer can also ask to speak to a human instead of the AI agent. In the ideal scenario, you have control to customize the terms under which the AI agent retains the interaction, versus routing the customer to the best agent or queue to meet their needs.
Here is what that could look like when a customer calls in.
The expansive value of human-AI collaboration
With this revised relationship between humans and AI agents, the human in the loop amplifies the impact of the AI agent. Instead of creating or reinforcing limitations, your human agents help ensure that you realize greater value from your AI investments with these key benefits:
1. Faster resolution times
When an AI agent can request and get help – and then continue resolving the customer’s issue – customers get faster resolutions without transfers or longer wait times. That improves First-Contact Resolutions (FCR) and gets customers what they need, faster.
2. More efficient use of human agents
In the traditional model, human agents spend a lot of time picking up the pieces when AI agents fail. With a collaborative model, agents can focus on higher-value tasks, such as handling complex or sensitive issues, resolving disputes, or upselling services. They are not bogged down by routine interactions that the AI can manage.
3. Higher customer satisfaction
Customers want quick resolutions without a lot of effort. Automated solutions that cannot resolve their issues leave customers frustrated with transfers, additional time on hold, and possibly having to repeat themselves. An AI agent that can ask a human coworker for help can successfully handle a wider range of customer interactions. And every successful resolution improves customer satisfaction.
4. Scalability without compromising quality
The traditional model of escalating to humans whenever AI fails simply doesn't scale well. By shifting to a model where AI can consult humans and continue working on its own, you ensure that human agents are only involved when they are uniquely suited to add value. This makes it easier to handle higher volumes without sacrificing quality or service.
5. Continuous learning to optimize your AI agent
Interactions between the AI agent and the human in the loop provide insights on the APIs, instructions, and intents that the AI needs to handle similar scenarios on its own in the future. These insights create the opportunities to continue fine-tuning the AI agent’s performance over time.
Generating value with the human in the loop
By adopting a more collaborative approach to the human-AI relationship, contact centers can realize greater value with AI agents. This new model allows AI to be more than just another tool. It becomes a coworker that complements your team and expands your capacity to serve customers well.
The key to implementing this approach is finding an AI solution provider that has developed an AI agent that can actively collaborate with its human coworkers. The right solution will prioritize flexibility, transparency, and ease of use, allowing for seamless integration with your existing CX technology. With this type of AI agent, the humans in the loop do more than act as a safety net. They drive value.