For 70% of organizations, the recognized need to improve customer experience (CX) has motivated businesses to prioritize their digital transformation. As customer expectations rise and brands look to become more competitive from a CX perspective, this is both an offensive and defensive business strategy.  

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Widespread, frequent engagement with services like Amazon, Netflix and Peloton have quickly conditioned customers to expect fast, frictionless and high-quality digital experiences for all their brand and provider interactions. CX professionals are paying attention, yet many acknowledge there is still much work to be done to meet evolving customer demands. 

A recent survey on the future of artificial intelligence (AI) in contact centers found that CX professionals are keenly aware that their customers expect a far more automated experience than what many businesses currently deliver. In fact, only 4% of the surveyed respondents considered their organizations as “transformational” in employing AI to elevate the customer journey and experience.    

This significant gap between consumer expectations and customer service delivery has been a result of legacy solutions and products, which increasingly present more barriers than benefits for most organizations.  However, traditional AI tools are costly. Operationalizing these tools  requires expensive data scientists and sector specialists, making them a budget ‘non-starter’ for most businesses. Despite the compelling case for AI automation, the substantial upfront investment required can be difficult to justify given the significant runway of time before they can deliver a return on that investment.   

But change is underway. As a result of recent developments in AI products, organizations of all sizes can unlock the next level of customer experience with straightforward and affordable solutions. At the same time, they can continuously and incrementally improve customer experiences. This is being done using “human-in-the-loop technology” to train AI models within the contact center.    

To appreciate the positive disruptive impact of this combination requires a top-level overview of the traditional method of training AI models. Previously, in a live environment, there was no way to easily intervene when an AI model’s output was incorrect without using, expensive data scientists with a significant turnaround time.  

New AI tools can be combined with human-in-the-loop technology to directly address these issues and remove these barriers. These new AI products are built in ways that can be easily adopted and sustained in contact centers. This is done by providing frontline contact center agents with the tools to resolve AI-model issues quickly and correctly – with “clicks, not code”, eliminating the time and cost delays traditionally associated with this process. 

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AI tools can now empower frontline agents to resolve issues quickly and correctly, while also improving the strength of AI overall and their internal resolution process. For instance, a business adds AI-informed chatbots or virtual agents to resolve regular customer queries without direct human involvement. When a customer calls with a problem, they are connected to a virtual agent. The speech-to-text and intent models work in the background, allowing virtual agents to identify and interpret what the customer is saying. If a virtual agent bot does not understand a customer question or does not provide the customer with the correct answer, the contact is transferred to a live agent. When this happens, the question is flagged up to the contact center staff who can then train the AI model with the correct response. Should another customer have that same question, the virtual agent will be able to provide the correct answer without escalating the contact to a human agent. This is human-in-the-loop technology in action.  

The “human” in this scenario is the agent who trains the bot to correctly respond to the query next time. Agents use their domain knowledge to label data to ensure bots provide reliable output. Quality assurance mechanisms are built-in so that no single agent can train the bot without it being approved by a supervisor.   

Just like people improve with training, so does AI. This is the power of tools that operate with continuous human-in-the-loop nudges. Over time they enable the AI models to perform 10x better – creating sustainable, ever-improving customer experiences that don’t require human experts to run and operate effectively. The result is cost savings and improved customer experience. Most companies can almost immediately automate about 30% of their customer interactions while increasing success rates, resolving more cases through automation and decreasing the cost per case.   

This is an exciting development in contact center technologies. . By building tools and implementation models that enable non-technical staff and live agents to easily train and run AI models, it is now possible to democratize AI and machine learning-led customer experiences. This helps companies meet and even compete with rising market expectations and turns AI adoption into ROI. It also positions businesses to sustainably scale a competitive customer experience that increases brand connection, customer satisfaction, and efficiency.  

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