Discourse around artificial intelligence (AI) spans industries and applications, and customer experience (CX) is no exception. Some say AI will take over CX jobs entirely, while others say it shouldn’t be used in customer interactions because it will never be able to fully replicate humans. 

At SupportNinja, we have a more nuanced view: we firmly believe that human CX teams and AI both have their respective strengths and weaknesses, and that they’re better together. 

Keeping a human in the loop is a great way to optimize human-AI collaboration. But what does a human-in-the-loop AI strategy look like, and how can it improve CX?

What is Human in the Loop (HITL)?

Human in the loop (HITL) is based on the premise that even with current advancements in AI, humans still need to supervise machine learning (ML). 

ML systems like generative AI don’t have beliefs, opinions, or knowledge beyond the extent of their datasets. Their entire “being” is dependent on processing factual information provided by humans. Because of that, humans are needed to train, test, and supervise how an AI is using the data.

By working together, humans and AI can confidently and efficiently complete a wide range of tasks, from identifying objects in images to responding to customer inquiries. Common applications for HITL include text generation, image labeling, audio transcription, content moderation, natural language processing (NLP), and more.

How Does HITL Work?

In HITL models, humans and AI work together. In some instances, humans monitor AI outputs, performing quality assurance (QA) and validations, and making corrections when needed. In other instances, humans perform the main actions with assistance from AI surfacing relevant information or parsing through data quickly to form the best response. 

This layered system doesn’t just fill gaps in the AI’s knowledge and improve its accuracy over time — it also keeps humans from getting bogged down in repetitive tasks, so they have time to work on more complex problems.

Particularly in the context of CX, AI algorithms are generally built within a closed system. This means that the AI is trained using specifically selected, human-approved content and / or data, and doesn’t have access to any external materials. 

Throughout the training process, humans provide feedback on the AI’s responses to ensure they meet the required standards. As the AI becomes more proficient, it can eventually perform its own QA, with humans stepping in to double-check responses as needed. This iterative process continuously trains the algorithm, enhancing its performance over time. 

Benefits of HITL in CX

Humans outperform AI whenever it comes to complex problem-solving, empathy, and creativity, but AI enables scaling instantly. To provide the highest level of service, the best solution is to combine the power of humans and AI via HITL.

In CX, the HITL approach is particularly helpful for:

• Improving efficiency and quality of service — When CX agents are in charge of fewer repetitive tasks, they can focus on providing excellent service as they resolve more complex customer inquiries. 

• Improving chatbots — Chatbot AIs can get things wrong (especially when pulling from open datasets), and they often struggle with complex questions. Chatbots are most helpful when there’s a mix of AI and humans working together to provide accurate information quickly. 

• Content moderation — Content moderation teams can train AI to identify harmful content, and as the algorithm becomes more accurate, humans can simply double-check the AI’s work. This reduces human exposure to distressing images and text.

• Enhancing personalization — Companies constantly gather data about their customers, like purchase history, wish lists, and past support tickets. With access to these datasets, AI can apply personalization instantly — but it can also make mistakes. Human feedback can improve AI’s ability to provide accurate, helpful personalization.

• Reducing escalation — AI doesn’t always fully comprehend or appropriately respond to inquiries, which makes for a frustrating customer experience. With a human in the loop, misunderstandings can be prevented or promptly addressed, leading to better customer satisfaction and less escalation. 

• Providing certainty for rarer datasets — In the U.S., “chips” universally means potato chips. However, someone from the U.K. might ask your AI what varieties of “crisps” you sell. But neither word will mean anything to your AI if these concepts weren’t part of its training data, or if it’s not explained via HITL. This concept also extends to image processing and beyond.
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• Ensuring safety standards — Imagine using AI in the medical field, banking, manufacturing, or any industry where safety standards and regulations continually change (and as fast as the world is changing these days, this really applies anywhere). Without a human in the loop, the AI’s dataset will end up being outdated, to the detriment of customers and staff.

Challenges with HITL

Implementing HITL is not without its challenges. It’s important that your CX team truly understands how to use AI to improve CX the right way. 

Some concerns you might face include:

• Finding the right balance — Even with the aid of an HITL workflow, AI can fall short in specific areas. Complex issues often require human assistance, and even for simple matters, some customers would rather speak with a human. Always give your customers the option to interact with a human agent.

• Choosing materials to train your AI — You don’t want to give your AI free reign over all available data, so you’ll need to carefully select the content it has access to and limit its level of creativity. It should be limited enough to prevent the AI from fabricating irrelevant answers, while still detailed enough to prevent knowledge gaps. 

• Training and managing human agents — HITL agents need to understand the AI model itself — the purpose of the training, the intended results, the optimization work likely needed post-deployment, the edge cases. They also need to understand what type of data might confuse the AI model, or worse, cause biases. It’s vital to carefully choose a team you can entrust with the vision that you have for your AI. 

• Questions about transparency — Should you try to downplay your use of AI, or make it clear when customers are not interacting with a live person? The answer is almost always the latter. By being upfront, you’re showing the customer that you respect them and their time, and you’re not trying to mislead them. 

• Data privacy concerns — Sensitive data used for training must adhere to strict guidelines to avoid breaches. At the same time, AI models may develop bias based on the data they’re given access to. Regular audits of fairness, bias, and accuracy are a must.

How SupportNinja uses AI technology

At SupportNinja, we leverage NinjaBot — our own AI solution for improving CX — to deliver fast, accurate responses, automate routine queries, and ensure our agents have all the information they need at their fingertips. 

We also offer bespoke builds to align with your needs. Our team of HITL experts will work with you to ensure your AI tools integrate seamlessly into your tech stack and are set up using only the data you want to include. 

Interested in learning more about how SupportNinja can empower your CX team with faster resolutions and smoother customer journeys? Get in touch with us.