In today's fast-paced digital landscape, CX departments face increasing pressure to deliver exceptional service quickly and efficiently — integrating AI technology helps them do so.

Both customers and companies have concerns about whether AI will have a negative impact on CX. While it’s true that an overreliance on AI can lead to impersonal interactions, human-only support can lead to slower response times and other negative outcomes. 

In reality, it’s all about balance. The best customer experiences happen when humans and AI work together. Let’s explore the downsides of an unbalanced approach to CX and the benefits of a balanced one.

The Challenges of Human-only CX

While CX certainly requires a human touch, relying solely on human CX capabilities limits efficiency and growth, and can fail to provide customers with the type of interaction they’re expecting. 

Here are a few of the biggest issues associated with human-only CX:

• Time limitations — Humans tend to take longer than AI to make decisions and perform tasks, and can only dedicate their full attention to one customer at a time. This makes it difficult to improve time-related KPIs, like average resolution time and first response time, and can result in growing pains as your organization scales. 

• Inconsistency — Regardless of training, human support agents sometimes give inconsistent answers to customer requests. This erodes trust and creates a frustrating experience for customers, especially if they’re given different answers by different agents.

• Limited data processing abilities — From predictive analytics to identifying patterns in KPIs, data is vital in CX. Humans can’t process enormous volumes of data all at once, but AI can. This even comes into play when remembering all of the details of your business — from policies to product information, AI can quickly and easily train on the most accurate data, while people may need time and practice to learn new information.

• Repetitive tasks — When customers don’t have access to self-service options, representatives have to answer the same questions over and over, which takes up valuable time they could be spending on more complex tasks. 

• Misalignment with customer preferences — Some groups, like Gen Z and millennials, actually prefer self-service technology like generative AI over speaking to a person. These customers may feel inconvenienced if their only support option is a person-to-person interaction.

The Challenges of AI-only CX

AI outperforms humans in terms of speed and efficiency, but it has its limitations. 

Here are some of the problems companies experience when they rely too heavily on AI for CX:

• Lack of empathy and personalization — This is one of the most common concerns about AI-driven CX. Customers expect personalization and want to feel connected to the brands they interact with, but AI can’t perfectly replicate a person-to-person interaction. This might leave some customers feeling disconnected and unappreciated. However, AI personalization is possible — in fact, AI can quickly analyze customer data and produce personalized recommendations.

• Potential quality issues — Unlike humans, AI can’t use creativity and critical thinking to adapt to unfamiliar situations, so when presented with unfamiliar customer requests, it sometimes falls short. Additionally, humans provide AI systems with crucial feedback (rating outputs, correcting biases, etc.) that improve their accuracy. Without this human input, AI is likely to produce poor-quality outputs at least some of the time, leading to more escalations and frustrated customers.

• Misalignment with customer preferences — Some customers prefer to speak to a person rather than interact with technology, especially if their request is particularly complex. Customers wanting to speak with a human representative will be frustrated if an AI chatbot is their only option.

How Does AI Improve Customer Experience?

While humans excel in areas like empathy, creativity, and emotional intelligence, AI excels in efficiency and data analysis. When integrated strategically, a combination of AI and human support can result in faster information processing, improved KPIs, more personalized recommendations, and better connections with customers. 

At SupportNinja, we call this AI-enabled CX.

One way we utilize this approach is with NinjaBot, our own AI chat solution that allows for faster, more accurate service and handles repetitive inquiries. This frees up agents’ time to work on more complex tasks that can’t be performed well by AI, like answering unfamiliar questions, coming up with ad-hoc solutions, demonstrating emotional intelligence during charged interactions, and handling urgent requests.

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The Path Forward for CX and AI

Open vs. Closed Datasets

To combine the unique strengths of humans and AI, start by establishing a closed dataset. In a closed dataset, your AI is trained exclusively with materials you select, like FAQ pages, knowledge bases, and SOP documentation, whereas an open dataset allows the AI to use any online sources to generate outputs.

“Open-dataset AI models that are available for public use, like ChatGPT, are very creative,” says Rio Barrameda, Senior Vice President for Products and Innovation at SupportNinja. “They pull from different unverified sources, and that’s where inconsistency and errors happen. With our AI, on the other hand, we have control over what we feed the generative model, and can even adjust its creativity level to prevent it from inventing answers.”

By restricting your AI’s training to a specific set of materials, you can ensure that it only leverages relevant and accurate information in its responses.

Humans in the Loop

Next, don’t let your AI run wild on its own — be sure to keep humans in the loop. Allow your review team to rate AI-generated CX responses and continue to expand its training with high-quality materials. Over time, incorporating this human feedback and oversight into your AI will improve its accuracy. 

For example, AI-powered SupportNinja chatbots generally produce anywhere from 12 to 20 errors during the first month they’re live, but that number goes down to just 2-3 errors by the second month thanks to rigorous QA.

Rio Barrameda says this human review part of the process is crucial for reducing errors. “Agents can flag our chatbot’s answer if they feel it's not completely accurate. A moderator can then take a look at the flagged answer, identify the data source where there was a mistake, correct it, and have the AI ingest the corrected version.”

This iterative process teaches the AI to provide more accurate, more empathetic responses that make for a positive customer experience.

Ready for AI-enabled CX?

Humans and AI each have their strengths and weaknesses, so why not combine them for better, more efficient CX? Integrating AI into your CX strategy can empower your organization to leverage its efficiency and data analysis capabilities while still benefiting from the uniquely human strengths of empathy and creativity. 

At SupportNinja, we firmly believe that AI and humans are better together. Using a combination of live agents and AI that works for your organization, our customized, agile, AI-enabled solutions deliver high-quality CX for your customers and drive better business results for you. 

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