How Generative AI Impacts Customer Support Functions

There’s an enormous hype around artificial intelligence (AI), with statistics showing that 64% of business owners believe AI can improve customer relationships. As many as 80% of executives reported improved customer satisfaction and contact center performance due to implementing AI tools and services.

Many business customers and customer success leaders know that they’ll need to adopt this technology at some point. Still, they may not be sure in what capacity or how it will impact their business operations — particularly regarding customer service experiences. 

Here, we’ll discuss everything you need to know about using AI for customer support, including how AI is changing customer service, the challenges associated with it, and how to implement it well.

How Generative AI Is Changing Customer Support

There’s no doubt that AI is changing customer support, especially now that generative AI is on the scene.

Previously, rule-based chatbots were the primary option for customer support automation through live chat. These bots worked on decision trees, leveraging defined rules to interact with users and provide simple answers to FAQs or solve basic problems. They mapped conversational patterns like a flow chart and worked specifically within scenarios they’d received training data for.

Now, generative and conversational AI systems are using natural language processing and large language models to better understand the context of customer inquiries and respond accordingly. These systems use generative AI to deliver answers (similar to what you may have seen with ChatGPT), and they typically incorporate machine learning to improve answer quality over time.

There are plenty of benefits of generative AI for customer support to consider. These include the following:

Related: Customer Support Chatbots: Key Features, Use Cases, & More

Improved customer experiences

Generative AI-powered chatbots allow your business to provide instant, 24/7 support to your customers. You can significantly increase response times to customer questions and offer personalized recommendations and solutions based on customer data.

Unlike previous rule-based chatbots that were defined by limited conversational workflows, tools leveraging generative AI can promote truly unique customer interactions. These personalized responses can enhance customer engagement through interactive and engaging conversations, creating stronger customer experiences through messaging.

As chatbots are now offering more advanced support to customers, they also reduce the burden placed on customer service teams. Human agents, as a result, are freed up to handle more complex customer queries that need their focused attention.

Related: Chatbots for Customer Service: Strategies & Tips to Grow Customer Satisfaction

Increased efficiency and productivity

Generative AI tools can boost efficiency and agent productivity throughout your business or call center. They can do this by:

• Automating routine tasks like support ticket routing and issue categorization to ensure that they end up in front of the right team member
• Generating accurate and informative responses to customer queries
• Reducing average handling time for customer issues

Next-level problem-solving 

Rule-based chatbots couldn’t solve customer problems outside of offering pre-approved solutions that matched their training data for specific customer needs. Generative AI, however, actually can solve problems based on questions and concerns customers are bringing to the table, and you can train it to do so within business policies. 

Advanced generative AI technologies can do the following: 

• Analyze customer data to identify trends, customer sentiment, and potential issues
• Suggest proactive solutions to prevent problems from occurring
• Accelerate issue resolution through advanced knowledge-base search and escalate to a human agent when needed

Real-time assistance for agents

Call center agents can benefit from generative AI, even outside of AI-powered chatbots that handle a chunk of customer questions. Customer support teams are currently leveraging AI for the following purposes:

• Providing real-time suggestions and information to support agents — accessing knowledge articles, conversational scripts, and AI functionality to create strong responses.
• Automating tasks to free up agents for more complex issues.
• Improving agent knowledge and skills through AI-powered training. 

Related: AI Call Centers: The Future of Customer Communication

Best Practices for Adopting Generative AI

When adopting generative AI to support your customer service agents and clients, consider the following best practices: 

Identify clear use cases

It’s not enough to just adopt a generative AI tool and call it a day. Consider specific use cases where these functions can add the most value. What are your existing bottlenecks and challenges? What concerns have been flagged through customer feedback?

You may realize that new agents struggle with relying too much on generic call scripts and want to use generative AI for relevant and personalized suggestions for each conversation. You might also realize that customers are frustrated with the outputs of rule-based chatbots and believe that AI-powered bots will provide stronger user experiences.

Keep in mind that your specific use cases may depend on your business type. E-commerce businesses may need chatbots that offer sales-focused functionality, and organizations offering financial services may need bots that comply with advanced security regulations.

Start small and scale

It’s best to start with a single pilot project to test the new technology and refine your approach instead of going all in on a multi-pronged approach.

If you’re going to use AI to automate tasks or provide real-time agent suggestions, you could start with a few tech-savvy and adaptable agents. This can help you ensure the technology is working and identify any potential problems before you launch it on a wide scale.

Invest in training

While many generative AI tools are designed to support your agents and streamline service operations, that doesn’t happen automatically. It’s essential to train your team so they have the skills to effectively use and manage generative AI.

This should include: 

• Showing your team how to navigate new AI tools, their interfaces, and functions.
• Explaining how you want team members to use different features to automate workflows.
• Providing training on how to use generative AI suggestions in conversations, including how to determine strong suggestions and how to implement them. 

Measure and iterate

As you implement new AI tech, it’s essential to monitor performance metrics and continually make adjustments as needed.

While the metrics you track will vary depending on the specific tools and functionality you’re using, these are a few good baseline options:

• Customer experience metrics, including net promoter score and customer satisfaction score. 
• Agent productivity, including the number of calls, messages, and support tickets managed. 
• Customer sentiment from different platforms implementing AI

Leverage customer experience insights to assess the impact of generative AI features. Ultimately, conversational AI solutions should be improving the customer experience — not hindering it. Tracking these metrics is essential to prioritize a strong customer experience first and foremost.

Challenges of Using Generative AI for Customer Support

While generative AI customer service tools can provide extensive benefits to your team, it’s crucial to be ready for the potential challenges that can come with this technology. Let’s take a look at the most common obstacles and see how to address them proactively.

Hallucinations

Generative AI models can sometimes generate incorrect or nonsensical information (called “hallucinations”), leading to customer confusion and frustration. Imagine a customer being told that your product has a feature that doesn’t exist before they convert, and later they find out that wasn’t accurate.

You can review chatbot conversation summaries to ensure accuracy and train your agents to flag when AI suggestions or scripts contain inaccurate information so you can address this immediately.

Strong guardrails and detailed access to a knowledge base may help prevent the worst of these errors, but keep a close eye on what AI is coming up with.

Lack of empathy

While AI can provide factual information, it often struggles to understand and respond to customer emotions, leading to impersonal interactions. It may also come across as harsh in particularly sensitive situations.

If a customer asks a chatbot to remove a pet from their prescription account following a loss, for example, you don’t want the chatbot to respond, “You’re all set! The problem has been resolved! Have a great day. :)”

While AI will never fully understand empathy — and that’s why human agents will always be needed — you can train your system to recognize key phrases like “frustrated,” “upset,” or “angry” to help the tools respond appropriately. Similarly, you can train the bot to identify situations and respond with a certain level of empathy, such as the pet loss scenario above.

Complex issue handling

The reality is that generative AI may not be equipped to handle complex or nuanced customer issues that require human judgment and problem-solving skills. While that may not be its job,  it is essential to recognize where the line falls and what those limitations are.

You can teach agents and bots to identify when the conversation needs to be placed firmly in live agent hands without AI customer support. It’s also important to have an “instant out” option on live chat so that customers can get in touch with a support team member when they feel it’s needed.

Dependency on technology

Reliance on AI systems can create vulnerabilities if the technology fails or experiences downtime. If you’re using AI-powered chat as a first line of customer defense, for example, a failure or system outage could significantly impact the workload of live agents when they’re suddenly swarmed by an overflow of messages.

While you can’t necessarily plan for this, you can make sure you choose a reliable provider with high uptime service level agreements and strong customer support for their clients.

Cost and resources

Implementing and maintaining a generative AI system can be expensive. You need to pay for the system itself and someone to oversee, train, and manage it. While it may ultimately provide a positive ROI by improving agent productivity and the customer experience, these costs shouldn’t be overlooked.

Implementing these AI systems often requires specialized expertise. You may have someone internally who can take this on; if not, you’ll want to work with a service provider who can help you through onboarding and setup and provide the support you need. 

Customer acceptance

Some customers may prefer human interaction and be resistant to using AI for support. This is particularly true for older customers or those who have previously had negative experiences with chatbots and struggled to connect to human agents — even when it was supposedly an option.

Many customers will likely come around as they’re exposed to more advanced chatbots, as they’re a convenient self-service option that can now help with many questions and concerns. 

If your audience demographics are resistant to chatbots, you can include a copy in the opening message to let people know what the bot is capable of and give them an instant out to connect with a live agent instead.

Regulatory compliance

Adherence to industry regulations and data protection laws is already complex for many businesses, but this can be an enormous challenge for AI systems.

Chatbots can, for example, process payments, but they can’t store that payment information in chat transcripts or summaries according to PCI regulations. This may require training, but opting for a strong, security-focused platform that has specialized industry experience is a good place to start. You can learn more about Nextiva’s security and industry compliance here. 

Make sure you’re also choosing a highly secure platform that will protect your business and customer information. Look for standard security practices like advanced encryption, two-factor authentication, and industry-standard certifications. 

Questions Leaders Should Ask Before Adopting Generative AI

If you’re still on the fence about adopting generative AI for your customer support teams, these are the questions you should ask yourself:

• Will generative AI deliver a significant ROI and improve customer satisfaction?
• Do we have the necessary data, infrastructure, and talent to implement and maintain the AI system?
• How will generative AI impact our workforce, and what training is needed?
• Can we ensure the ethical and responsible use of AI, including addressing bias and privacy concerns?
• What are the potential risks, and how will we mitigate them?

Nextiva Helps You Scale Customer Support Effectively With AI

The potential upside of using fully generative AI for customer service (supporting live agents, of course) can be incredible. You can deliver a stronger and more personalized customer experience, streamline agent productivity, and automate workflows. 

However, it’s important to weigh those upsides against potential challenges — inaccurate information, security concerns, and user resistance. Having a strong implementation plan, following best practices, and selecting a reliable provider can help mitigate the biggest challenges and promote the benefits. 

Nextiva’s unified CXM platform blends AI features with an all-in-one, easy-to-use CX platform that feels familiar to agents and improves team productivity. It offers all the features you need to deliver outstanding customer support. 

We prioritize security and reliability, giving you a tool your team can count on. No matter where your business is on its AI journey, Nextiva is a reliable CX solution.