28 Powerful Insights on Generative AI for Customer Support Success

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Every customer support team has to deal with help desk tickets. It’s crucial to providing exemplary customer care, but it can be tedious and overwhelming, especially for large organizations receiving thousands of monthly tickets. What happens when a customer reaches out for support? First, they’ll create a help desk ticket. Then, someone on the support team will respond to the ticket to resolve the customer’s issue. If the problem is simple, they may be able to fix it immediately.

In many cases, tickets can contain complex issues that take time to unravel. Instead of simply responding to a customer's initial query, support agents often have to sift through tons of information to identify the root cause of an issue before they can create a solution. Generative AI for 24/7 Customer Support can help. This article will discuss effectively integrating generative AI into your customer support strategy to improve efficiency, enhance customer satisfaction, and increase overall service quality.

One way to achieve your goals is using ChatBee's AI customer support tool. It can help you quickly and accurately integrate generative AI into your existing customer support operations to improve efficiency and service quality.

What is Generative AI for Customer Support?

Generative AI is a subset of artificial intelligence that can produce new content by decoding patterns within its input data, including:

Text

Images

Codes

Product designs

Music

Central to this technology are large language models, or LLMs, like GPT (Generative Pre-trained Transformer).

LLMs are trained on vast datasets, enabling them to understand and produce unique, human-like content. They can:

Summarize notes

Distill information

Answer questions

Compare and contrast entities

Perform competitive analysis

Create code

This makes them versatile tools for various applications.

Enhancing Customer Service with Generative AI

In customer service, this capability translates into chatbots and virtual agents that can understand and respond to customer inquiries with remarkable accuracy and relevance. Generative AI in customer service can also improve service personalization, complex problem-solving, and feedback analysis.

How Generative AI for Customer Support Eases the Burden on Support Teams

Support teams facing high-stress situations and endless repetitive tasks often experience burnout. Support agents can focus on their work's more engaging and intellectually stimulating aspects by offloading routine inquiries to AI.

Many companies are already taking concrete steps to reduce the burden on their employees. According to our Customer Service Trends Report 2023, 71% of support leaders plan to invest more in automation to increase the efficiency of their support team.

Tools like AI-powered chatbots will allow your support team to do more by:

Automating answers to frequently asked questions

Collecting information to help triage complex problems

Routing urgent or VIP queries to the agent best equipped to resolve them

This doesn’t mean humans will be eliminated from customer service. Rather, they’ll gradually evolve and develop the skills necessary to collaborate with this rapidly advancing technology.

How Generative AI for Customer Support Requires No Training to Get Started

As businesses grow, so does the volume of support inquiries they receive. But hiring and training more support agents may not always be the most practical or cost-effective response.

According to 41 percent of the customer care leaders surveyed by McKinsey in 2022, training a new employee to achieve optimal performance can take up to six months. An additional 20 percent reported that such comprehensive training takes over six months.

Scaling Customer Support with Generative AI

Implementing generative AI for customer support can help your team achieve scalability. It allows you to offer 24/7 assistance to your customers and more consistent responses, no matter how high the volume of inquiries becomes. In short, you overcome two hurdles at once:

You expand your level of support without overburdening your current human agents or increasing the costs associated with training new employees.

Reducing Training Costs with AI

For Samuel Miller, customer support operations manager at Dental Intelligence, the most significant value added for AI is the reduction of training costs. “For us, it’s really about saving money on training because we don’t have to train them on every single thing. We can just train them on the major issues they have to do, and not so much on the day-to-day things that customers can find, the knowledge articles, and stuff like that. It allows us to go deeper in the training quicker.”

How Generative AI for Customer Support Improves Team Productivity

According to recent McKinsey research, another benefit of generative AI for customer support is its ability to increase team productivity by 40-45 percent.

This can take different forms for each business. For example, a healthcare enterprise may use sentiment analysis to detect a frustrated customer and escalate the issue to a human agent for personalized attention.

If you’re unsure about deploying Gen AI in your company, take Kavita Ganesan’s advice and look out for inefficient processes. Founder of the consulting business Opinosis Analytics and Ph.D. in Natural Language Processing (NLP), she believes that:

“Finding those manual processes that are repetitive and require human-level thinking – that’s a key point – is where AI solutions can really make an impact in the short term because those problems are well-understood and likely have metrics you can use as a way to measure how it’s performing against the manual approach.”

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28 Best Ways to Use Generative AI for Customer Service

1. Dynamic AI Chatbots for Resolutions

Moving beyond the black-and-white logic of traditional bots, generative AI chatbots bring a fluid, human-like understanding to customer interactions. They can handle dynamic queries by tapping into live databases and unified customer profiles in a powerful CX tool. For instance, a customer asking about their bank account balance after a phishing scare gets real-time, accurate information.

AI-Powered Customer Experience

With AI, even complex tasks become streamlined, such as:

Rescheduling multi-city flight itineraries

Adjusting flight dates
Updating seat preferences
Modifying meal options

AI can guide or even complete these tasks for the customer, enhancing their experience. Stay many steps ahead with smart chatbots that:

Predict Customer Satisfaction (CSAT) before customers feel compelled to leave a low rating

Notify you in real-time, allowing you to address issues proactively

It’s all powered by AI magic.

2: Advanced Sentiment Analysis

Feedback is gold, but mining it is a challenge. Generative AI dives into the ocean of customer feedback, extracting valuable insights regardless of volume. From parsing through millions of reviews to picking up subtle sentiment cues, it helps businesses understand customer pain points and joys at scale. This deep dive into customer emotions is invaluable for refining products and services.

A Versatile Conversational Partner

It can also read between the lines during conversations to aptly adjust its tone and manner to suit the user. For example, a customer responding to a light-hearted ad campaign would receive responses with the same zeal, whereas a customer reporting fraud would trigger the AI to keep its demeanor serious and succinct.

3. Auto-Generating Customer Replies

Example: Salesforce Einstein AI

Generative AI understands customer intent. That capability sits at the core of many new customer service use cases for the technology, such as auto-generating customer replies. Indeed, GenAI applications, like Service GPT by Salesforce, can do this by first understanding the customer query and sieving through various knowledge sources looking for the answer. These knowledge sources likely include:

Web links

Knowledge base

Other customer databases (which may also allow for personalization)

Human Oversight: A Safety Net for AI

In trawling these, GenAI automates a relevant customer response, which the agent can evaluate, edit, and forward to customers. That final part is crucial: keeping a human in the loop lowers the risk of responding with incorrect information and protects service teams from GenAI hallucinations.

4. Assisting Agents as They Type

Before LLMs burst onto the scene, many people played with generative AI when using tools like Gmail. Indeed, the email tool predicts how a sentence will likely end, and if it guesses right, the user can hit the “tab” button, and it’ll complete their message.

AI-Powered Response Enhancement

Embracing the advent of large language models (LLMs), Zendesk built a customer service version of this – on steroids. Its “expanding agent replies” solution allows agents to type the bare bones of their response and then fleshes it out for them, saving them time in responding to customers across digital channels.

Again, the contact center must plug the solution into various knowledge sources for this to happen, as is the case across many other use cases, and an agent stays in the loop.

5. Automating Note Taking

Agents often receive a barrage of information, which they must remember. Yet, keeping track of all the critical details is tricky. As such, many supervisors encourage note-taking. Nevertheless, even that can impede an agent’s ability to engage in active listening as they multitask, resulting in increased resolution times.

Sprinklr’s “call note automation” solution aims to overcome this issue by jotting down crucial information as the customer talks. Agents can refer to these golden nuggets of information when forming their replies instead of relying on the unstructured, bulky transcript. Knowing this, they can stay focused on what the customer is saying and not try to remember what they said previously, which should improve their call handling.

6. Unearthing Customer FAQs

As generative AI monitors customer intent, many vendors have built dashboards that track the primary reasons customers contact the business and categorize them. With this information, contact centers can understand their primary demand drivers. This enables the service team to prioritize actions to improve contact center journeys. These actions may include:

Improving agent support content

Solving upstream issues

Adding conversational AI

Google Cloud’s Generative FAQ for CCAI Insights allows contact centers to upload redacted transcripts to unlock this capability. The tool may also generate conversation highlights, summaries, and a customer satisfaction score to store in the CRM.

7. Automating Post-Call Processing

When a service agent ends a customer interaction, they must complete post-call processing. That typically involves uploading a contact summary and disposition code to the CRM system. Generative AI solutions can now automate this process, shaving seconds from every contact center conversation and saving significant service operation resources.

CCaaS Magic Quadrant leader Genesys is one vendor offering such a solution. After each conversation, it automates these post-call processes for agents to review, tweak, and publish in the CRM. The vendor standardizes the format of each conversation summary, making it easier for future agents handling follow-ups to understand what happened on the previous call.

8. Simplifying Call Transfers and Escalations

When a contact escalates, the customer must often repeat their problem and the information they shared with the first agent, a common source of customer frustration. Yet, generative AI can help by summarizing the contact so far and sending that to the second support agent or supervisor. As a result, they can continue the conversation from where it broke down, saving time and preventing the customer from repeating themselves.

The Verint Interaction Transfer Bot does precisely that. Whether the first agent is a human or a bot, it sends a quick, informative summary rather than an unwieldy transcript.

9. Detecting Customer Service Automation Opportunities

Generative AI helps contact centers spot opportunities for conversation automation by uncovering customer FAQs. Yet, AI Insights by Five9 takes this further. The solution takes customer conversations and groups them by various traits, like intent. From there, GenAI and NLP are applied to search for patterns within these groups of contacts, suggesting process and automation improvement opportunities.

In doing so, the tool indicates how often these opportunities present themselves and the possible cost-savings the contact center can make by acting on them.

10. Developing Quality Assurance Scorecards

Example: The Verint Quality Template Bot

After years of call and contact monitoring and CSAT/sentiment analysis, experienced team leaders and quality analysts understand what an excellent customer conversation looks like. Nevertheless, transferring that knowledge into specific, measurable, and fair quality assurance (QA) scorecard criteria is easier said than done, not to mention time-consuming.

GenAI can help here via solutions like the Verint Quality Template Bot. With this, a QA leader can input simple prompts on what a top-notch customer-agent interaction looks like on a specific channel. Then, the GenAI-infused solution will scour historical contacts to spot phrases and behaviors that reflect those prompts on the channel to auto-build a QA scorecard.

The QA team can review, edit, and finalize that scorecard before repeating the process across other channels (and perhaps specific customer intents).

11. Adding Context to Automated Quality Scoring

Example: Manager Assist for Amazon Connect

Many CCaaS providers now offer the capability to automate quality scoring, giving insight into all contact center conversations. Generative AI takes this further by automating not only “what happened” questions (e.g., “Did the agent say this or do that?”) but also additional criteria.

The Manager Assist for Amazon Connect solution does this by harnessing GenAI to auto-fill more subjective scorecard criteria such as:

Did the customer leave the call satisfied?

Did the agent offer any concessions?

The solution provides a rationale for the automated answer in case quality analysts, supervisors, or coaches wish to delve deeper or an agent wants to challenge it.

12. Pinpointing Agent Coaching Opportunities

Alongside auto-filling more of the quality scorecard (as above), Manager Assist for Amazon Connect provides an automated agent performance summary for every customer conversation. That summary includes coaching and positive recognition opportunities.

Indeed, in an example that AWS shared, the solution-generated feedback such as: “The agent could have been more proactive in offering a discount or resolution earlier rather than waiting for the customer to ask. Taking initiative shows commitment to fixing the issue.”

“The agent did a good job apologizing and taking accountability for the website issues. Expressing empathy for the customer’s frustration is important.”

The innovation also inspires cooperation between quality assurance and coaching teams, who can create a connected learning strategy to bolster agent performance.

13. Alerting Supervisors to Agent Issues

Generative AI unlocks several chances to turn insight into action, including insights that conversational intelligence tools uncover. For instance, NICE uses such tools to detect customer sentiment in real-time.

The Forrester Wave CCaaS leader then applies GenAI to monitor the trend in sentiment and alert the supervisor when it drops significantly. They may then swoop in and save the day. Alongside sentiment, contact centers may harness GenAI to alert supervisors when an agent demonstrates a specific behavior and jots down customer complaints.

14. Measuring Customer Moods, Not Just Sentiment

Many contact center providers offer the capability to score conversations via sentiment. Some may even share insight on how that sentiment has changed over time, so contact centers can decipher (across intents) what is driving positive or negative emotions.

Nifty, but GenAI has allowed vendors to go further. Instead of tagging emotions as positive, negative, or neutral, GenAI-powered sentiment solutions (such as Mood Insights by Talkdesk) capture more specific feelings like:

Frustration

Gratitude

Relief

With this insight, brands can examine how their agents evoke various emotions and uncover new best practices for coaching the agent population.

15. Translating Live Customer Calls

There are many solutions for translating customer chats and messages in real time. Yet, the options for voice have remained particularly limited. Why? They leverage speech-to-text to create a transcript from the customer’s audio. The transcript then passes through a translation engine to pass a written text translation to the agent's desktop. From there, the agent types out a response, which plays out through a text-to-audio stream.

Technically, this works, and agents and customers can engage in phone conversations while speaking different languages. But, the whole process of having agents type out their replies takes time.

The Promise of Real-Time Voice Translation with AI

Those prolonged periods of silence are a real rapport killer. Enter generative AI, which promises real-time voice translation. OpenAI demonstrated earlier this year how this works using ChatGPT, as shown below.

Language Translation in Contact Centers

Unfortunately, there are no purpose-built solutions for contact centers yet. Still, Google has pledged to make such a feature available on its Google Contact Center AI Platform soon. Undoubtedly, it will leverage Gemini, not ChatGPT.

16. Modifying Agent Accents in Real-Time

Background noise cancellation specialists, such as Sanas and Krisp, generate much of their business in customer service and have long sought ways to bolster their tech stack to increase their presence in contact centers.

Accent Modification

GenAI has given them the answer. Take Sanas as an example. It has paired the technology with its audio processing capabilities to turn English spoken with a heavy accent into non-accented American English. Already, 12 of the top 20 customer service BPOs have leveraged the solution, reportedly cutting agent attrition by up to 50 percent.

Ethical Implications of AI-Powered Accent Modification

Elsewhere, a Japanese telecoms provider is trialing similar software that modifies the tone of irate customers. Both use cases have merit, although ethical concerns have been raised about both, especially accent translation. Critics have stated that the tech is “playing into racism.”

17. Spotting Gaps in the Knowledge Base

To automate customer queries, GenAI-based solutions drink from various knowledge sources. Typically, the contact center knowledge base is the most predominant. Yet, sometimes, there is no knowledge article for the solution to leverage as the basis of its response.

When this happens, it may flag the knowledge base gap to contact center management, which can then assess the contact reason and create a new knowledge article. As a result, the GenAI application and live agents during voice interactions have something to work from, enhancing the contact center’s knowledge management strategy.

18. Generating Knowledge Articles

Some GenAI solutions can create new articles to fill gaps in the knowledge base (as above). CustomerAI by Twilio is an example of such a solution. It understands customer intent, assesses how agents and supervisors have successfully handled such queries, and uses that information to develop a new knowledge article.

Enhancing Knowledge Base with AI

A service team may then have a supervisor or experienced agent assess the knowledge article, edit it, and publish it in the knowledge base to keep a human in the loop. Another advantage of these auto-generated articles is that they’re in the same format, allowing agents to comprehend and action them quickly.

19. Simplifying Self-Service and Bot-Building Activities

Generative AI is changing low-/no-code solutions. It allows users to build applications using natural language instead of drag-and-drop tooling. That will impact many aspects of customer service, and chatbot development offers an excellent early example.

Rapid Bot Development with GenAI

Consider the Generative App Builder embedded into Google’s CCaaS solution: The Contact Center AI Platform. It allows contact centers to build bots in minutes. Indeed, in natural language, the developer can explain what information the bot should collect, its tasks, and the APIs it needs to send data. Then, the platform spits out a bot, which the business can adapt and deploy in its contact center.

20. Increasing the Scope of Conversational AI

Nuance was one of the first vendors to add ChatGPT to its conversational AI platform. It harnessed the LLM in such a way that if a virtual agent receives a question it hasn’t had training to handle, generative AI provides a fallback response.

AI-Powered Knowledge Base Access

The weblinks and contact center knowledge sources that the conversational AI platform integrates with inform the response, helping to automate more customer queries. Alongside the answer, the GenAI-powered bot cites the sources of information it leveraged, which the customer can access if they wish to dig deeper.

21. Keeping Self-Service Interactions on Track

Automated customer service interactions sometimes break down when customers change their intent halfway through a conversation, confusing the virtual agent. The Conversation Booster by Nuance uses generative AI to combat this issue as users carry out self-service tasks within the bot. These may include:

Making payments

Scheduling appointments

Updating their personal information

Indeed, the bot detects the intent change and presents a message to refocus the customer, pull the conversation back on track, and improve containment rates.

22. Simulating Conversations for Bot Testing

Like Nuance and Google, Cognigy has pushed the boundaries of generative AI innovation in customer service, as its “Conversation Simulation” tool exemplifies. The tool bombards virtual agent applications with mock customer conversations to test how well the bot supports various inputs.

Pairing this with the Cognigy Playbooks reporting platform, service teams can:

Verify bot flows

Validate outputs

Add assertions

According to a 2023 Gartner report, the Customers’ Choice conversational AI vendor defines an “assertion” as the conditions a bot must meet to pass a test.

23. Building Customer Surveys

It’s easy to stew over what to include in a customer survey. Yet, GenAI can support the ideation process. Consider the Sprinklr Surveys solution. It leverages:

Strategy documents

Brand guidelines

Other assets to build customer questionnaires for review in seconds

From there, Sprinklr customers may harness the provider’s omnichannel capabilities to distribute these surveys, converge the data, and (again, using GenAI) analyze the feedback.

24. Extracting Insights from Customer Feedback

LLMs can:

Consume large amounts of information

Strip away trends

Convert those into concise, structured takeaways

InMoment’s Smart Summary Generator does this for customer feedback. Indeed, the GenAI-powered solution first ingests various sources of such feedback, including:

Surveys

Conversation transcripts

Online reviews

From there, it generates an overview of trends. If a contact center can continuously feed such a solution with knowledge sources, it can monitor customer complaints and act quickly to foil emerging issues.

25. Predicting a Customer’s Net Promoter Score

The Net Promoter Score (NPS) is a common customer experience metric, typically tracked in the contact center. To calculate a company’s NPS, the customer service team asks the customer: “On a scale of one to ten, how likely are you to recommend our business to a friend or colleague?”

Depending on their answer, the contact center puts the customer into one of three categories:

Promoters

Detractors

Neutrals

It also uses their score to calculate an overall NPS and benchmark performance. That metric brings significant benefits from segmenting customers to gauging customer loyalty. Nevertheless, there are drawbacks. For instance, only a small sample of customers will respond to the NPS survey.

Also, customers don’t like filling in surveys; they generally prefer low-effort experiences. That’s why Evaluagent has launched a GenAI-powered solution that analyzes a customer’s contact center conversation before predicting what score they would have left if asked the NPS survey. While the solution is in beta, the contact center QA provider believes the results are “promising” when tested against real-life NPS data.

26. Defining Troubleshooting Steps

Generative AI can assimilate the resolution ideal path by assessing successful conversation transcripts across a particular customer intent. Flow Modelling by Cresta offers such a solution, determining this path based on its impact on various customer experiences and business outcomes.

The capability uncovers the characteristics that lead to successful resolutions. These may inform agent coaching and scorecard creation initiatives.

27. Augmenting Search Functions

With generative AI, search engines can auto-generate answers to written questions. That functionality may impact several customer service applications, such as the knowledge base.

When an agent types in a question, the answer can pop up, so the agent doesn’t have to trawl through articles and documents to find it. Such a capability may also bring new customer service possibilities to life.

The Industry Benchmarks by NICE is an excellent example of this. At its heart, the solution contains a wealth of anonymized contact center conversation data that NICE has gathered and used to develop sector-specific benchmarks for many metrics. Such metrics include:

Customer sentiment

Call reasons

Automation maturity

And more

Upfront, the vendor installed a GenAI-infused search engine so service teams can see how they compare to the competition by simply entering a few written prompts.

28. Responding to Reviews with Generative AI

Responding to customer reviews promptly and appropriately is crucial for maintaining a positive brand image. When customers take the time to leave a review, they’re providing valuable feedback about their experiences with your business. Responding to these reviews shows you value their input and demonstrates your commitment to customer satisfaction.

Personalized Customer Responses with AI

Generative AI enables automated responses to customer reviews, ensuring timely answers while freeing valuable time for customer service agents. It doesn’t just churn out generic responses but uses the information in the review to generate a personalized response. For example, a customer praises your fast delivery service.

Leveraging AI for Effective Review Management

The AI system could respond by expressing gratitude for their positive feedback and reinforcing your commitment to maintaining this efficiency level. “Thank you for recognizing our fast delivery service! We’re thrilled to hear you had a great experience. Your feedback encourages us to continue working hard to ensure all our deliveries are as prompt as yours.”

If a customer complains about a faulty product they received, the AI could express regret for their negative experience and assure them that immediate steps are being taken to resolve the issue.

“We’re truly sorry to hear about the faulty product you received. We understand the disappointment it has caused, and we’re taking immediate action. Our team is arranging for a replacement to be sent to you as soon as possible, and we’re reviewing our quality control processes to prevent similar issues in the future.”

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How to Implement Generative AI in Customer Service

1. Define What You Need to Achieve

Start by laying out a blueprint of your business goals. This clarity guides your AI strategy and helps measure its impact. Are you aiming to speed up responses, or are you focusing on enhancing customer self-service?

To track progress, select appropriate KPIs like:

Customer response time

Customer satisfaction ratings

Self-service completion rates

Establish a testing timeline for implementing and evaluating the AI system, including milestones and review points. Research and benchmark your goals against industry standards to ensure they are competitive and achievable.

2. Gather and Analyze Customer Service Data

Your current customer service data is a sea of insights. It reveals the what and how of customer interactions, forming a baseline upon which to improve. Examine your service logs to understand common queries and response times.

Conducted sentiment analysis of customer feedback to identify pain points. Interview your support team for firsthand insights into frequent customer challenges.

3. Choose the Right AI Tool

Selecting the appropriate AI tool is akin to finding a key member for your team. Like an eCommerce company thrives with an AI adept at analyzing purchase histories and campaign interactions to personalize shopping, a tech company needs an AI adept at combining knowledge-based articles to retrieve technical information.

Selecting the Right AI Solution for Your Business

Your generative AI must make sense of your business's unique pulse. Assess whether pre-built AI solutions from major platforms align with your needs or if a custom-built API is more suitable. Evaluate integration capabilities with your current customer service software. Choose an AI solution that can scale with your company, handling increased data and complexity over time.

4. Upload and Categorize Datasets

Training your AI with relevant data is akin to providing a rich learning experience. The more it knows, the better it assists. To provide a solid training base, collect data from diverse customer engagements, including:

Chat logs

Email exchanges

Call transcripts

Categorize data for context in buckets like:

Complaint resolution

Product inquiries

Billing questions

Scrub the data of personally identifiable information to maintain customer privacy and comply with data protection regulations.

5. Program and Train AI Models

This is where your generative AI begins to adapt to a specific customer service scenarios:

Choose a Suitable Neural Network Model

Select a transformer, recurrent neural network, or another suitable model for foundational AI training.

Include Diverse Interaction Styles

Integrate a variety of interaction styles in the training data to prepare the AI for different customer temperaments and inquiry types.

Engage in Multiple Training Rounds

Progressively add more complex and varied data sets to refine the model’s understanding.

Continuously Test with New Data

Regularly assess the AI's understanding and response accuracy by testing it with

new, unseen data.

6. Test and Refine

Think of this phase as the dress rehearsal before full deployment. Testing the AI in a controlled setting helps identify and rectify any issues. Deploy generative AI in a controlled environment allows for issue identification and resolution before it goes live.

To implement generative AI in customer service:

Deploy to a Control Group

Start with internal support agents or a limited customer segment for initial testing.

Close Monitor Performance Metrics

Track response times and accuracy to ensure the AI meets service standards.

Rigorously Assess Contact Center Metrics

Evaluate key metrics to confirm readiness for broader deployment.

This careful approach helps ensure the AI’s performance aligns with your service goals.

7. Implement and Seek Feedback

Deploying generative AI in customer service is not the end but the beginning of an ongoing journey. Be rigorous in getting cold, hard performance feedback from your direct users. Actively seek feedback via customer surveys:

What are they loving?

Where are they facing issues?

Use these insights to tweak and polish your AI. Use conversation analytics to uncover unreported but trending sentiments about your AI’s performance in customer speech.

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