Customer service is evolving, and AI is leading the charge. 

This article explores how companies are using AI in customer service to handle more customers without breaking the bank on new hires, and how it's turning frustrating service ordeals into hassle-free experiences.

We'll explore the future of AI in customer service and address the challenges. Whether you're a B2B SaaS leader or just someone who's tired of being on hold, this read might just change how you see your next customer service experience.

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AI and Customer Service

As generative AI becomes easier and more accessible to use, more company leaders across the scale of companies are taking notice.

A recent study found that 79% of business leaders expect AI to change how they handle customer service in the next three years. They're particularly excited about 6 main benefits:

1. Reduction in average resolution time cycles 
2. Increase of CSAT scores
3. Getting more work done faster
4. Saving money
5. Handling increased customer requests and demands without the need for growing the customer service team
6. Better managing attrition in their customer support teams by using AI to onboard team members faster and cut down on training periods.

With capabilities like case summarization, sentiment tracking, and customer intent modeling, These out-of-the-box LLM models layered with Gen AI have removed the need for extensive NLP engineering. 

Gartner's latest report underscores this trend. Out of 1,400 surveyed leaders, 45% are already piloting Gen AI projects, and 10% have moved beyond testing to implement Gen AI solutions in their day-to-day operations. 

Notably, customer service emerged as a top use case, with 41% of respondents reporting Gen AI implementations in marketing, sales, or customer service by the end of 2023.

Traditional Approach to Customer Service 

Contact centers have long been the heart of customer service operations. For years, they've relied on a mix of technologies to boost agent productivity:

1. Interactive Voice Response (IVR) systems
2. Agent assist tools
3. Robotic Process Automation (RPA)
4. Basic chatbots

While these technologies have helped, they haven't fully lived up to their promise. 

Robotic voices of IVRs often disappoint customers. Despite efforts to manage limited agent bandwidth, the call abandonment rate has shown little improvement. While escalations due to delayed hand-offs have decreased, they still account for a significant portion of interactions. 

However, not all is in vain. With simpler tasks becoming automated, contact center workers have found themselves taking on new, more complex roles - like teaching customers how to use digital services

Large Language Models (LLMs) and Generative AI (Gen AI)

Large Language Models (LLMs) are expanding the boundaries of what's possible in automation. These sophisticated AI models are trained on vast datasets, allowing them to:

1. Recognize and classify text and speech
2. Generate human-like responses
3. Perform tasks with unprecedented speed and accuracy‍

As more investment pours into Generative AI (Gen AI), more powerful AI algorithms can be built with increased computing power and more affordable cloud infrastructure. This shall open doors to innovative applications in customer service that were once thought impossible.

Customer service isn't just about solving problems—it's about creating experiences that keep customers coming back. 

From improving workforce engagement management to creating more sophisticated conversational AI, Gen AI is touching every facet of customer service.

Let's explore some AI customer service use cases that are becoming prevalent in 2024

7 Use Cases of AI in Customer Service [Gen AI in 2024] 

Auto-generate customer replies via chatbots or emails 

AI customer support is now creating highly personalized responses in real-time with generative AI. These aren't just template-based replies; the AI model analyzes the customer's query, tone, and history to craft unique, context-appropriate messages.

For example, if a customer writes in about a delayed order with a frustrated tone, the AI can generate an empathetic response, offer a specific solution, and maybe even include a discount code as a goodwill gesture. 

It handles everything from quick chat questions to complex email replies, speeding up response times while keeping the interactions personal and human.

Automate note taking and transcription 

Gone are the days of manual note-taking during customer calls. AI in customer service is now listening in, transcribing conversations, and creating structured summaries in real-time. But it goes beyond simple transcription.

The AI categorizes issues, highlights action items, and even picks up on emotional cues. 

For instance, if a customer mentions they're "really disappointed" with a product, the AI flags this sentiment for follow-up. This human-like note-taking ensures that any agent can quickly get up to speed on a customer's history, eliminating the need for customers to repeat their stories.

Generating dynamic FAQs and spotting gaps in knowledge base 

Gen AI is turning static FAQ pages into dynamic, ever-evolving knowledge bases. By analyzing customer queries across various channels, the AI identifies gaps in the existing information and automatically generates new entries. It also updates existing answers to make them clearer or more current. This ensures that customers always have access to the most up-to-date and relevant information.

Identifying agent training opportunities 

By analyzing thousands of customer interactions, Gen AI is uncovering nuanced insights into agent performance and training needs. It can identify patterns where agents excel or struggle, even picking up on subtle cues like tone of voice or time taken to resolve specific types of issues. 

For instance, the AI might notice that while an agent handles billing queries efficiently, they take longer with technical problems related to a particular product. This allows managers to create targeted, personalized training programs, improving overall service quality.

AI-powered Search 

AI search is now being preferred over traditional search algorithms within customer service platforms because it understands context and intent, not just keywords. When a customer or agent searches for information, the AI can pull relevant data from multiple sources - customer history, product databases, current promotions, and more, to provide comprehensive answers in real time.

Automate Workflows

Customer service agents often spend a significant portion of their time on repetitive tasks like data entry, ticket categorization, and routing issues to the right department. GenAI can categorize and route tickets based on content, auto-fill forms based on conversation context, and even draft responses for agent review. This automation reduces the cognitive load on agents, allowing them to focus more on complex problem-solving and building customer relationships.

Virtual Assistants for Complaints/Query Handling

Quick query resolution is crucial for contact center agents, but with limited bandwidth, especially in high-volume industries like hospitality and financial services, scalability becomes a challenge. 

Virtual Assistants can integrate with various bank systems to handle routine tasks such as blocking a card, sending statements, or resetting passwords. It can also take up calls on behalf of the agents, understand the context and convey resolution steps in a complete human-like manner. 

By offloading these tasks, agents are free to focus on queries that require their attention, leading to improved First Contact Resolution (FCR) and reduced Average Handle Time (AHT).

The momentum doesn't stop there. A BCG survey revealed that an overwhelming 95% of global customer service leaders expect AI chatbots to play a role in customer interactions within the next three years.

How Organizations Can Benefit from GenAI in Customer Service

Handle large volumes of data without proportional costs

Problem: During peak seasons or unexpected events, customer inquiry volumes can skyrocket.

For example, an e-commerce site might see inquiries triple during holiday sales, or an airline might face a deluge of questions during travel disruptions. Traditionally, this meant either hiring temporary staff (expensive and time-consuming) or leaving customers frustrated with long wait times.‍

Benefit: GenAI can scale instantly to handle these surges. It can process thousands of inquiries simultaneously, providing immediate responses to common questions, and intelligently routing complex issues to human agents. This helps with higher Contact Deflection Rate and better Agent Utilization Rate. 

Deliver personalized experiences and differentiate through superior customer service

Problem: Customers expect personalized service, but delivering this at scale is challenging. Customers often feel frustrated by having to repeat their information or receiving generic responses that don’t account for their history. At the same time, as products and prices become increasingly similar across competitors, companies find it difficult to differentiate and retain customers based solely on what they sell.

Benefit: GenAI analyzes a customer's history—past purchases, preferences, and communication style—to tailor each interaction across all channels (chat, email, phone, social media). Whether it’s offering a goodwill gesture to a long-time customer or adjusting language to match expertise, this personalized, high-quality experience not only improves CSAT and NPS but also becomes a key differentiator, driving loyalty even when products or prices are similar.

Enhance agent efficiency and provide 24/7 support 

Problem: Customers increasingly expect round-the-clock support, but staffing a 24/7 contact center is expensive. Meanwhile, during business hours, agents often get bogged down with repetitive queries, reducing their availability for complex issues. 

Benefit: GenAI can provide instant 24/7 support for routine inquiries, eliminating round-the-clock staffing costs. By handling repetitive tasks, companies can handle increased customer requests and demands without the need for growing the customer service team.‍

Metrics impacted:

• Agent Efficiency:
  • Better average response time
  • Improved first contact resolution
  • Reduced average handling time
• Overall Performance:
  • Lower ticket volume
  • Shorter resolution cycles

Lower training and onboarding expenses 

Problem: Training new customer service agents is time-consuming and expensive, especially in industries with high turnover. New hires often take weeks or months to become fully productive, and in the meantime, they may provide inconsistent service. 

Benefit: AI serves as a virtual teaching assistant for new hires. It can provide instant answers to product questions, suggest appropriate responses, and guide agents through complex processes. 

This helps new agents become fully productive in less time, and improves service consistency, leading to higher CSAT scores and reduced error rates even among less experienced staff.

Centralize and integrate data for better insights 

Problem: Customer data is often siloed across different systems - CRM, support tickets, billing, etc. This makes it difficult for agents and managers to get a complete picture of a customer's history and needs. 

Benefit: AI agents or AI assistants can integrate data from multiple sources in real-time, providing a 360-degree view of each customer. During interactions, it can pull relevant information from all connected systems, giving agents a complete context for each customer. 

For managers, it can analyze trends across all these data sources, providing deeper insights into customer behavior, common issues, and opportunities for improvement.

How are other organizations using this to their advantage?

Companies that use AI-generated customer support are seeing measurable improvements. Let's look at some examples of AI in customer service

1. Delta Airlines' Ask Delta chatbot 

Delta's GenAI-powered chatbot helps customers with various tasks like check-in, bag tracking, and flight searches. This implementation has significantly reduced call center volumes by 20%, showcasing how AI can handle routine inquiries efficiently, freeing up human agents for more complex issues.

2. Octopus Energy's AI-drafted emails 

Octopus Energy utilizes GenAI to quickly draft detailed email responses to customer inquiries. Interestingly, these AI-generated emails have led to an 18% increase in customer satisfaction scores compared to human-written responses. This allows their agents to focus on more intricate customer issues while maintaining high satisfaction levels for routine queries.

3. Call Summarization for Escalations 

Many companies are implementing 'Transfer Bots' that summarize customer interactions before transferring calls to another agent. This eliminates the need for customers to repeat their issues, saving time and reducing frustration. It's a prime example of how GenAI can enhance customer experience by streamlining communication within the support process.

Successful implementation of AI in customer service requires a strategic, phased approach tailored to your specific environment/ business needs. 

Future of Gen AI in Customer Service

Before discussing the future, it's crucial to understand the current limitations of Gen AI:

1. Factual Inaccuracies: The quality of GenAI outputs depends heavily on its training data. There's a risk of providing inaccurate information with high confidence, which can be particularly dangerous in customer service scenarios.
   
2. Inherent Bias: Even when fine-tuned for specific contexts, GenAI models may inherit biases from their foundational training data, potentially leading to unfair treatment of certain customer groups. This becomes a crucial consequence in the finance industry, particularly across equity/investment groups.
   
3. Data Security: There's a risk that GenAI could inadvertently reveal proprietary information, intellectual property, or sensitive customer data. For industries like banking, insurance /healthcare - data security is paramount and the risks that come with AI can be a dealbreaker for many companies.
   
4. Lack of True Understanding: While GenAI can generate human-like responses, it doesn't truly understand context or emotions, which can lead to inappropriate responses in sensitive situations.

Currently, the most effective risk mitigation strategy is maintaining human oversight. Human agents should review AI-generated content before it reaches customers, ensuring accuracy and appropriateness.

The evolution of GenAI in customer service is expected to follow this trajectory:

1. Supervised complex query handling: Gen AI will handle increasingly complex queries with human-like responses but under human supervision, similar to a junior agent being mentored by a senior agent.
   
2. Proactive Problem Solving: As it advances, Gen AI will start to identify and solve potential issues before they arise, much like a smart home system preventing breakdowns.
   
3. Personalized AI Assistant: Ultimately, GenAI could evolve into a fully personalized assistant, akin to a concierge who knows customer preferences and anticipates needs, delivering a human-like experience with minimal human intervention.

As GenAI becomes more reliable, the need for human oversight will decrease. However, companies must ensure that:

1. Their AI systems are built on accurate, unbiased, and secure data.
2. The "human touch" and empathy are maintained as the technology matures.
3. They stay aware of and adapt to customers' evolving familiarity and expectations with AI technology.
4. Ethical considerations are at the forefront of AI implementation, ensuring fair and equitable treatment of all customers.
5. There's a clear escalation path to human agents for situations where AI isn't sufficient.

The future of GenAI in customer service is promising, but it requires careful implementation and continuous refinement to truly enhance the customer experience while maintaining trust and ethical standards.

But the potential here is huge. 

If you're curious about how this AI revolution could work for your business, why not take a look at what Fluid AI can do? It might just give you some ideas on how to stay ahead in this fast-changing world of customer service. After all, seeing is believing, right?

Book your Free Strategic Call to Advance Your Business with Generative AI!

Fluid AI is an AI company based in Mumbai. We help organizations kickstart their AI journey. If you’re seeking a solution for your organization to enhance customer support, boost employee productivity and make the most of your organization’s data, look no further.

Take the first step on this exciting journey by booking a Free Discovery Call with us today and let us help you make your organization future-ready and unlock the full potential of AI for your organization.