The Impact of AI on Customer Service

Artificial intelligence (AI) drives major improvements in customer service efficiency and personalisation, meeting demands for instant, multi-channel support. Gartner forecasts that 80% of customer service organisations will implement generative AI by 2025, representing remarkably swift adoption.

AI helps businesses transcend traditional service model constraints while creating substantial value. Through AI-powered transformations, sectors like global banking could potentially approach a trillion dollars. Increasing customer preference for AI interactions further propels this evolution of service delivery.

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AI in Customer Service: A Balanced Approach

Research shows that 72% of business leaders (Hubspot) believe AI can outperform humans in certain customer service tasks. However, how do you make the most of AI tools? To transform customer service, it is not simply a case of deploying the latest technology. Transformative results come from strategic integration and putting people first rather than technology alone.

Forrester identifies AI’s value in enabling proactive service by predicting customer issues before they occur. Potentially this will shift contact centers from cost centers to profit centers through enhanced efficiency.

Meanwhile, Gartner advocates against viewing AI primarily as a replacement for human agents. Instead, they recommend a balanced approach where AI tools augment and empower human capabilities in customer service operations.

7 Key Use Cases of AI in Customer Service

The application of AI in customer service is diverse and continues to expand as the technology evolves. Several key use cases are currently prevalent across various industries.

1. AI-powered chatbots and Virtual Assistants

These AI-driven tools are becoming increasingly sophisticated, moving beyond simple rule-based responses to engage in more natural and context-aware conversations. They serve as a first line of support, efficiently handling frequently asked questions and routine inquiries.

By providing 24/7 self-service support, customers can get immediate assistance regardless of the time or location. This automation of initial customer interactions plays a crucial role in deflecting a significant volume of tickets away from human agents. This allows them to focus on more complex or sensitive issues.

Furthermore, AI chatbots can guide customers through troubleshooting processes, offering step-by-step solutions for common technical or product-related problems. Leveraging machine learning, these virtual assistants can also offer AI personalised product recommendations and assistance. They enhance the customer experience and potentially drive sales. The ability of AI to provide multilingual support and translation further extends the reach and effectiveness of customer service operations.

2. Call center transformation

Call centers today face significant operational challenges. Agent turnover rates vary between 18% and 25%, costing organisations approximately $14,000 per replacement. Meanwhile, each customer interaction on assisted channels averages $3.50. Artificial intelligence solutions offer compelling opportunities to address these pain points while improving both customer and agent experiences.

AI can transform call centers by implementing intelligent virtual assistants. These systems can handle routine inquiries such as order tracking, account information, and basic troubleshooting, freeing human agents to focus on complex issues requiring empathy and critical thinking. By integrating with company knowledge bases and customer data through APIs, AI assistants provide personalised, accurate responses without the wait times typically associated with human support.

How AI Inference Costs Are Reshaping Call Center Economics

The economics are increasingly favourable as inference costs plummet with each new AI model generation. With innovations like NVIDIA’s Blackwell GPUs promising 30x performance improvements, previously cost-prohibitive AI implementations are becoming accessible to more organisations. These technologies not only reduce the per-interaction cost but also improve agent retention by eliminating repetitive tasks that contribute to burnout and turnover.

Also read: Improve NPS and save time with an AI-powered Call Center

3. Human Agent Assistance

AI interacts directly with customers and plays a vital role in empowering human agents to perform their tasks more efficiently and effectively. AI-powered tools provide agents with real-time access to relevant information and knowledge, acting as virtual assistants that can quickly surface the answers needed to address customer queries.

These systems can also suggest appropriate responses and next-best actions during customer interactions, ensuring consistency and speed in communication. By automating tedious tasks such as note-taking and post-call processing, AI reduces the administrative burden on agents, allowing them to devote more attention to the customer interaction itself.

Moreover, AI can summarise lengthy customer conversations and ticket histories, providing agents with a quick understanding of the context without requiring them to read through extensive transcripts. Advanced AI capabilities can even analyse customer interactions to identify opportunities for agent coaching and improvement, contributing to the overall quality of service.  

4. Sentiment Analysis and Customer Insights

AI technologies can analyse customer interactions across various channels to understand their emotions and satisfaction levels. By identifying the key reasons for customer contact and pinpointing common pain points, AI provides businesses with valuable insights into the drivers of customer behaviour.

This data-driven understanding of customer preferences and sentiments enables organisations to tailor their service strategies, proactively address potential issues, and ultimately enhance customer loyalty.  

For example, the Verint Quality Template Bot uses this technology to improve quality assurance processes by analysing past conversations and creating QA scorecards based on simple prompts. This approach saves QA teams time reviewing and finalising these AI-generated scorecards across different communication channels. Companies can develop more effective quality measurement tools by combining AI analysis with human oversight for better customer experiences.

5. Automated Ticket Management

AI helps you manage customer support tickets more easily. AI can automate the creation of tickets from various communication channels, ensuring that all customer inquiries are captured and tracked efficiently.

Intelligent routing capabilities ensure that tickets are directed to the most appropriate agents or teams based on factors such as the nature of the issue and agent expertise, leading to faster resolution times. Furthermore, AI can automate the tagging and categorisation of tickets. It improves organisation and facilitates better tracking of trends and performance.  

6. Knowledge Base Management

Maintaining an up-to-date knowledge base is essential for both customer self-service and agent support. AI tools are now being used to assist in writing and updating knowledge base articles and frequently asked questions (FAQs), ensuring that the information is accurate and readily available. 

AI can also analyse the performance of existing knowledge base articles, identifying content gaps and suggesting new articles based on real-time service data, thereby enhancing the effectiveness of self-service resources.  

7. Personalised and Proactive Support

Leveraging the vast amounts of customer data available, AI enables businesses to tailor support interactions to individual customer needs and preferences. For a deeper understanding of how to implement this, read our comprehensive guide to AI hyper-personalisation, complete with real-world examples.This personalisation extends to anticipating customer needs and proactively offering solutions before issues even arise.

AI-driven systems can also send personalised communications to customers based on their specific profiles and past interactions, fostering a sense of being understood and valued. This shift towards proactive and personalised support enhances customer loyalty and overall satisfaction.  

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Devoteam’s Use Cases

AI in Customer Service: Platform Providers

Feature	AWS	Google Cloud	Microsoft Azure	ServiceNow
Platform/Suite	Amazon Connect (AI-native), AWS CCI (add-on AI)	Google AI Customer Engagement Suite	Azure AI Services	Customer Service Management (CSM) with integrated AI
Key AI Offerings	Amazon Q, Contact Lens, Outbound Campaigns, CCI services (Lex, Transcribe, Comprehend, etc.)	Dialogflow CX/ES (Virtual Agents), Agent Assist, Conversational Insights	Azure OpenAI Service, AI Search, AI Speech, AI Language	Now Assist (GenAI), AI Agents, Virtual Agent
Core Functionality	Provides a full AI cloud contact center or integrates AI into existing systems.	Enhances customer interactions and agent performance via an AI suite.	Offers AI building blocks to create custom customer service applications.	Embeds AI directly into CSM workflows for automation & assistance.
Main Focus/Goal	Improve CSAT & agent productivity via integrated AI features & analytics.	Improve CX, reduce costs, and empower agents across omnichannel engagements.	Enable flexible development of intelligent customer service solutions using various AI services.	Streamline workflows, enhance agent performance, and automate issue resolution within CSM.
Key Benefits	Reduced wait times, faster resolution, real-time agent assist, insights from conversations.	Consistent omnichannel experience, NLP, complex virtual agents, real-time agent guidance, operational insights.	Custom virtual agents, agent assist (transcription/analysis), post-call analytics, speech/language features, PII handling.	Case/chat summarization, reply generation, automated KB creation, AI search, autonomous resolution (AI Agents).

AWS: AI-Powered Solutions for Contact Centers 

AWS offers a suite of AI-powered solutions for contact centers, with Amazon Connect serving as its flagship AI-native cloud contact center platform. This platform integrates AI across all channels, providing features like Amazon Q in Connect, which assists both agents with real-time information and customers with intelligent self-service. Contact Lens provides real-time and historical analytics for quality management, while Outbound Campaigns enable proactive customer engagement.

For organisations with existing contact center infrastructure, AWS Contact Center Intelligence (CCI) solutions offer the flexibility to add AI capabilities without requiring specialised machine learning expertise.

These CCI solutions encompass:

• Self-Service Virtual Agents powered by services like Amazon Lex, Polly, and Kendra
• Real-time Call Analytics & Agent Assist utilising Transcribe, Comprehend, Translate, Kendra, and Chime
• Post-Call Analytics leveraging Transcribe Call Analytics and Comprehend

AWS’s offerings aim to enhance customer satisfaction by reducing wait times and improving resolution speed, boosting agent productivity by providing real-time assistance, offloading repetitive tasks, and gaining valuable insights from customer conversations.  

Google Cloud: Enhancing Customer Engagement with AI

Google Cloud provides a Customer Engagement Suite with Google AI, formerly known as the Contact Center AI Platform, designed to elevate customer interactions and optimise agent performance. This suite includes key AI products such as:

• Conversational Agents built on Dialogflow CX and ES, which enable the creation of intelligent virtual agents
• Agent Assist, which provides real-time guidance and automated responses to human agents
• Conversational Insights, which analyses customer interactions to provide valuable operational insights.

Google Cloud’s solutions aim to improve the overall customer experience, reduce operational costs associated with customer service, and empower agents with the tools and information they need to resolve issues more effectively. The platform delivers consistent omnichannel engagements across web, mobile, voice, email, and apps, supporting multimodal information and seamlessly integrating with existing telephony systems, CRM, and workforce management applications.

Key features include:

• Natural language processing for understanding and responding to customers
• Virtual agents capable of handling complex queries
• Real-time assistance for agents
• Automated responses to common inquiries
• Analytics tools for gaining deeper insights into customer interactions

Microsoft Azure: Intelligent Customer Service with AI

Microsoft Azure offers a range of AI Services that can be leveraged to build intelligent customer service applications. These services include:

• Azure OpenAI Service, which enables the development of custom generative AI solutions
• Azure AI Search, facilitating efficient knowledge retrieval
• Azure AI Speech and Language provides capabilities for building conversational interfaces and analysing customer interactions.

Azure AI facilitates various customer service scenarios, including the creation of virtual agents, the provision of real-time assistance to human agents through transcription and analysis, and the execution of post-call analytics to derive valuable insights from customer conversations.

The platform offers a set of features, such as real-time and batch speech-to-text conversion, text-to-speech capabilities for natural-sounding voice responses, speaker identification for verification, language identification for multilingual support, Personally Identifiable Information (PII) extraction and redaction for data privacy, conversation summarisation for quick context understanding, and sentiment analysis to gauge customer emotions.

Azure AI services are designed to integrate seamlessly with existing telephony systems and other Azure services, providing a flexible and powerful foundation for building customised customer service solutions.  

ServiceNow: Integrating AI into Customer Service Management

ServiceNow’s Customer Service Management (CSM) platform is deeply integrated with AI capabilities. It offers a suite of features designed to streamline workflows, enhance agent performance, and provide round-the-clock customer service.

Key AI functionalities include:

• Now Assist, which leverages generative AI to provide intelligent assistance to both agents and customers
• AI Agents, designed to autonomously resolve issues and perform tasks
• Virtual Agent, an intelligent chatbot that facilitates self-service and resolves common inquiries

Now Assist provides a range of capabilities, including the summarisation of cases and chats, the generation of reply recommendations for both email and chat, the automatic creation of knowledge base articles, and AI-powered search functionalities, all aimed at improving both agent productivity and customer self-service experiences.

The introduction of AI Agents signifies a move towards more proactive and automated customer service processes, with these agents capable of acting autonomously to resolve issues, make decisions, and interact with their environment under user control.  

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Looking Ahead: AI in Customer Service Trends for 2025

AI in customer service is dynamic, with several key trends anticipated to shape its evolution soon, particularly in 2025.

Dominance of Generative AI

Generative AI, with its ability to understand and generate human-like text, is poised to become a dominant force in customer service. Gartner predicts that by 2025, 80% of customer service organisations will leverage Generative AI to enhance their customer experience.

While currently positioned at the ‘Peak of Inflated Expectations,’ Generative AI is expected to mature rapidly towards practical and productive applications. Forrester also anticipates a substantial increase in the adoption of AI and automation in service, with generative AI playing an expanding role in providing faster and more human-like responses to customer inquiries.

Deloitte identifies generative AI as a key trend shaping 2025, highlighting its potential for product innovation, boosting customer service productivity, and transforming the customer experience through AI shopping assistants. These projections indicate a strong industry-wide belief in the transformative power of generative AI in revolutionising how businesses interact with their customers.  

Rise of AI Agents and Agentic AI

The evolution of AI in customer service is moving towards more autonomous systems capable of handling customer interactions from start to finish. Gartner identifies “AI Agents” as a high-potential use case, albeit with lower short-term feasibility. However, they anticipate that by 2029, agentic AI will be capable of autonomously resolving 80% of common customer service issues without any human intervention.

What are AI Agents? AI agents are intelligent systems designed to perceive their environment and take actions to achieve specific goals. AI Agents leverage artificial intelligence to understand user intent and execute tasks autonomously or with minimal human intervention.

Deloitte’s forecast suggests a more immediate adoption trend, with 25% of enterprises using generative AI expected to deploy AI agents in 2025, growing to 50% by 2027. ServiceNow also anticipates a rapid and significant impact from agentic AI on business operations.

Forrester envisions AI transforming customer service interactions into a collaborative partnership, where AI handles routine tasks, freeing up human agents to focus on complex issues requiring empathy and personalisation. This collective outlook suggests a future where AI agents play an increasingly prominent role in delivering customer service, from assisting human agents to operating more autonomously.  

Focus on Personalisation and Proactive Service

Understanding and catering to individual customer needs is becoming increasingly critical, and AI is a key enabler in achieving this. Gartner highlights customer personalisation as a “Likely Win” for AI in customer service, offering high value and feasibility. IBM emphasises AI-driven personalisation as a crucial trend, advocating for organisations to anticipate and meet customer needs even before an issue arises.

Forrester also points towards a trend of proactive, enterprise-wide service in 2025, where AI analyses data to identify patterns and predict potential issues, allowing businesses to offer solutions before customers even experience a problem. These trends underscore the growing importance of leveraging AI to create more tailored, relevant, and preemptive customer service experiences.  

Integration of AI Across Multiple Channels (Omnichannel)

Customers expect seamless and consistent interactions across all the channels they use to engage with a business. AWS emphasises the importance of seamlessly enabling AI across the entire contact center to deliver personalised experiences across preferred channels.

A reimagined AI-supported customer service model encompasses all touchpoints, including both digital self-service channels and agent-assisted options across various platforms. Industry reports further indicate that omnichannel customer experience will transition from being a desirable feature to an expected standard in 2025, with advanced AI-driven tools playing a central role in managing customer data holistically to ensure consistent and personalised interactions across all channels.

This signifies a move towards a unified and AI-powered approach to customer service, regardless of the chosen mode of communication.  

Emphasis on Trust, Privacy, and Responsible AI

As AI becomes more deeply integrated into customer service operations, the importance of trust, privacy, and responsible AI practices is gaining significant traction. Forrester predicts an increased focus on these aspects in 2025, as organisations strive to balance the benefits of personalised service with the need to protect customer privacy.

Zendesk outlines specific best practices for ensuring data security when using AI for customer service, including prioritising encryption and employing transparent algorithms. However, customer experience leaders recognise the potential for AI to erode customer trust, making the protection of trust and empathy a primary concern in AI implementations.

This growing awareness underscores the need for businesses to adopt ethical and secure AI practices to maintain customer confidence.  

Evolving Role of Human Agents

The advent of AI in customer service is not expected to eliminate the need for human agents entirely but rather to transform their roles. AI will augment the capabilities of customer service agents, enabling them to handle more intricate and complex customer issues with greater precision. AI will offload repetitive and predictable tasks, allowing human agents to concentrate on interactions that demand empathy and personalisation, potentially leading to a workforce of more highly skilled and trained professionals managing AI automation.

Industry reports indicate that a significant majority of contact center agents already perceive AI assistants as tools that enhance their abilities, helping them resolve issues more effectively and efficiently. This indicates a shift towards a collaborative model where AI and human agents work together, leveraging their respective strengths to deliver superior customer service.  

Challenges

Ensuring Trust, Privacy, and Security

As AI systems handle increasing amounts of sensitive customer data, maintaining trust is paramount. Companies must prioritise robust data governance, security measures like encryption, and transparency in how AI is used to avoid eroding customer confidence.

Addressing potential biases in AI algorithms is also crucial. Increased regulatory scrutiny is expected, particularly concerning data privacy and AI standards.   

Integration Complexity and Data Readiness

Integrating sophisticated AI tools with existing legacy systems and fragmented data sources presents a significant technical hurdle.

The effectiveness of AI heavily relies on access to high-quality, unified data, and many organisations struggle with data silos and data quality issues.   

Strategic Implementation and Demonstrating ROI

Many leaders face an “optimism gap,” recognising AI’s potential but struggling with where to start and how to implement it effectively.

Defining clear objectives, selecting the right use cases (balancing value and feasibility), and demonstrating tangible return on investment (ROI) remain key challenges. Unrealistic expectations for immediate ROI can lead to premature scaling back of AI initiatives.   

Managing the Workforce Transition

Successfully integrating AI requires upskilling employees to work alongside AI systems and manage automation effectively. Addressing employee concerns about potential job displacement and ensuring their engagement throughout the transition is vital for successful adoption. A skills gap in implementing and managing AI can also hinder progress.   

Also read: Why Putting People First is Key

AI Reliability and Accuracy

AI models, particularly generative AI, can sometimes produce inaccurate or nonsensical outputs (“hallucinations”). Ensuring the reliability and factual accuracy of AI-generated responses and actions is critical to avoid negatively impacting customer experience and trust.

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7 Strategic recommendations to leverage AI in customer service

1. Use cases prioritisation

Prioritise implementing high-value and feasible AI use cases that align directly with specific business objectives. Following Gartner’s framework for evaluating use cases can help identify those applications most likely to deliver tangible benefits and successful outcomes.

Starting with “Likely Wins“, such as customer personalisation and AI-powered agent assistance, can provide a strong foundation for broader AI adoption.

2. AI Strategy

Develop an AI strategy that carefully considers both the potential for automation and the critical role of human agents. The goal should be to augment human capabilities with AI tools rather than solely focusing on replacing human interaction.

This balanced approach will ensure that businesses can leverage the efficiency gains offered by AI while still providing the empathy and complex problem-solving skills that human agents excel at.

3. Gen AI capabilities

Actively invest in generative AI capabilities to enhance operational efficiency and deliver more personalised customer experiences.

Recognising the widespread adoption trend predicted, businesses should explore various use cases for generative AI, such as automating content generation, summarising customer interactions, and creating more natural and engaging conversational interfaces.

4. AI agents

Prepare for the inevitable rise of AI agents and their increasing potential to handle more complex customer interactions and even operate autonomously.

While widespread autonomous AI in customer service may still be on the horizon, piloting AI agents for specific, well-defined tasks can help organisations gain valuable experience and prepare for this future trend.

5. Focus on Seamless Omnichannel Experiences

Strategically focus on creating seamless omnichannel experiences powered by AI. As emphasised by AWS, McKinsey, and various industry reports, customers expect consistent and personalised support across all channels.

Implementing AI solutions that can integrate and manage customer interactions holistically across different touchpoints will be crucial for meeting these expectations.

6. Building Customer Trust

Building and maintaining customer trust by ensuring the responsible implementation of AI technologies is a high priority.

Addressing data privacy, security, and ethical considerations is paramount. Businesses should implement robust data protection measures, strive for transparency in their AI practices, and ensure that the use of AI aligns with customer expectations and regulatory requirements.

7. A Continuous Journey

Continuously monitor and adapt to the rapidly evolving AI landscape in customer service. Staying informed about the latest advancements, insights from research institutes, and offerings from technology providers will be essential for businesses to remain competitive and effectively leverage AI’s transformative potential in their customer service operations.

Conclusion

The current landscape showcases a diverse range of AI applications, from chatbots handling routine inquiries to AI-powered tools augmenting the capabilities of human agents.

Looking ahead to 2025, the trends indicate a significant acceleration in the adoption of generative AI, the emergence of more autonomous AI agents, a heightened focus on personalised and proactive service delivery, the seamless integration of AI across all customer interaction channels, and an increasing emphasis on building trust through responsible AI practices.

Leading technology providers like AWS, Google Cloud, Microsoft Azure and ServiceNow are actively developing and offering a wide array of AI-powered solutions. Research institutes consistently point towards a future where AI plays a central and transformative role in shaping the future of customer service.

In conclusion, artificial intelligence represents a fundamental shift in how businesses can interact with and support their customers. By strategically embracing AI, organisations can unlock new levels of efficiency, personalisation, and customer satisfaction. However, a thoughtful and human-centric approach, one that balances technological innovation with the enduring value of human connection, will be key to realizing the full potential of AI in shaping the future of customer service.