Service
Artificial Intelligence
Shape the future with human-first AI
Highlights
- AI and generative AI are powerful tools for helping companies overcome the wide range of challenges they face in their customer service function.
- By applying AI across the customer journey, companies can achieve key business outcomes, including strong customer loyalty, robust revenue growth, and cost-efficient operations.
- A three-phase approach of assist, augment, and transform can guide organizations on their AI adoption journey and help them realize the expected outcomes from AI.
- A four-layer architecture can be the foundation for a wide range of AI customer service solutions that are instrumental in transforming contact centers and the broader customer service function.
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Why AI for customer service
GenAI enables a complete reimagination of customer service across the customer journey.
The customer service value chain encompasses the entire customer journey, from a customer’s initial awareness of a product or service and consideration of purchase to customer service and, ultimately, loyalty and advocacy. Key personas—including digital marketing specialists, digital and data analysts, customer service managers, various agents, and service quality managers—play a crucial role in effectively engaging customers at each stage of this value chain (see Figure 1). Equipped with intelligent information for a specific customer scenario—be it personalized marketing or effective service post-sales—every persona can positively impact the customer experience, hence contributing to an enterprise’s growth.
But today’s customer service organizations are struggling with numerous challenges. The most common of these are high labor costs, complex and disconnected customer journeys, high volume of inquiries, expanding number of social media channels for customer inquiries, repetitive questions, reliance on agent-assisted interactions, and difficulty providing personalized support at scale. These very challenges, however, present significant opportunities for intervention. This is where generative AI (GenAI) comes in.
With its ability to understand and generate human-like text and interact conversationally, GenAI is enabling a complete reimagination of customer service across the customer journey. The technology promises to transform contact center operations: from reactive service units to proactive, value-adding, and revenue-generating powerhouses. By automating tasks, personalizing customer interactions, and providing predictive insights, GenAI empowers customer service agents to anticipate customer needs, offer tailored solutions for any customer scenario, and even create new revenue streams through targeted upselling and cross-selling opportunities. This shift allows businesses to enhance the customer experience, optimize customer operations, improve efficiency, and unlock new growth potential.
Figure 1: Customer service value chain, personas, and key GenAI use cases
GenAI is widely projected to impact 40% of functional spend in customer service across industries, emphasizing its transformative potential. GenAI customer service use cases can be designed specifically for each persona and their role to enhance their effectiveness and help them deliver superior customer experiences. For instance, digital marketing specialists can use AI-powered tools to personalize customer outreach, while digital and data analysts can leverage AI to gain insights into customer churn and behavior patterns. Customer service managers can rely on AI-driven predictive models to optimize resource allocation and service levels. Contact center agents can benefit from AI-powered smart assistants, intelligent call routing, and an enterprise knowledge base to resolve customer issues efficiently.
By integrating GenAI use cases across the customer service value chain, businesses can empower their key personas to deliver personalized, efficient, and proactive support, ultimately fostering customer loyalty and driving business growth.
A structured approach to customer service transformation
A phased, "enterprise-wise" AI approach for GenAI-led transformation traversing three stages—assist, augment, transform—can help organizations effectively adopt and deploy AI.
As shown in Figure 2, the approach starts with the assist phase, where AI boosts human capability by automating tasks and helping with repetitive tasks. This includes activities such as knowledge search from across multiple information stores and summarization, smart assistance specific to a scenario for agents and customers through natural language interactions, and help creating and sending emails. By using GenAI for these tasks, businesses can not only automate mundane activities like drafting an email (thereby improving productivity and efficiency) but more importantly, they can deliver rich and sharp contextual information to specific personas that will help make intelligent and information-based decisions for any customer scenario.
The next phase is augment, where humans and machines collaborate to achieve better outcomes. This involves using AI for activity optimization, personalized engagement, and predictive recommendations. For example, AI can personalize marketing campaigns and customer experiences, and predict which products customers are most likely to buy, thereby enhancing the potential to cross-sell and upsell. This collaboration between humans and AI gives an assistant to every persona in the value chain, who can also seek recommendations on actions that will best address the customer situation, leading to more efficient and effective decision-making.
Figure 2: A phased approach to AI-enabled customer service transformation
Finally, in the transform phase, AI elevates human capabilities by personalizing outreach and generating real-time insights and conversation insights—and, in the process, redefines value chains and innovative plays. For example, AI can not only summarize customer reviews and understand the customer’s sentiments to personalize customer interactions in real time, but it also can help identify creative ideas for new products and services and new business models based on the insights from customer reviews. This transformation helps businesses stay ahead of competitors and create new customer value.
An architecture for customer service transformation
Complementing the three-phase implementation approach is a four-layer architecture for GenAI transformation, which provides a comprehensive framework for integrating AI into customer service operations.
As Figure 3 illustrates, the architecture’s foundation is robust data management, encompassing structured, unstructured, and external data sources, along with data lakes and warehouses. This data fuels GenAI’s foundational large language models (LLMs) and enables advanced analytics.
The architecture’s strength lies in its purposive and contextual task agents, powered by GenAI. These agents are designed for specific roles within the customer service value chain, such as self-service, quality assurance, marketing, and insights generation. Human oversight and controls ensure ethical and responsible AI implementation, with guardrails and observability mechanisms in place. The task agents are built on the underpinning information stores from the data lakes and data marts including unstructured data such as customer transcripts, and other sources such as customer reviews that will enable rich analytics and insights.
These then fuel the relevant work systems that serve as specific assistants to each of the personas in the ecosystem, thereby enabling AI-augmented intelligence that helps drive intelligent, informed decisions for every customer scenario.
This layered approach offers several benefits. It allows for a modular and scalable implementation, catering to diverse customer service needs. The focus on purpose-built AI agents ensures efficient and targeted solutions that can be built on data and information needed for the solution. Moreover, the emphasis on data security, ethical considerations, and human-in-the-loop oversight builds trust and transparency in AI-driven customer service transformations.
Figure 3: A four-layer AI architecture for customer service transformation
With this architecture as the foundation, companies can develop and implement a wide range of solutions that help transform the customer service function to achieve critical business outcomes such as:
- An increase in digital self-service by 20-40%.
- Reduction in average handling time by 10-20%.
- Reduction in agent training time by 10–25%.
- A 10–20% decrease in agent attrition.
- A 5–25% reduction in call transfers.
- A 10–20% increase in containment using virtual agents.
- A 10–40% improvement in customer satisfaction.
- A 20–50% surge in adoption of various messaging channels.
Case study: Contact center agent
Working in concert, the three-phase approach and four-layer architecture can help companies execute a complete, end-to-end transformation of their contact centers.
Here’s an example of how GenAI can dramatically change the day in the life of a contact center agent and the value delivered by the agent:
Before GenAI: The day starts with a mountain of customer queries. The agent spends precious minutes sifting through CRM data to understand customers’ context and history. Each interaction involves manually searching for standard responses or composing emails from scratch. The agent juggles multiple systems, struggling to keep up with the volume, leading to longer handling time, frustrated customers, and agent burnout. There's little time for proactive engagement or personalized service, and the workday challenges often lead to high agent attrition.
After GenAI: GenAI enables customer self-service with humanlike interactions, making it seamless for the customer. If the customer wants to engage with a human from the contact center, the agent is greeted with a structured dashboard, where GenAI has already analyzed customer data to provide relevant context, customer sentiment, and suggested actions. Personalized responses are readily available, and emails are automatically generated with professional summaries. The agent gets all the information in a structured format in a single place and doesn’t have to hop through multiple systems. This gives the agent time to focus on understanding complex customer needs and providing empathetic support. As the assistant to the agent, GenAI handles the mundane and repetitive tasks, freeing up the agent’s time for proactive outreach and building stronger customer relationships, making the agent’s job more rewarding—thus leading to stronger customer and agent retention.
Figure 4: Current state vs. future state for a contact center agent
Creating stronger customer relationships to drive growth
Today’s business landscape is undergoing a dramatic transformation with heightened customer expectations, fierce competition, and relentless technological advancements.
Customers demand personalized, efficient, and always-available service experiences, and expect to be served through social media channels of their choice, pushing traditional customer service models to their limits.
This dynamic environment, further intensified by labor shortages and skill gaps, requires companies to completely reimagine their customer service function and contact center operations. Generative AI is a powerful tool to help drive such transformation, allowing organizations to deliver personalized experiences and streamlined interactions, break down silos between channels to provide seamless and consistent service, and automate a wide range of customer service activities. Companies can create and deliver superior customer experiences that foster strong, long-term customer relationships, provide competitive differentiation, and drive stronger revenue growth in a challenging and evolving business environment.
