AI in the Contact Center: Transforming Value Creation

AI is quickly transforming customer service and contact center operations to generate measurable value. With the integration of genAI and agentic AI, organizations can now move beyond pilots and into strategic full-scale deployments that drive quantifiable results, even as an MIT report notes that 95 percent of genAI pilots fail.  Structured interactions, clear metrics, and abundant conversational data offer the ideal conditions for AI success. GenAI creates natural-sounding responses and summaries, while agentic AI goes further, taking action, orchestrating workflows, and making real-time decisions.

Emerging AI Capabilities Expand Customer Service Possibilities

AI automation has already demonstrated its value as a way to streamline and accelerate standardized, high-volume processes. Now, genAI and agentic AI add the ability to deliver humanlike service in less standardized interactions. Agentic AI can also orchestrate complex contact flows and multiple customer intents to route and handle customer engagements more effectively.

Take a common scenario: When a customer calls in or starts a chat, a robotic and scripted response is often unhelpful and frustrating. However, today’s conversational AI models can offer customers the assistance they need in a way that feels natural and personalized. Customers can resolve their issues quickly using systems that guide them through making a payment or troubleshooting an internet connection. This cultivates more positive feelings about the brand. AI-enabled resolutions also free up the company’s human representatives to build relationships with customers whose needs require high-touch engagement or who have complex issues to resolve. That closer engagement also strengthens consumer sentiment.

GenAI and agentic AI capabilities can transform the employee experience too, not only by handling repetitive customer issues but also by surfacing relevant guidance and recommendations in real time to help solve complex or uncommon issues faster. This kind of context-based knowledge presentation can also improve onboarding and training processes to improve outcomes, reduce agent turnover, and control costs. For customers, the difference maker is more than just faster resolution; it’s the feeling of being understood. For agents, it is not about simply finishing the call as efficiently as possible, but working confidently, supported by real-time knowledge and guidance.

AI-Driven Results in the Contact Center

Early results show momentum building. Unlike open-ended enterprise pilots, contact centers operate with structured intents and measurable KPIs, allowing AI to mature rapidly from proof of concept to scaled deployment. A recent global survey of consumers, service agents, and executives found that contact center AI use cases are outperforming the general 5 percent genAI success rate reported by the MIT/NANDA study:

•       Thirty-three percent have improved their first contact resolution rates, and another 52 percent expect to do so.
•       Twenty-four percent have reduced their operating costs, and another 65 percent expect to do so.
•       Seventy-three percent of customer service representatives spend less time on mundane tasks.

For a large enterprise, combining AI automation, genAI, and agentic AI can transform the economics of service, reducing costs by hundreds of millions over five years while boosting satisfaction scores by double digits.

The report’s authors calculated that a 5,000-employee contact center operation using AI automation, genAI, and agentic AI to route customers, provide effective AI-powered self-service, and increase first-contact resolution could save between $200 million and $300 million over five years, while increasing the organization’s Net Promoter Score (NPS) by 20 to 30 percent.

Strategic Foundations for Contact Center Transformation

A lasting AI strategy begins with a clear understanding of the kind of value you aim to create. Does the organization need to reduce call volume, improve time to resolution, or improve Customer Satisfaction Score (CSAT)?

With clear goals, leaders can then review all the contact center levers, such as self-service; routing; agent skills and tools; and workforce, quality, and performance management. Leaders must view these levers through the lens of customer intent. What are the most common reasons that customers call in? Which of these reasons are the most important to address in support of the primary value creation goals? Customer intent data is critical for making strategic decisions at this stage.

For example, if your company’s main transformation value driver is to reduce costs, you need to understand the root cause behind customer calls and solve for that cause, with a conversational redesign of the center’s self-service experience to increase customer use and reduce call volume. Working with conversational design experts can refine voice interactions, improving both customer satisfaction and agent experience.

AI transformation also requires standardized real-time data to inform next best steps, smart process orchestration to reduce wait times and resolve more issues on first contact, and appropriate governance and security to protect customer data and maintain regulatory compliance.

Building on Initial Transformation Success

Once the initial use case starts to deliver value, it’s possible to extend that success gradually and strategically to other value-focused use cases in the call center. For example, after conversational design improvements for self-service, the next step might be to improve orchestration across contact channels through better intent identification and intelligent routing. This kind of use case can improve first -call resolution rates by 30 to 50 percent.

Other transformational use cases focus on employee experience and workforce management. Using AI to give frontline agents a unified workspace with searchable resources and just-in-time information delivery can improve agent productivity by as much as 70 percent. Using AI to forecast, schedule, and coach agents can improve workforce capacity by as much as 25 percent through optimization and quality improvements.

Approaching these use cases in a methodical and strategic way allows the organization to use learnings from each new use case to improve planning and implementation for the next. Over time, each successful use case fuels the next. Data improves design, design improves containment, and containment frees resources for innovation. The contact center becomes not just a service hub, but the enterprise’s AI flywheel, where real-world learning accelerates intelligent growth across functions.

As enterprises search for practical ways to prove AI’s value, the contact center stands out, not as a cost center, but as the first true testbed for enterprise intelligence at scale.

Bright Hung is a senior director at Capgemini Invent, leading AI-enabled contact center transformation across industries. He applies advanced AI, data, and human-centered design to deliver intelligent, efficient, and empathetic customer and agent experiences. Carlos Alzate is a senior manager at Capgemini Invent, architecting enterprise programs that turn the contact center into a value engine powered by AI. He sets strategy, designs the operating model, and mobilizes cross-functional execution to scale what works with control, creating impactful gains for clients.