Editor’s note: This post is part of the Nemotron Labs blog series, which explores how the latest open models, datasets and training techniques help businesses build specialized AI systems and applications on NVIDIA platforms. Each post highlights practical ways to use an open stack to deliver real value in production — from transparent research copilots to scalable AI agents.
Whether it’s getting a complex service claim resolved or having a simple purchase inquiry answered, customers expect timely, accurate responses to their requests.
Teams of specialized AI agents can help organizations meet this need. With advanced reasoning skills, they learn the details of everyday business, helping retain and satisfy customers. By automating routine tasks, AI agents ease the workload on human agents, allowing them to focus on tasks requiring a more personal touch.
AI-powered customer service tools like chatbots have become table stakes across every industry looking to increase efficiency and keep buyers happy. According to a recent IDC study on conversational AI, 41% of organizations use AI-powered copilots for customer service and 60% have implemented them for IT help desks.
Now, many of those same industries are looking to adopt agentic AI, semi-autonomous tools that have the ability to perceive, reason and act on more complex problems.
How AI Agents Enhance Customer Service
A primary value of AI-powered systems is the time they free up by automating routine tasks. AI agents can perform specific tasks, or agentic operations, essentially becoming part of an organization’s workforce — working alongside humans who can focus on more complex customer issues.
AI agents can handle predictive tasks and problem-solve, can be trained to understand industry-specific terms and can pull relevant information from an organization’s knowledge bases, wherever that data resides.
With AI agents, companies can:
- Boost efficiency: AI agents handle common questions and repetitive tasks, allowing support teams to prioritize more complicated cases. This is especially useful during high-demand periods.
- Increase customer satisfaction: Faster, more personalized interactions result in happier and more loyal customers. Consistent and accurate support improves customer sentiment and experience.
- Scale Easily: Equipped to handle high volumes of customer support requests, AI agents scale effortlessly with growing businesses, reducing customer wait times and resolving issues faster.
AI Agents for Customer Service Across Industries
AI agents are transforming customer service across sectors, helping companies enhance customer conversations, achieve high-resolution rates and improve human representative productivity.
For instance, ServiceNow recently introduced IT and customer service management AI agents to boost productivity by autonomously solving many employee and customer issues. Its agents can understand context, create step-by-step resolutions and get live agent approvals when needed.
To improve patient care and reduce preprocedure anxiety, The Ottawa Hospital is using AI agents that have consistent, accurate and continuous access to information. The agent has the potential to improve patient care and reduce administrative tasks for doctors and nurses.
The city of Amarillo, Texas, uses a multilingual digital assistant named Emma to provide its residents with 24/7 support. Emma brings more effective and efficient disbursement of important information to all residents, including the one-quarter who don’t speak English.
AI agents meet current customer service demands while preparing organizations for the future.
Key Steps for Designing Agents for Customer Support
AI agents for customer service come in a wide range of designs, from simple text-based virtual assistants that resolve customer issues to animated avatars that can provide a more humanlike experience.
To create an effective AI agent for customer service:
- Select the right model: Evaluate open models, like NVIDIA Nemotron, that provide a powerful building block to create specialized models or to combine with frontier models — for any domain.
- Collect and organize customer data: AI agents need a solid base of customer data (such as profiles, past interactions and transaction histories) to provide accurate, context-aware responses.
- Use memory functions for personalization: Create specialized agents using customized open models that have access to proprietary data. Advanced AI systems remember past interactions, allowing agents to deliver personalized support that feels human.
- Build an operations pipeline: Customer service teams should regularly review feedback and update the AI agent’s responses to ensure it’s always improving and aligned with business goals.
Getting Started With AI Agents for Customer Service
NVIDIA AI Blueprints make it easy to start building and setting up AI agents by offering ready-made workflows and tools to accelerate deployment. Whether for a simple AI-powered chatbot or a fully animated digital human interface, the blueprints offer resources to create AI assistants that are scalable, aligned with an organization’s brand and deliver a responsive, efficient customer support experience.
Editor’s note: IDC figures are sourced to IDC, Market Analysis Perspective: Worldwide Conversational AI Tools and Technologies, 2024 US51619524, Sept 2024