You see the promise of generative AI, but the fear of "hallucinations" and brand risk has you stuck in pilot mode. To go from a beta test to a practical playbook, you need solid answers.

So, what is generative AI in customer service? It’s technology that goes beyond following scripts to generate new, human-like responses in real-time. This is the key difference between new and old systems:

• Standard chatbots follow rigid, pre-written flowcharts. They only know what you've manually programmed.
• Generative AI understands a customer's intent and creates original, conversational answers by drawing from your knowledge base.

This guide provides the framework for implementing this technology safely, managing risk, and empowering your agents. Let's see how.

How is generative AI used in customer support?

Generative AI acts as a powerful co-pilot for your entire support operation. It steps in to enhance nearly every part of the customer journey.

Its main uses include:

• Powering smart virtual agents to resolve common issues instantly, 24/7.
• Increasing human agent productivity by automating repetitive tasks like summarizing calls and drafting replies.
• Delivering deep personalization by tailoring responses based on a customer's unique history.
• Providing seamless omnichannel support that maintains context as customers move between chat, email, and other channels.
• Collecting and analyzing data to uncover trends, customer sentiment, and areas for improvement.

What are the real-world benefits of generative AI?

Implementing generative AI delivers powerful business results that go far beyond just faster emails.

• Increased speed and efficiency. AI models resolve common customer issues in seconds, freeing human agents for complex problems. This is how Substack is able to maintain an impressive 90% resolution rate for its support inquiries.
• Improved customer experience. Instead of just answering tickets, AI can analyze them to find the root cause of problems. Whop used insights from Decagon to inform targeted fixes, which resulted in a 50% reduction in support tickets.
• Significant cost savings. When AI automates routine tasks, productivity soars and operational costs drop. Skincare company Curology, for example, successfully reduced its customer support costs by 65%.
• 24/7, multilingual support. You can instantly scale your operations to be available anytime, anywhere, and in any language. After implementing AI support, ClassPass was able to expand its customer service to be available 24/7.

Challenges to consider

While the benefits are significant, a successful rollout means being realistic about potential problems so you can head them off.. Key challenges of AI for customer service include:

• Accuracy and reliability. AI systems can occasionally produce inaccuracies or hallucinate if not properly trained and set up with appropriate guardrails.
• Data security and privacy. Companies must ensure customer data is handled securely and in compliance with regulations like GDPR.
• Workforce impact. Training staff to work alongside AI and addressing their concerns about job displacement are crucial aspects of implementation.
• Implementation costs. Significant investment in technology and training may be required, which can be a challenge for smaller organizations.

Decagon: Implementing generative AI customer support without the risks

For enterprise teams, the primary challenge is more about deploying safe, reliable, and controllable AI, rather than choosing an AI partner. This is where Decagon’s architecture provides a distinct advantage.

Decagon’s main differentiator is its Agent Operating Procedures (AOPs). Instead of complex code, AOPs allow your CX managers to build and update sophisticated AI workflows using natural language. This dramatically reduces setup time and developer reliance.

These AOPs integrate directly with your existing tools, allowing the AI to reference data, take real actions, or trigger workflows in CRMs, helpdesks, call centers, and knowledge bases.

From day one, Decagon was built for enterprise needs. Security, privacy, and observability are core to the platform, not an afterthought. This includes Watchtower, a powerful quality assurance and visibility tool. It allows you to monitor AI interactions, test agent behavior in a sandbox, and ensure every response is accurate before it ever reaches a customer.

Decagon delivers this secure, generative AI support across all the channels your customers use: chat, voice, email, SMS, and custom APIs.

The results are transformative. As the VP of Client Solutions at Bilt explains, 

“Decagon created a tool that allows our customers to seamlessly receive help across our business. Additionally, the team worked tirelessly to build integrations into our CRM and support setups to make sure we would not have to change any of our current operational workflows. Couldn't recommend them more highly!”

Here’s more detailed look at how Decagon's AI agents were used to automate complex inquiries for Bilt Rewards.

This power extends to voice with Voice 2.0, which offers:

• Extremely low latency for natural, fluid conversations.
• Customizable AI voice profiles to perfectly match your brand's tone.
• Cross-channel memory so customers never have to repeat themselves if they switch from chat to a call.
• Smooth handoff to human agents for complex, high-empathy issues.
• Text-to-voice support for fully accessible, high-quality interactions.

Getting started with generative AI for customer support

Diving into generative AI doesn't have to be a massive, six-month project. The smartest way to begin is by focusing on immediate value.

Start by analyzing your current support tickets. Look for the high-volume, low-risk conversations. These are the repetitive questions that clog your queue, like "How do I reset my password?" or "What is your return policy?" 

Automating these simple inquiries first provides a quick, measurable win and builds a foundation for more complex tasks. From there, you can build out automated workflows for more complex issues.

Decagon’s speed is a major advantage here. While traditional enterprise solutions can take months to deploy, our platform is designed for rapid setup. As engineers and CX managers can both build AI workflows using natural language, you cut down on developer bottlenecks. This collaborative, user-friendly approach means you can go from kickoff to live deployment much faster, proving ROI in weeks, not quarters.

Building reliable AI customer support

The era of generative AI for customer support is here, but moving from a nervous pilot program to a confident, full-scale deployment requires a powerful model that offers safety and reliability.

You don't have to choose between innovation and protecting your brand. A platform like Decagon gives you the tools to manage risk, ensure accuracy, and empower your agents, all while delivering the speed and personalization customers now expect.

Book a demo to see how Decagon's enterprise-grade platform makes reliable, secure AI a reality for your team.