The generative AI contact center has transformed the world of customer experience.
While generative AI can have a powerful impact on various business processes, it’s particularly compelling in the contact center. McKinsey suggests implementing generative AI contact center strategies into your business, which can lead to a 45% productivity cost improvement and other benefits.
However, as interest in generative AI solutions continues to grow, the market competition is also evolving. Virtually every contact center innovator and tech giant is now exploring new ways to bring LLM-powered solutions to business leaders. This means finding the right solution for your company can be complex.
Fortunately, we’re here to help. Here’s our behind-the-scenes guide to comparing generative AI contact center solutions in 2024.
Step 1: Define Your Generative AI Contact Center Goals
The first step to choosing the right generative AI contact center solutions is deciding what you want to achieve. Today’s ultra-flexible generative AI technologies can address a variety of use cases. Agent assistant tools empowered with generative AI can improve team productivity and efficiency. They can automate repetitive tasks and coach staff in real-time.
Chatbots and virtual assistants for consumers, built with generative AI, can deliver personalized self-service experiences. Companies can use bot builders to design incredible virtual agents capable of offering 24/7 support to customers on a range of channels.
There are even comprehensive generative AI toolkits available in CCaaS platforms, which can assist with everything from call summarization to KPI analysis. Setting clear goals for your new solution will help you decide what generative AI system you need.
Step 2: Examine Your Current Ecosystem
Like most innovative contact center tools, generative AI solutions work best when aligned with your existing technology stack. At a basic level, if you’re using generative AI for customer service, you’ll need to ensure your system can integrate with your contact center.
This could mean leveraging the “bring your own AI” options some CCaaS vendors offer or working with vendors that provide their proprietary solutions. For instance, Microsoft offers Copilot for Sales and Copilot for Service, which integrates with Teams and Dynamics.
Alongside your contact center, it’s worth looking closely at the other tools you want to align with your generative AI strategy. Do you want to connect AI coaching bots with tools like Microsoft Viva? Are you using CRM and customer data platforms that can help you train your generative bots with proprietary insights? The more flexible your chosen solution is, the better.
Step 3: Consider Customization Needs
Like all forms of AI, generative AI contact center tools rely on data to deliver exceptional results. Some pre-built solutions are already trained on vast volumes of service-focused data, like DialpadGPT.
However, they’ll learn, adapt, and improve over time based on the data they can access about your business and customers. With this in mind, before implementing a new generative AI solution, it’s worth ensuring you have the correct data ecosystem.
Examine your existing CRM records, contact center recordings, and knowledgebase articles. Think about how you can prepare your data to provide your AI bots with the most accurate insights into your company. Additionally, ensure your chosen solution can be customized to suit your needs.
Most leading generative AI solution providers will support data connections and customization options so you can create a unique experience for your customers and employees. For instance, Microsoft has its “Copilot Studio” for creating bespoke AI tools.
Step 4: Examine Security and Privacy Options
Maintaining security, governance, and compliance is one of the biggest challenges companies face when implementing generative AI tools. Some of the earliest tools introduced in the industry, such as ChatGPT, didn’t have the security settings required to protect data in the business landscape. This presented a host of compliance issues to evolving brands.
Whether embracing your own AI copilots for employees or designing advanced chatbots, ensuring you’re protecting your business and customers is crucial. Ensure the generative AI solution gives you complete control over your data. Your vendor shouldn’t use your information to train other bots and should allow you to implement your own security policies.
Additionally, it’s worth examining how your vendor addresses issues like AI hallucinations and mistakes. Poorly trained bots can hurt your brand’s reputation and harm your ability to stay compliant.
Step 5: Prioritize Access to Insights
Finally, deploying generative AI tools in the contact center means committing to optimizing and improving your technology. You can only improve the responses given by your bots, enhance customer and employee experiences, and eliminate risks with the correct data.
The right generative AI solution provider will ensure you can access crucial insights into your solution’s performance or leverage analytics from integrated tools. You should be able to use comprehensive conversation analytics across multiple channels to make more informed decisions.
At the same time, it’s worth sourcing your feedback, too. Please speak to your team members to determine how straightforward and intuitive the tools are and how they contribute to greater efficiency. Listen to your customers to learn more about how they feel when interacting with your bots and virtual agents. These combined insights will help you optimize your CX strategy over time.
Comparing Generative AI Contact Center Tools
Generative AI contact center solutions are quickly becoming necessary in the CX landscape. However, there are many different ways to implement and use LLM-powered solutions.
Making the right choice for your business means carefully assessing your use cases, existing technology stack, and customization needs. Plus, you must ensure you can preserve compliance standards and gather valuable insights as you implement your next-generation tech.
