At Kustomer, we provide 24x5 Technical Support to our customers and pride ourselves on our consultative approach to support.
We’re all about AI being used in customer support, so wanted to explore: how can we apply AI to see similar advances in technical support?
How AI is currently being utilized in technical support
AI is being used as a way to provide answers to questions in a scalable way. Previously systems would review a question and provide links to documentation articles that may answer the question. The customer would have to review the document and find the solution within its content. Now, AI does most of the investigative work and will provide answers directly from articles and even concatenate information from multiple sources to provide clear instructions on next steps.
At Kustomer we recently updated our engagement with AI to be conversational, meaning we allow multiple questions to be asked to the AI bot to answer multiple questions you may have in one session.
How AI can help in identifying and resolving issues before they become major problems for customers
AI can be leveraged in multiple ways to spot trends. It can be configured to review metrics and even content of your incoming support inquiries. A way that is starting to become popular is the use of AI to review all of your inquiries and then organize them by theme. You can then continue to use AI to review those themes and spot trends. This can lead to internal conversations on how to improve your system and processes but also where to focus your energy from a documentation standpoint.
For example, if you are receiving questions about a specific product or service, then putting effort into documenting those areas more will result in the AI presenting more complete answers in the future. This will alleviate volume that may need to be addressed by your team.
Key benefits of implementing AI in a technical support environment
The first key benefit is time. The sooner you can provide a customer with a solution, the better. In a perfect world, customers don’t want to spend time engaging a support team. Many customers would like to be autonomous and perform tasks and actions on their own. Getting an answer or performing an action quickly is the first step to great customer service.
The second is accuracy. Having an AI tool that can provide the same answers that are accurate to many questions that are presented in multiple ways. The more the AI answers questions or performs actions, the better it will be with providing the first answer accurately.
Challenges in integrating AI into technical support systems
The biggest challenge will be how well the AI tool can connect with your existing infrastructure. You may need to do some coding or integration but having an existing system or systems can help greatly. You will want a robust system that can integrate but having a database of content is a great starting point. What we have seen begin to happen across the SaaS industry is that your vendor partners are already partnering with AI tools to provide you some of these tools and functionality native within their software suite.
Here at Kustomer, we are leveraging Open AI’s GPT-4o-mini to power AI generated responses to assist both agents and customers with support inquiries. KIQ Agent Assist and Customer Assist are two big products we have released for our clients to implement generative AI for customer service without having to develop the integration themselves.
The role of human technical support agents evolving alongside AI
AI is best used as a partner to human technical support agents. There are many benefits with engaging a person on a support matter. It helps build relationships and brand loyalty but AI can help ensure that those interactions are the most impactful ones. AI can complement the human support agents by answering the easier questions as well as provide direct steps and actions on various repeatable tasks. With AI handling those requests the human agent has more opportunities to be consultative, supportive, and empathetic with customers on their more complex issues/questions.
Privacy and ethical considerations should companies keep in mind when deploying AI in technical support
I recommend that companies consult with their internal Data Security & IT teams when building out their AI repository. If they are also a software company, leveraging their internal data hosting team for guidance is a great path as well. These teams will be able to guide them in the best direction for storage, access, and authentication into these tools and repositories so they are as secure as possible. Each organization may be different and depending on where you are located you may have different privacy policies or local laws to work with when storing the personally identifiable information-related data that may be gathered via the AI tool.
Future developments in AI-driven technical support
I can see AI becoming more predictive. For example, as a company’s dataset increases, your AI tool will be able to spot time-based trends. For example, for a SaaS platform, it will know that customers from a certain industry tend to ask these specific questions the first 8 weeks after their go live date. So, why not provide that information before the questions arise? Or customers may encounter specific questions with a product so why not provide the answers before they encounter the issue?
