This is an excerpt from our AI eBook: Achieve CX Excellence with AI-Powered Journey Orchestration
Generative AI models are only as good as the data they have access to. While large language models are very powerful, they require fine-tuning with an organization’s specific knowledge, workflow & automation meta-data, and data to provide real value for the specific questions that they receive. Opportunities to improve the models are abundant but training and supervision is required.
With that said, there are six key applications where generative AI can supercharge the customer experience and customer journey orchestration:
Businesses can train generative AI models to extract relevant information from customer case data in customer support tickets, chat transcripts, and survey feedback. Automating this process will help time, reduce effort, and ensure accuracy in data capture. Specific customer details for extraction would include:
Generative AI models can be trained to summarize large sets of data across multiple related objects into simple and clear summaries. In doing so, companies can save significant time as well as equip teams with the context they need to provide better experiences to end-customers. These can include summaries of:
Building off of generative AI’s summarization capabilities, organizations can train models to suggest relevant answers to internal teams and users. Employees receive content directly versus having to search for information across legacy enterprise systems, thereby saving time and effort.
As Generative AI models begin to perform well, businesses can increase their focus into delivering answers directly to end-customers in omni-channel environments.
With ongoing training of the generative AI models, there is an evergreen opportunity to improve experiences. One key way is through Q&A Response Generation.
Q&A Response Generation: Businesses can generate both questions and answers for customers' top questions, including whether teams are responding accurately to the queries. Combining this data with additional relevant context, such as the interaction rating, journey status sentiment, customer product information, and more will allow teams to provide finely-tuned Q&As to organizations.
Outside of empowering customers and customer-facing teams with generative AI, we believe there is also a large use case for empowering organizational leaders to quickly and easily make better decisions with their data without the need for complex data analysis, specialist tools, or dedicated analysts.
Specifically, organizations have the opportunity to leverage generative AI to augment visual dashboards to answer quick questions and provide flexible insights that could only previously be unlocked with a lot of time, effort and technical expertise. Some critical insights that can be gleaned include:
Generative AI is a game-changer for businesses across industries.The capacity to automate content generation, optimize touchpoint and analyze large volumes of data can help businesses dramatically improve customer engagement and satisfaction. With generative AI, businesses can uncover valuable insights and take charge of customer and employee outcomes at scale.