Discover how AI can transform your business by extracting insights from unstructured data. Learn about the benefits of Retrieval Augmented Generation (RAG) and how it can enhance your customer experience.
Traditionally, companies have handled data found in structured formats, with rows and columns, including customer engagement data gathered through CRM applications. Every business also has a huge amount of information trapped in “unstructured” data, such as documents, images, audio, and video recordings. This unstructured data could be highly valuable, providing businesses with AI insights that are more accurate and comprehensive because they are grounded in customer information.
Many organizations want a holistic customer view but have lacked the technical ability to see, access, integrate, and use their unstructured data in a trusted way. They can now do just that with the power of large language models (LLMs) and generative AI.
Successful organizations must build integrated, federated, intelligent, and actionable solutions across every customer touchpoint while reducing complexity to win in AI.
This starts with tapping into unstructured content, gathering knowledge effectively, indexing the data efficiently, and pulling insights from every department.
Helping AI to know better and serve customers
When a customer needs help with a recent purchase, typically, they start the conversation with the company’s chatbot. For the experience to be both relevant and positive, the entire exchange must be grounded in that customer’s data, such as their recent product purchase, warranty information, and any past conversations. The chatbot should also tap into company data, such as the latest learnings from other customers who have bought similar products and internal knowledge base articles.
Some of this information might reside in transactional databases—structured information—while the rest might be in unstructured files, such as warranty contracts or knowledge base articles. Both data types need to be accessed, and the right data needs to be utilized. Otherwise, the exchange with the chatbot will be, at best, frustrating and, at worst, inaccurate.
Obtaining the best and most accurate AI responses requires augmenting LLMs with proprietary, real-time, structured, and unstructured data from within a company’s applications, warehouses, and data lakes.
An effective way of making those models more accurate is with an AI framework called Retrieval Augmented Generation (RAG). RAG typically enables companies to use their structured and unstructured proprietary data to make generative AI more contextual, timely, trusted, and relevant.
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Ensuring relevancy, whatever the scenario
Combining all your customer data, structured and unstructured, into a combined 360-degree view will ensure customers have the most relevant information for any enterprise scenario.
Financial institutions, for example, can use it to provide real-time information on market or financial data to their employees, who can blend that information with a customer’s unique banking needs to give them actionable advice based on their situation.
Many companies are exploring using RAG technology to improve internal processes and provide accurate and up-to-date information to advisors and other employees. Offering contextual assistance, ensuring personalized support, and continuous learning will improve efficiency in decision-making across the organization.
For organizations preparing their data for AI, first, they have to know where all of their data is and understand its quality – and whether it is good enough for their generative AI models. Second, they must ensure their data is fresh, relevant, and retrievable to combine structured and unstructured data for the best outputs. Thirdly, they must activate that data across their applications and build the right pipelines so RAG can pull that data when prompted and provide the necessary answers.
