Linda Saunders | Director | Solutions Engineering Africa | Salesforce | mail me |
Every organisation is working to harness Artificial Intelligence (AI) to improve sales and customer service experiences. However, great AI relies on data. Traditionally, companies have used structured data, such as rows and columns, often from Customer Relationship Management (CRM) applications.
Businesses also hold vast amounts of unstructured data, including documents, images, audio, and videos. This unstructured data can offer valuable insights by improving the accuracy and relevance of AI-powered solutions.
Using unstructured data effectively
Many organisations desire a complete customer view but lack the tools to manage unstructured data reliably. Large Language Models (LLMs) and generative AI now make this possible.
To excel in the AI era, organisations must build unified, actionable solutions for every customer interaction while reducing complexity. This begins with accessing unstructured data, indexing it efficiently, and extracting insights across departments. When a customer needs help, their conversation often starts with a company chatbot. For a positive experience, the chatbot must use relevant customer data, such as purchase history and past interactions.
The chatbot should also access internal data, including recent insights from other customers and knowledge base articles. Some data might be in structured formats, like databases, while other information may be in unstructured files, such as contracts. Both types must be accessible and used appropriately. Otherwise, interactions may become frustrating or inaccurate.
To improve AI responses, LLMs must integrate real-time, structured, and unstructured data from proprietary sources. Retrieval Augmented Generation (RAG) enables companies to use their data for more contextual and accurate generative AI. RAG makes AI outputs timely, relevant, and trustworthy by leveraging structured and unstructured proprietary data.
Thriving in the AI era
Combining structured and unstructured data into a 360-degree view ensures customers receive relevant information in any situation. Financial institutions can use real-time market data to provide actionable advice tailored to a customer’s unique needs.
Many organisations use RAG technology to streamline processes and deliver accurate, personalised assistance. This enhances efficiency and decision-making across the organisation.
To prepare data for AI, organisations must first locate and evaluate the quality of their data. Next, they must ensure data is fresh, relevant, and retrievable to combine structured and unstructured data effectively. Finally, they must activate their data by building pipelines so RAG can retrieve it as needed.
Related FAQs: AI insights from unstructured data
Q: What are the key use cases for leveraging AI insights from unstructured data in customer experience?
A: Key use cases include sentiment analysis to gauge customer feelings, chatbots for improved customer interaction, and analytics to derive actionable insights that enhance service delivery and engagement.
Q: How can companies unlock the value of their unstructured data?
A: Companies can unlock the value of their unstructured data by implementing AI solutions that process and analyse this data, allowing them to derive valuable insights that inform business decisions and improve customer experience.
Q: What role does machine learning play in transforming unstructured data into structured data?
A: Machine learning algorithms help in processing unstructured data by identifying patterns and trends, thus transforming it into structured data that can be easily analysed for actionable insights.
Q: How can Large Language Models (LLMs) enhance the analysis of unstructured data?
A: LLMs can enhance the analysis of unstructured data by using natural language processing to understand and interpret text data, providing deeper insights and improving the accuracy of data analysis.
Q: What does it look like in practice to use AI to extract insights from a company’s unstructured data?
A: In practice, using AI involves deploying AI tools to analyse customer feedback, social media interactions and other text data, leading to the identification of trends and actionable insights that drive strategic decisions.
Q: How can businesses ensure robust data management when dealing with large volumes of unstructured data?
A: Businesses can ensure robust data management by implementing comprehensive data governance frameworks, utilising advanced data analytics tools and regularly training their teams on best practices for managing and analysing unstructured data.
Q: What are the benefits of using AI and machine learning for extracting insights from unstructured data?
A: The benefits include the ability to process large volumes of unstructured data quickly, generate valuable insights that inform decision-making and enhance the overall customer experience through personalised interactions and improved service offerings.
Q: How can companies identify data trends from their unstructured data?
A: Companies can identify data trends by employing AI analytics tools that analyse patterns in customer feedback and interactions, allowing them to track changes in sentiment and preferences over time.
Q: What is the significance of treating unstructured data as a gold mine for business value?
A: Treating unstructured data as a gold mine signifies recognising its potential to provide valuable insights that can drive innovation, enhance customer experience and ultimately lead to increased revenue and competitive advantage.
Q: How do AI-powered chatbots utilise unstructured data to improve customer interactions?
A: AI-powered chatbots utilise unstructured data by analysing customer queries and feedback to learn from past interactions, enabling them to provide more accurate responses and enhance the overall customer experience.
