The Role of Data to Harness AI

Harnessing AI for Enhanced Sales and Service: The Role of Data

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Organizations are racing to leverage AI to enhance their sales and service experiences. The Role of Data to Harness AI cannot be underestimated. However, great AI solutions rely on quality data.

Traditionally, companies have managed structured data—neatly organized into rows and columns, such as customer engagement data from CRM systems. But businesses also hold a wealth of unstructured data in formats like documents, images, audio, and video recordings.

This unstructured data can be highly valuable, offering deeper AI insights that are more accurate and comprehensive, grounded in real customer interactions. Yet, many organizations struggle to effectively access, integrate, and utilize their unstructured data to gain a holistic customer view.

With advancements in large language models (LLMs) and generative AI, organizations can now bridge this gap. To succeed in the AI era, companies need to develop integrated, federated, intelligent, and actionable solutions across all customer touchpoints while managing complexity.

Leveraging Unstructured Data for Superior AI Performance

For instance, when a customer seeks help with a recent purchase, they typically start with a company’s chatbot. To ensure a relevant and positive experience, the chatbot must be informed by comprehensive customer data, including recent purchases, warranty details, and past interactions.

Additionally, the chatbot should draw on broader company data, such as insights from other customers and internal knowledge base articles. This data can be spread across structured databases and unstructured files, like warranty contracts or knowledge articles. Accessing and utilizing both types of data is crucial for a satisfying interaction.

The key to accurate AI responses is augmenting LLMs with both real-time structured and unstructured data from within a company’s systems. An effective approach is Retrieval Augmented Generation (RAG), which combines proprietary data with generative AI to enhance contextuality, timeliness, and relevance.

Ensuring Relevance Across Scenarios

A unified view of customer data—both structured and unstructured—provides the most relevant information for any situation. For example, financial institutions can leverage this comprehensive data to offer real-time market insights tailored to individual banking needs, providing actionable advice based on current information.

Companies are increasingly exploring RAG technology to improve internal processes and deliver precise, up-to-date information to employees. This approach enhances contextual assistance, personalized support, and decision-making efficiency across the organization.

The Role of Data to Harness AI

Preparing Data for AI: Key Steps

Data Discovery and Quality: Identify all data sources and assess their quality to ensure they are suitable for generative AI models.
Data Freshness and Retrieval: Ensure data is current, relevant, and easily retrievable, combining structured and unstructured data for optimal results.
Data Activation: Implement the necessary pipelines and integrations to enable RAG to access and utilize data effectively when needed.

By addressing these areas, organizations can harness the full potential of AI, transforming customer interactions and enhancing service efficiency. Talk to Tectonic today if your data is ina disarray.