Struggling with managing ever-growing volumes of content? AI advancements like Large Language Models (LLM's) as implemented by ChatGPT offer new ways to extract value from large amounts of content, but how can your organization prepare itself for these kind of use cases?
On average, 70% of all content in your organization is unstructured. Think about marketing web pages, customer support topics, social media posts, knowledge base articles, product information, etc. How can you keep this ever-growing collection of content manageable?
Without a clear approach to content governance, you face efficiency issues that turn processes into time-consuming and costly tasks. Moreover, how can you make sure your customers can find the information they are looking for in an intuitive way?
Too often, AI is seen as the magic solution to this problem. However, most content that is present in organizations nowadays is not ready for AI use cases yet: it is often dispersed across different systems, mixed with presentation logic in web pages and lacking proper annotations and metadata for AI to properly consume. Indeed, for Large Language Models (LLM's) to provide highly-accurate information and insights, additional domain knowledge, metadata and context is required.
That’s where Content Science comes in: the practice of optimizing your unstructured content, ensuring your customers get accurate and up-to-date information through AI-enabled capabilities like smart search or chatbots.
Techniques like Retrieval Augmented Generation (RAG) are not a magic bullet. Without a centralized content governance process and additional content cleanup, enrichment or classification, the output of Generative AI models often falls short to enable high-end production use cases.
Content Science is the answer to this challenge. It focuses on harnessing your unstructured content to its fullest potential by optimizing its lifecycle for AI use cases.
Implement innovative ways for customers to easily find the information they are looking for.
Automate repetitive tasks, so your teams can focus on higher-value activities, reducing inefficiencies and decreasing time-to-market.
Ground LLM's in your organizational knowledge and provide your support teams with an internal knowledge companion.
Most organizations struggle with content ROT: content that is Redundant, Outdated and Trivial. Moreover, content gets siloed across various sources over time, making it harder to use as the foundation for consistent and engaging customer experiences. A Content Pipeline allows you to apply the discipline of Content Science by transforming raw content into structured, annotated, and accessible information, significantly enhancing the performance of applied AI models.
A Content Pipeline complements your RAG setup by collecting and connecting diverse data sources. It begins by cleaning and filtering content to remove noise and irrelevant information. Then, classification and annotation processes organize the content into meaningful categories and add valuable metadata. Constructing a knowledge graph embeds these structured insights, creating rich, interconnected data representations.
By introducing a Content Pipeline, you can start working on several innovative use cases across various business scenarios, such as:
At Amexio Fuse, we know that defining an initial business case is challenging. That’s why we offer support in this crucial first step, helping you package a Content Pipeline as part of a larger AI strategy. Next to supporting you with defining a strategy, we can also help to define a first concrete pilot use-case that immediately demonstrates business value.
Let's meet up and discuss the first steps on how to kickstart a Content Science initiative in your organization.
Content Science can indeed be seen as the counterpart to Data Science: Content Science focuses on the systematic organization, classification, and management of unstructured content to enforce higher-quality results in content retrieval scenarios using AI models. Data Science, on the other hand, involves the extraction of insights from structured data through statistical analysis, machine learning, and other advanced analytical techniques. It is typically used for audience segmentation, content personalization and recommendations.
We are still in the early stages of the widespread AI adoption across industries. Because of that, organizations currently find themselves in an exploratory context, where the feasibility of AI initiatives needs to be further investigated. As a first step, we therefore propose adopting a "lean" approach, focusing on specific AI use cases fitting your business domain.
In that regard, we propose scheduling two initial workshops:
1. General introduction to AI and the most recent developments in the field. This workshop is mainly intended as inspiration and to gain insight into potential use cases as well as the risks of the technology.
2. Exploration of your business domain and definition of a pilot AI use case for your organization. This workshop has 4 distinct phases:
Implementing an AI use case often comes down to solving a content problem. Let's dive into the importance of a solid content strategy for maximizing the success of your AI initiatives.
Jan Lemmens
Solution Manager CXM