AI-powered content discovery: Use cases and best practices
From promoting products or services to documenting processes, content is crucial at every stage of your business. Yet, content management across multiple platforms and applications can be complex due to its variety of formats and lack of centralization (not to mention the time it takes to find information buried within documents and spreadsheets).
With AI-driven retrieval-augmented generation (RAG) systems and metadata, you locate information easily while getting personalized content recommendations based on your history — a process known as content discovery.
In this guide, let’s explore the definition of AI-powered content discovery and its benefits, top use cases, and best practices for taking full advantage of your data.
What is AI content discovery?
AI content discovery is the practice of using artificial intelligence to find and recommend content — including visuals, text, audio, and other formats — based on users’ interests, behavior, and preferences. It leverages technologies like machine learning (ML), natural language processing (NLP), and RAG to analyze vast quantities of data and deliver suggestions to users according to their profiles.
You can use AI in content discovery when you want to:
- Search specific information across documents within data storage
- Ask questions related to your contracts, agreements, or reports
- Locate recently accessed files in your document management system
- Suggest products that your customers may be interested in
- Deliver personalized content recommendations to your target audience
How AI in content discovery works
Based on search topics, browsing history, and content consumption patterns, AI analyzes data to tailor content based on your interests. Here’s a breakdown of how the typical content discovery process works:
- Data collection: AI systems gather information from your behavior, content metadata, and external data, and algorithms identify patterns and interests based on these sources
- Analysis: NLP understands the meaning and context of text-based content, while AI analyzes the semantic relationships between words to identify information based on your intent
- Prediction: ML models predict your preferences based on historical data
- Personalization: AI creates detailed user profiles based on this information and recommends content that aligns with your profile
- Continuous improvement: AI keeps learning from interactions and refining recommendations over time, while systems regularly update their algorithms to maintain accuracy and relevance
Discover how to use AI for business.
Why you should use AI to discover content
With content discovery in your enterprise AI strategy, you get the following benefits.
- Efficient content management: Traditional content discovery, which involves manual search and curation, is time consuming and often unable to keep pace with your business’s growing production. AI automates content creation, tagging, categorization, and summarization, improving management and retrieval efficiency.
- Deeper insights on unstructured data: A Box-sponsored IDC white paper reveals that 90% of information generated by organizations is unstructured. This data is often siloed across departments or software systems, making it difficult to find and reuse. AI can analyze customer feedback, market reports, and other documents to extract trends, sentiments, and potential issues, thereby facilitating decision-making.
- Personalized customer experience: Imagine using AI to analyze client reviews of your products and support. With AI-powered content discovery, you can extract insights into customer sentiment and use this information to create segmented marketing campaigns, develop new products, and deliver more personalized customer experiences.
Top use cases for artificial intelligence in content discovery
A McKinsey survey reports that AI adoption by organizations increased from 55% in 2023 to 72% in 2024. The study reveals that businesses are now using this technology for more functions than in previous years.
Explore the best use cases for AI in content discovery:
| Use case | How it works | Who can benefit from it |
|---|---|---|
| Enhanced search | Retrieval-augmented generation models use metadata to refine search queries across documents, delivering more accurate and contextual responses | Legal teams, insurance companies, and government agencies that need to locate specific clauses or precedents within contracts, policies, and other extensive documents |
| Document retrieval | Based on metadata, RAG models identify and prioritize the most relevant documents from large repositories | Financial services, publishers, hospitals, real estate agencies, and businesses that require frequent access to customer files, articles, patient records, agreements, or any other type of text-based content |
| Automated question answering | Conversational AI systems use NLP to understand and provide accurate and contextually relevant answers to users | Customer support teams, law firms, educational institutions, and businesses that need to find answers related to their content |
| Information summarization | AI algorithms extract key information from large volumes of text and generate concise summaries | Life sciences organizations, market research firms, and businesses dealing with large volumes of text-based documents |
| Content curation and personalization | AI analyzes user preferences to recommend content or products based on user engagement and feedback | Media companies, e-commerce platforms, content marketing teams, and other businesses that aim to deliver more relevant content to users |
| Sentiment analysis | AI determines the sentiment expressed in text, whether positive, negative, or neutral | Customer support teams, sales departments, and any other area that needs to understand client feedback and identify potential issues regarding their brand reputation |
| AI content generation | AI generates content ideas in formats such as text, images, and videos based on given prompts or data | Marketing teams, retail companies, and businesses looking to improve their content discoverability through blog posts, social media content, product descriptions, and more |
Discover what drives the increased adoption of AI in enterprises.
How to use AI in content discovery: Best practices
According to a survey by Gartner, 92% of organizations are considering investing in AI-powered software. If your company is among them, follow these best practices before choosing an AI content discovery platform:
1. Centralize and integrate content repositories
Effective data lifecycle management prioritizes confidentiality, integrity, and availability. Splitting information across multiple platforms leads to inefficiencies in content discovery.
Cloud storage systems offer a centralized content repository, making it easier for AI tools to scan and retrieve relevant data. This approach ensures all discoverable content is accessible from a single location, reducing the time spent searching for information.
2. Use platforms that facilitate content curation and discoverability
When you have large digital asset libraries, you need platforms that enable content curation and discoverability to access this vast amount of enterprise information.
Imagine your marketing team trying to find a video among thousands of documents, images, and files from other departments. They might spend a lot of time searching, only to realize that a critical asset is lost in an extensive repository of unstructured data.
To simplify the search and retrieval process, opt for AI-powered content discovery tools that allow you to organize files in collections by department, project, or team. This way, everyone can quickly locate content using AI while your key information stays safe.
3. Integrate RAG and metadata for enhanced discovery
RAG is a technique that combines retrieval and generation methods to improve AI response quality and relevance. Metadata — data that describes other data — provides essential context, categorization, and searchability for your content. Integrating both allows AI systems to better understand, index, and retrieve information based on specific criteria such as keywords, authors, dates, and more.
You can also extract more insights from content by giving additional context about each piece. For example, with RAG, you can generate a summary of a report that highlights key findings and trends based on the metadata, or answer questions about the origins of specific documents based on their creation date, author, and associated keywords.
Opt for a suitable retrieval method, such as keyword-based or semantic search, that aligns with your metadata management best practices and RAG implementation. On top of that, take advantage of metadata templates to standardize and categorize content. Some platforms offer pre-built options that you can customize to match your organization’s specific needs and content types.
Level up your AI initiatives with our leading content discovery platform
Box AI offers a suite of capabilities that integrate advanced AI models into a single platform. With the Intelligent Content Cloud, you can easily find valuable information hidden in your documents, summarize content, and unlock powerful insights from unstructured data.
Here’s how AI helps content discovery with Box:
- Data-driven insights in real time with Box Hubs: Ask questions across multiple documents, spreadsheets, and presentations and get instant answers
- Easy integration with other tools: Forget having to constantly switch between different interfaces — and extend AI capabilities to apps you already use
- Content generation and summarization: Empower your teams to automatically create, review, sum up, and refine information with simple prompts
- AI Trust: Responsible, secure enterprise-grade AI that protects your data and gives you complete control over its use
Reach out to us and let’s discuss how to implement AI content discovery across your enterprise.
While we maintain our steadfast commitment to offering products and services with best-in-class privacy, security, and compliance, the information provided in this blog post is not intended to constitute legal advice. We strongly encourage prospective and current customers to perform their own due diligence when assessing compliance with applicable laws.