With an ever growing stream of data and information, enterprises with effective information retrieval systems can gain a competitive edge in extracting value from that information and making informed business decisions. 

In recent years, exciting developments in artificial intelligence and machine learning have brought advanced capabilities that greatly improve efficiency and accuracy in information retrieval compared to traditional techniques, ultimately driving success in today’s data-driven businesses. 

In this article, we cover the multiple ways AI and machine learning are revolutionizing information retrieval, bringing greater productivity and efficiency for organizations in customer service, document management, enterprise search and a myriad of other areas.

Evolution of Information Retrieval

Humans have a long history of organizing and storing information from the first card catalogs of ancient libraries to the search engines of today. The internet age kicked off with the introduction of the World Wide Web in the 1990s, and keyword-based search engines like Yahoo! Search laid the groundwork for more advanced forms of search. 

Keyword-based search engines match keywords from a user query to an index of a webpage or document and rank the most relevant information. They work by using ranking algorithms that calculate relevance through factors like how frequently a word appears in a document and its rarity across a collection of documents. However, keyword-based information retrieval was limited in its success because it lacked the semantic context to understand a user’s needs.

Relevant viewing: Unlocking the Power of Natural Language Search

As the web expanded, along with the volume of digital content and complexity of queries, keyword-based search engines gave way to AI-powered information retrieval using machine learning (an example of which is vector search). These advanced search technologies allowed for large-scale data analysis and semantic search capabilities. 

They were able to provide more accurate and relevant results by going beyond simple keyword matching to understanding the intent of a query and nuances in the meaning of words (e.g., the different meanings of “novel” or similarities between the words “navy” and “blue”) to better match results to the query. 

Unlike its predecessors, semantic search technology provides results based on the query’s contextual meaning and what is known about the user, such as the user’s search history and location. AI-powered search, using machine learning and natural language processing, also learns and improves over time with more data and user interactions. 

Information retrieval using machine learning and AI remains the main area of exploration and growth in IR today.

Core Technologies Driving the Revolution

Key developments in AI have led to the current revolution in information retrieval characterized by more accurate, contextual results and human-like interactions with machines. Many advanced search systems use a combination of these AI technologies. Let’s review the core technologies below.

Natural Language Processing

Remarkable gains in the accuracy and relevance of search results have come with the application of natural language processing (NLP), a subset of AI focused on helping machines understand and interpret human language, to information retrieval. 

With NLP, search engines can understand a user’s intent in forming a query through the nuances in natural language and the context around the query rather than solely depending on the exact words used. NLP accomplishes this level of contextual awareness through a combination of machine learning algorithms, deep learning techniques and computational models that process relationships and patterns between words.

For example, Google’s Bidirectional Encoder Representations from Transformers (BERT) is an NLP model that helps Google’s search engine understand human language in queries with a higher degree of accuracy than previously possible. BERT uses a machine learning algorithm to analyze the context of words in a sentence from left and right directions, a major step forward in contextual understanding from the previous method of interpreting words in a single direction.

Machine Learning Algorithms

Machine learning (ML) allows machines to learn from data and improve over time, much as humans do. It is revolutionizing information retrieval by creating systems that continuously learn and adapt to user behavior. 

In this branch of artificial intelligence, large amounts of data are fed into machine learning algorithms that learn the patterns and relationships within the data sets. The algorithms automatically improve over time in making predictions as they receive and process more data, without being programmed to do so. 

In information retrieval, ML allows IR systems to learn from each user interaction and improve the relevance of results over time, such as a user’s search history. Systems using ML learn what matters to the user and adjust results according to the user’s behavior, bringing adaptability and greater personalization into the search experience.

Deep Learning

Data sets have grown to massive proportions in the digital age. Information retrieval techniques have had to adapt to handle the complexities and ambiguities of queries against a stream of largely unstructured data, which is difficult to interpret through traditional means. 

Deep learning has played a crucial role in today’s information retrieval advances, making it possible to understand complex queries, such as ones that involve unstructured data like images, videos or social media posts. Deep learning uses multilayered, artificial neural networks designed after the human brain to detect patterns in data and process large data sets at a scale impossible for humans. 

OpenAI’s ChatGPT, for example, is a form of generative AI in information retrieval that uses deep learning to learn patterns in vast amounts of text data, understand the intent of a query, and generate human-like responses.

Relevant Reading: Best Generative AI: 8 Strategic Questions for CIOs

Key Benefits of AI and Machine Learning in Information Retrieval

Applications of AI and machine learning in information retrieval have brought important improvements and advantages to enterprises.

Enhanced accuracy and relevance

AI and machine learning integrated into IR have moved search systems past simple keyword matches to understanding user intent. Using AI technologies in deep learning and natural language processing, IR systems can process contextual data in user interactions, search history and query language to reach a level of precision in providing relevant results and recommendations previously not possible. 

Increased productivity

Today’s enterprises deal with large amounts of data and documents to manage. By reducing the time needed to find documents while increasing the accuracy of the retrieved results, AI-based technologies help employees perform their job duties more easily, boosting workplace productivity. These systems improve over time and provide increasingly personalized results and anticipating employee needs.

Cost efficiency

AI helps automate information retrieval within organizations, streamlining operations and freeing up employees for more strategic tasks. For example, NLP and machine learning can be used to automate the tagging and categorizing of document files, reducing manual labor.

Scalability

As organizations grow, their data exponentially increases, leading to challenges in quickly retrieving the relevant information to help customers and analyzing data for insights to help the business. AI and machine learning help organizations scale by adeptly processing large volumes of data and analyzing them to find patterns that provide new insights for the business. 

They can also handle concurrent queries at once without compromising performance, leading to greater customer satisfaction even as the company grows.

Real-world Applications of AI-Powered IR Systems

The benefits of AI-powered information retrieval are evident in the ways enterprises are already applying the technologies to help their businesses today.

Enterprise search solutions

Finding relevant information that is scattered across silos is a growing problem for enterprises. AI-powered search solves many of these challenges with the ability to search across apps, sources, content and data stores. Using NLP and machine learning, enterprise search solutions also provide personalized recommendations based on user profiles, behavior, and user intent, improving accuracy over time. 

For example, leading telecom provider Comcast used Coveo’s AI search platform to relaunch its intranet and improve the experience of its 100,000 employees through personalized recommendations and unified search across multiple data sources. 

Relevant reading: How Comcast Used AI and Unified Search to Transform Employee Experience

Comcast experienced a 92% boost in content sharing and an 81% search success rate. The company also saw improved productivity through decreasing the clicks needed to access content, creating a workplace where employees could easily find information.

Customer support

AI in information retrieval has been pivotal in improving customer support experiences, making digital customer self service much easier through tools like chatbots that use natural language processing to reduce the need for human intervention. Using AI to retrieve information from a knowledge repository, customer service workers can also provide answers quickly and accurately to assist customers. 

By implementing Coveo’s generative answering feature, cloud-based accounting software provider Xero was able to proactively answer customer questions before they were asked. They were able to increase self-service resolution by 20% in just six weeks. 

Ecommerce

AI-based information retrieval has been greatly influential in making ecommerce more intuitive and satisfying for customers through personalized product recommendations and immediate and accurate search results. With machine learning algorithms, ecommerce tools use a customer’s browsing history and past purchases to provide tailored recommendations and improve search results over time. 

Grappling with an inventory of over one million parts and accessories scattered across its website, truck and trailer parts distributor FleetPride needed to improve its online ordering system for its customers. 

The B2B ecommerce provider used Coveo’s AI technology to provide customers with a unified product search experience that made it easy to see accurate product information with the precision necessary to order items by exact part number.

Challenges and Considerations

While AI and machine learning bring powerful capabilities to information retrieval, there are drawbacks to consider for businesses making use of these technologies. 

Data privacy and security

Because AI systems collect and require a large volume of data from a number of sources to feed machine learning algorithms, they raise privacy concerns such as where the data came from and how it is being stored. Companies using AI information retrieval systems must ensure compliance with regulations such as the General Data Protection Regulation (GDPR) and safeguard sensitive information.

Integration with existing systems

Integrating AI systems into legacy enterprise infrastructures can present challenges involving compatibility between systems and technical complexity in aligning AI capabilities with existing systems. Organizations must ensure they have the right tools and people in place to smoothly transition these new technologies with minimal disruption.

Stakeholder buy-in

Involving stakeholders from the outset across departments (e.g., users, IT, C-levels) is  crucial for successful AI integration and implementation. Gaining cross-departmental support will lead to smoother adoption of new technologies organization-wide.

Future Trends

The integration of AI and ML in information retrieval holds tremendous potential for further advancements. 

For example, predictive analytics is finding applications in search where IR systems can anticipate user needs before they even form a query, using contextual data about the user to proactively deliver information. 

We will also increasingly see the integration of IR with emerging technologies like augmented reality (AR) and virtual reality (VR), providing opportunities for users to interact in 3D formats when performing searches or testing out products virtually in physical environments, such as customer’s living rooms. 

Another area of growth in the future will be the expanded role of AI in enhancing digital experiences across various sectors to bolster productivity, automate workflows and personalize user experiences.

Conclusion

AI and machine learning have made significant impacts on the field of information retrieval, moving systems beyond keyword matching to interpreting the intent, context and patterns within queries to produce more accurate and relevant results. 

These advancements have given companies the abilities to automate IR tasks and deepen personalization for customers and employees alike in the search and knowledge retrieval experience. As the integration of AI and machine learning in information retrieval matures, we can expect even more innovations to drive benefits to enterprises that give them a competitive edge.

Ready to bring AI-powered search to your organization? Explore solutions from Coveo.

Dig Deeper

Today’s business leaders are bending over backwards to adapt and respond to rapidly changing digital expectations. Learn about the key components to consider when evaluating how your information retrieval system stacks up in our free download, The Buyer’s Guide for Information Retrieval Systems.