How AI-Driven Enterprise Search is Transforming E-commerce Businesses

On the Internet, search functionality is omnipresent. If you’re on a reasonably large website or online store, there’s a good chance it’ll have its own dedicated search bar. Moreover, the address bar of your browser can always double up as a search bar. Indeed, these are the two main forms of search that most netizens are familiar with: site search, which fetches results on a specific website or online store, and web search, which fetches results from all over the Internet (best exemplified by search engines such as Google and Bing).

However, there’s a third major application of search technology that most netizens are unaware of. This is referred to as ‘enterprise search’, and it aims to ensure that the employees of a company can always reliably find any company-internal data, files, documents or content they might want to access.

And just like site search and web search, enterprise search is also undergoing a major upheaval thanks to the increasing use of artificial intelligence (AI) and machine learning (ML).

Let’s take a closer look at what exactly enterprise search is, why it’s so critical to businesses and how relatively new developments in AI are transforming it for the better.

What is enterprise search?

Enterprise search refers to a powerful software solution that enables organizations to efficiently retrieve information across various internal databases and repositories. It streamlines data access, promoting collaboration and productivity by offering a centralized search platform.

Companies typically generate large numbers of documents, files, emails and various other kinds of workplace content. The larger the company, the more the amount of content that is typically produced. Moreover, this data might be stored in various places: 

• on the company internet 
• in private or public clouds 
• on social media platforms 
• in content management systems
• in data lakes and warehouses
• in file systems
• on various software-as-a-service (SaaS) platforms
• in email inboxes
• on GitHub
• in various CRMs and LMSs

Thus, it should be clear that enterprise data can be extremely diverse in terms of purpose and file format, and can be stored in a variety of locations (a phenomenon referred to as ‘data fragmentation’). Moreover, the sheer amount of data a company generates on a daily basis can sometimes be staggering.

For these immense volumes of data to be useful, it should be possible to retrieve documents and pieces of information from all these sources in an effective manner. And it is this task that enterprise search engines perform.

Behind the scenes, enterprise search engines keep indexing new content as it is produced in various locations, so that they can keep track of it and retrieve it as and when required.

The problems that high-quality enterprise search solves

Businesses that use low-quality enterprise search solutions will face several issues when it comes to best using and searching through their data. Below are the three main such problems.

1. Extracting value from unstructured data

Business data comes in through a variety of sources and platforms (such as email, CRM tools, and social media), and in a variety of formats. The diversity and lack of structure of this data makes it challenging to exploit to the fullest. If this data could all be analyzed and organized in the right way, then it could yield useful insights.

2. Low search accuracy

This is a problem with keyword-based search in general. Companies using such older technology will not get highly accurate or relevant search results, which can result in a lot of lost employee productivity.

3. A lack of insights about user search behavior

By examining user search behavior, companies can identify useful patterns that can help them improve their search capabilities. To best accomplish this, companies need powerful analytics tools. 

While older search engines might also have such tools, they may not be able to transform the insights gained into useful actions. For instance, they might not be able to use search query data for purposes such as search result personalization.

Understanding the role of AI and ML in enterprise search

Search engines powered by AI and ML enable users to carry out searches using very natural-sounding queries. Thanks to AI, search engines are able to “understand” the intent behind a search (rather than mechanically looking for the specific words that the user has typed in). ML, on the other hand, enables search engines to get better at providing relevant search results over time.

Thus, AI enterprise search can allow employees to search for critical information in a much more natural manner, and can also greatly improve the relevance of the search results for any given query. In addition, AI-drive search engines also provide additional features like autocomplete and spell check, and use a technology called ‘neural information retrieval’ to intelligently rank search results by relevance.

Why AI in enterprise search

There are several reasons why using an AI-based enterprise search engine makes sense for a large number of businesses. Three of them are:

1. High accuracy

AI-based search engines are able to provide much more relevant search results. This means that employees can find what they are looking for in a much faster and easier way.

2. Ability to monitor incoming data

It is possible for AI-based search engines to carry out “continuous indexing”. In other words, as fresh data (documents, notes, forms, etc.) is ingested from various sources, the search engine can index them almost in real-time, ensuring that its search results are always up-to-date.

3. Improvements with learning loops

Machine learning (ML) relies on underlying models that can become more and more effective over time by analyzing the various searches and their outcomes. Such a positive feedback loop means that a search engine that uses ML will provide a better performance the more it is used.

The benefits of AI-driven enterprise search

In addition to the more obvious benefits mentioned above, there are several more ways in which AI-driven enterprise search helps companies. Below are just five such additional benefits of AI enterprise search.

1. Addressing long-tail queries

“Long-tail queries” are search queries that are relatively rare. They are also often relatively long, which means that they are often more specific. Given that AI-driven search engines are capable of figuring out users’ search intent and contextualizing a given query, they are better at finding relevant matches even for such highly specific queries.

2. Greater utility of generated content

The content that is generated by a company in the course of its operations is worthless if it can’t be retrieved or referred to when it’s required. By producing more relevant results, AI-driven enterprise search helps increase the utility that can be derived from any given piece of content.

3. Enhanced productivity

According to the International Data Corporation (IDC), “[a] knowledge worker spends about 2.5 hours per day, or roughly 30% of the workday, searching for information”. 

This is a startling statistic, and hints at the immense scope for improving employee productivity and morale through more effective AI-driven enterprise search. In addition, by enabling employees to search for information in a more natural manner, an AI-driven enterprise search solution can also reduce their cognitive loads, thereby improving their overall productivity.

4. Increased savings

Since time is money in the business world, the productivity losses mentioned above can also add up and eventually result in an actual financial impact on the bottom lines of companies. Thus, AI enterprise search doesn’t simply make the search process faster, smoother and more convenient: it also has a concrete impact on the bottom line.

5. Improved organizational knowledge graph

A ‘knowledge graph’, also sometimes called a ‘semantic network’, is a network that represents the relationships between various kinds of entities. Such graphs can be used to provide more relevant search results. The use of an ML-based enterprise search engine can help your organization create and refine a knowledge graph, thus leading to search results that improve over time.

Streamline your business with AI-driven enterprise search

Every company has to start thinking seriously about enterprise search once it crosses a certain size. And there are no two ways about it: an enterprise search system that provides a seamless and intuitive experience, and that automatically gets better and more accurate over time, is not merely a nice-to-have, it’s a must-have. To integrate Zevi’s AI-driven search engine into your enterprise systems, start free trial here.