- AI-Driven Enterprise Search Solution vs Traditional Search Engines
- How Does an AI-Powered Enterprise Search Tool Work?
- Key Features of an AI-Powered Enterprise Search Tool
- Integrations and Connectors
- Intelligent Recommendations
- Search Analytics
- Role-Based Access
- Support for Multiple Search Type
- The Transition of Enterprise Search Software - The Generative AI Impact
- Where Does Appinventiv Fit In?
- FAQs around AI-Powered Enterprise Search Software Development
Imagine your investors suddenly ask you to share a detailed three years’ marketing performance report. Now while your team creates holistic year-by-year presentations for sharing with the stakeholders, the ads performance tracking sheet is missing. Considering the criticality of that report, think about the panic that will ensue in your team in this situation and the loss of productive time.
According to an IDC study, employees spend around 2.5 hours per day searching for information, which can add to over 8.8 hours every week – equating to one entire workday. This illustrates how people spend A LOT of time looking for important business data.
There is a solution that the business world has found to eliminate this issue – AI driven enterprise search tools.
A custom AI-powered enterprise search tool eliminates silos and helps business employees find information they require effortlessly, irrespective of the format. Now, you must be wondering how it is different from a typical document name entry in the search bar.
AI-Driven Enterprise Search Solution vs Traditional Search Engines
The primary difference between the two lies in the fact that while Google cannot answer “why is our shipment delayed”, it’s easy for an AI-powered enterprise search tool.
Customizable AI enterprise search software solutions powered by artificial intelligence can –
- Understand human language through semantic search and contextual understanding features.
- Recognize multiple document types like contract, invoices, and purchase orders.
- Filter search results to find micro-level information in data collections.
- Classify and categorize content according to business-decided subsets.
Now that we know what a custom AI-powered enterprise search tool can do, let us get down to the ‘How’ part of understanding the running mechanism.
How Does an AI-Powered Enterprise Search Tool Work?
On a mass scale, AI driven enterprise search solution providers give businesses an API which they use to plug into their data sources and extract information for processing and storing. But when you look at the working of an individual AI-powered enterprise search software, it is a lot more detailed.
A. Connect Data Source and Ingest Information – AI enterprise search for business intelligence comes with a connector that pulls insights from multiple data sources and connects, crawls all the structured and unstructured data.
B. Index Content – Next, a single unified index is created to enable homogenous ranking of the search results, irrespective of their source.
C. Enrich Content – The software then extracts metadata from the content and uses natural language processing facilities to classify and gather key content.
D. Analyze Content – After the documents content are recognized, they are classified and semantic correlations are created between the different content pieces.
E. Answer Delivery – The algorithms built inside the custom AI powered enterprise search tool rank passages, snippets and provide users with the most accurate response to their query.
Understanding the working of AI driven enterprise search solutions is the first step for a business to get the right custom solution built by an AI enterprise search software development company. As an extension to understanding the working of the software, it is equally critical to know the key features that define the enterprise search software development process.
Key Features of an AI-Powered Enterprise Search Tool
There are a range of features businesses look for when getting a cloud based AI enterprise search software built to provide a frictionless search experience to their employees.
Integrations and Connectors
A crucial part of AI powered enterprise search software development lies in building a connector and integration system wherein the software can connect with varied content and data sources such as HubSpot, Google Drive, and ServiceNow, etc.
AI enterprise search software should make use of AI abilities to offer contextual and personalized content recommendations on the basis of users’ search, preference, and interests.
Testing whether the enterprise search AI model works or not would require businesses to gain actionable insights into their employees’ search behavior with metrics like total search, popular search, number of clicks vs no clicks. These insights will help companies make changes in the algorithms to increase user engagement.
Noting how security is the primary factor in every AI driven enterprise search development, it is important to build an architecture where only people with permission can get their queries addressed through a limited access to key data sources. This would help ensure that unauthorized employees don’t access sensitive business data.
Support for Multiple Search Type
A custom AI powered enterprise search tool should enable users to perform searches across multiple types – natural language – like ones offered by Bing or Google, multimedia – search by videos or images, and multilingual queries.
On the back of these features, several benefits of AI powered enterprise search for business intelligence gets established where the elimination of data silos, modernization of data compliance, governance, lowered cybersecurity risks are simply the surface-level outcome.
However, while historically, enterprise search has been all about helping business users find critical information from a large pool of data sets, regardless of the type – text, image, video, etc., its long-term adoption would require custom AI powered enterprise search tool creators to go a step further.
The Transition of Enterprise Search Software – The Generative AI Impact
The benefits of AI powered enterprise search have been evident through factors like increased accuracy in results, faster decision making, employees spending less time searching for information, etc. However, for the enterprise search software uses to become more innovative and widespread, it is important for the tools to expand.
This is where generative AI comes into the picture. When incorporated in enterprise search software solutions, it doesn’t just enable users to search insights from across multiple data sets but also generate charts and use NLP-based chatbot.
Here are some ways we can expect generative AI to impact AI Enterprise search software development process and impact.
- Natural Language Processing (NLP): Generative AI models specialize in understanding and generating human-like text. By using the NLP techniques, they can strengthen AI enterprise search software used to understand their user queries, extract insights from unstructured data, and provide accurate search results.
- Content Generation: The technology can be used for creating high-quality content, like product descriptions, user manuals, or knowledge-base articles based on the data sets which are available in the enterprise. This content can then be utilized for enriching enterprise search results, offering users relevant and comprehensive information.
- Automatic Summarization: Generative AI models excel at summarizing long documents, reports, and articles into concise summaries in your cloud-based enterprise search software. By integrating their automatic summarization capabilities into your enterprise search, you can gather the main ideas and key points without having to read through the entire document.
- Question Answering: Advanced generative AI models can help businesses generate accurate and detailed answers to specific user queries. This can be particularly useful in enterprise search, where users often have specific queries related to their work. By providing precise answers, generative AI enhances the search experience and saves the search time for users.
- Personalized Recommendations: The AI algorithms can study user behavior and preferences to generate personalized recommendations. In the context of enterprise search, these recommendations can suggest relevant documents, articles, or resources based on a user’s past searches, interests, or job role.
- Multilingual Support: Generative AI models add on to the AI Enterprise search software development benefits at the back of their training in multiple languages that enables bridging of the language barrier in the enterprise search software for business intelligence. They can also be built to translate queries or documents, enabling users to search and access information in their preferred language.
Where Does Appinventiv Fit In?
At Appinventiv, we have worked with a number of enterprises struggling with streamlining their operations. Right from adopting and integrating artificial intelligence to make businesses proactive and predictive to applying OpenAI integrations for generating enterprise data specific outputs, we have helped a range of mid- and large-scaled enterprises become profitable with our AI development services.
Get in touch with our generative AI development company today to elevate your enterprise search tool to its next level.
FAQs around AI-Powered Enterprise Search Software Development
Q. What is the role of AI in enterprise search software?
A. AI-enabled enterprise search tools are used to look for information inside an organizational database. It identifies and provides indexing, searching, and displaying of specific content to authorized users across the company. Some of the top AI enterprise search tools examples that are strengthening this role include – Coveo, Elastic Workplace Search, and Sinequa, etc.
Q. What is the importance of a custom AI-powered enterprise search tool?
A. An AI search software for large organizations holds immense benefits around finding key information from millions of data sets in one click, enhancing business’s knowledge management, supporting prompt decision making, and providing smooth archive access.