White Paper

AI guidelines for businesses

The areas of application of artificial intelligence are so diverse that it is often difficult to find the right point of departure. After all, AI technologies can be used in virtually all sectors of a company to make processes more efficient or to find new approaches for mastering tasks. In order for AI to be successfully integrated and to achieve the desired success, companies should carefully consider the initial steps.

Artificial Intelligence in the Life Sciences

The life sciences and big pharma industry have embraced digital transformation and process automation. As companies in this industry enter the digital age, there is an industry-wide push to digitize research and development.

Critical Best Practices for Information Insight

Suppose your organization is looking to start using enterprise search or similar services. In that case, it’s essential to know what constitutes best practices in this field. If your company has a hyperautomation initiative, then, you're looking to automate almost every process in your organization to the highest possible degree. But regardless of your endeavors, you should always seek to streamline your workflow and optimize activities in your business. Platforms are constantly evolving, and Insight Engines are no exception.

Enabling Digital Dexterity: How Search is Creating the New Remote Work Experience

In the bird’s eye view of 2020’s aftermath, new needs are emerging for digital dexterity in the workplace. What organizations will do to stay competitive involves taking another look at how their teams learn and thrive in the new remote work landscape.

Generating Insights and Connecting Information With Large Language Models

This exclusive whitepaper will explore large language models (LLMs) and how they help organizations overcome data and knowledge silos to enhance knowledge-finding and promote valuable knowledge-sharing. How can your company recognize insights and business-changing information faster and safer with generative AI? How do large language models make connecting dots of information across the business landscape easier and faster, and how can you use them securely by integrating them into an existing platform? 

Information insight for your business

We’ve officially entered the Information Age. Just ten years ago, the amount of data generated globally was roughly 30 exabytes. In the space of only four years, this figure had already mushroomed to ten times its previous level.  Today, experts estimate that by 2025, the combined volume of data generated, captured, copied, and utilized worldwide will skyrocket past 180 zettabytes.

Interacting with conversational engines

Chatbots have been the hottest trend in the field of customer service for a number of years now. Yet studies indicate that when it comes to conventional chatbots and the way artificial intelligence is being applied, there is still plenty of room for improvement.

The many facets of digital transformation

The myriad of new developments that digitalization has spawned in recent years has had a profound impact on companies, industries, and indeed society as a whole. In order to capitalize on these developments and actively shape the transformation process (catchphrase: digital transformation), companies have to adopt a new approach to strategic planning – specifically, the development of a concrete digital strategy.

Using Intelligent Search to Automate Bid Management and Sales

There are many key verticals and departments throughout the business landscape that have transformed to automation faster than others. For example, customer service, e-commerce, marketing, and even human resources management have undergone a considerable amount of hyperautomation over the last several years to a decade.

Using Knowledge Graphs for Ultimate Business Knowledge Comprehension

Data and key business information can continuously be extracted with the help of specialized AI techniques and machine learning models. However, extraction versus comprehension are two very different things, making it critical to have a knowledge base to integrate numerous datasets and show the relationship between different data points.