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.
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.
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.
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.
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.
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 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.
As AI and machine learning models work endlessly to pull data and extract information from your enterprise, it is vital to have a knowledge base to integrate numerous datasets and show the relationship between different data points. Knowledge graphs can provide context to the data in a structured flow, allowing businesses to understand the reasoning behind making certain decisions.