Is AI coming for our jobs or can it help make our jobs easier? Learn about the growing role of AI in test automation.

Imagine finding yourself in an airport, receiving a message from your travel app: “Due to severe weather conditions, your flight is delayed by 3 hours. We anticipate your gate opening within the next hour. Here’s a $30 voucher for refreshments while you wait.” While this scenario might seem like something out of a Hollywood movie, in reality, it could lead to tense confrontations and disruptions. That’s why clear communication and support before travel are crucial.

The life sciences industry is rapidly embracing artificial intelligence (AI) to automate complex processes, enhance efficiency, and improve outcomes. From drug development to patient care, AI-driven automation is transforming the landscape. Here are some of the top artificial intelligence use cases for process automation in the pharmaceutical industry.

It’s not a surprise that in today’s challenging economic landscape, rising costs pose a significant threat to the telecommunications industry. Consider that in 2022, Bain Capital was predicting that Telcos would grapple with increased personnel and escalating operating costs due to inflation. And here we are.

At Qlik World 2023, Mike Capone stood on stage and proudly announced the acquisition of Talend to form Qlik’s Data Business Unit. The Data BU had the charter to combine the best of Qlik Data Integration with Talend and Stitch Data to deliver market-leading data integration and quality solutions.

The rise of AI brings unprecedented opportunities, deeply rooted in the transformative power of data. As organizations strive for smarter, faster outcomes—whether accelerating product launches, streamlining supply chains, improving customer experiences, or enhancing workforce productivity—they must address five key needs for enterprise AI adoption: Move/ Transform - To effectively adopt AI, companies need to bring together and transform data of all types from all sources.

There are many ways AI can improve government case management processes. Examples include: AI capabilities often depend on a large language model (LLM), an advanced AI system that can understand, analyze, interpret, and generate human language. It uses deep learning techniques to predict and produce coherent text based on input prompts. A large language model trained on government agency data is capable of tasks like text extraction, translation, summarization, and conversational responses.

We are excited to announce a tech preview of Cloudera AI Inference service powered by the full-stack NVIDIA accelerated computing platform, which includes NVIDIA NIM inference microservices, part of the NVIDIA AI Enterprise software platform for generative AI. Cloudera’s AI Inference service uniquely streamlines the deployment and management of large-scale AI models, delivering high performance and efficiency while maintaining strict privacy and security standards.

In an era where artificial intelligence (AI) is reshaping enterprises across the globe—be it in healthcare, finance, or manufacturing—it’s hard to overstate the transformation that AI has had on businesses, regardless of industry or size. At Cloudera, we recognize the urgent need for bold steps to harness this potential and dramatically accelerate the time to value for AI applications.