It sounds counterintuitive—using a technology that has trust issues to create more trustworthy data. But smart engineers can put generative AI to work to improve the quality of their data, allowing them to build more accurate and trustworthy AI-powered applications.

Test automation involves software tools and scripts to execute tests automatically without manual intervention, which accelerates testing cycles, enhances accuracy, and minimizes human errors. Artificial Intelligence (AI) includes machine learning, natural language processing, and computer vision. These systems simulate human intelligence, enabling machines to learn from data, make decisions, and solve problems autonomously.

According to Gartner, 87% of organizations are classified as having low business intelligence and analytics maturity, meaning they struggle to extract value from their data. This alarming statistic highlights a common struggle—turning raw data into actionable insights. Product teams often find themselves overwhelmed by the sheer volume of information they collect. Extracting meaningful patterns, deciphering user behavior, and predicting market trends from this sea of customer data can seem daunting.

When it comes to managing AI projects, the Command Line Interface (CLI) can be a powerful tool. With ClearML, the CLI becomes an essential resource for creating job templates, launching remote for JupyterLab, VS Code, or SSH development environments, and executing code on a remote machine that can better meet resource needs. Specifically designed for AI workloads, ClearML’s CLI offers seamless control and efficiency, empowering users to maximize their AI efforts.

Large Language Models (LLM) are changing the way developers and QA engineers solve problems. They allow for quicker code generation, debugging, and automated testing, reducing development time by up to 40%. This shift has prompted 67% of senior IT leaders to focus on generative AI, with 33% planning to make it a top priority within the next 18 months. However, while LLM models offer immense potential, understanding how to get the most out of them while maintaining quality is important.

In today’s rapidly evolving tech landscape, it's tempting for businesses to chase after the latest trends—Artificial Intelligence (AI) being the crown jewel of them all. However, unless you're an AI and data analytics provider, focusing solely on AI might be a misstep.

Since generative AI exploded onto the global market, organizations have flocked to adopt it. SAP is no exception–late last year, the ERP launched its embedded AI copilot, Joule. In addition, SAP has invested in other AI companies, hired a chief artificial intelligence officer, and added generative AI features to its products. In order to spur cloud adoption, many of SAP’s premium AI features will only be available to RISE with SAP and GROW with SAP customers.