According to recent research from TechTarget’s Enterprise Strategy Group (ESG), generative AI will change software development activities, from quality assurance to debugging to CI/CD pipeline configuration. Many organizations are turning to generative artificial intelligence and automation to free developers from manual, mundane tasks to focus on more business-critical initiatives and innovation projects. Therefore, it’s no surprise that generative AI is poised to have a massive impact by automating software development tasks today and in the near term, according to ESG’s data. In the resear ..read more

At every organization, the digital landscape is evolving rapidly, presenting IT operations teams with unique challenges. Teams require innovative approaches to manage vast amounts of data and complex infrastructure as well as the need for real-time decisions. Artificial intelligence, including more recent advances in generative AI, is becoming increasingly important as organizations look to modernize how IT operates. As a result, organizations are turning to AI to automate tasks—from code development to incident response—to reduce manual effort and human error, and to boost workforce efficien ..read more

In today’s rapidly evolving landscape, incorporating AI innovation into business strategies is vital, enabling organizations to optimize operations, enhance decision-making processes, and stay competitive. The annual Google Cloud Next conference explores the latest innovations for cloud technology and Google Cloud. This year, Google’s event will take place from April 9 to 11 in Las Vegas. Google Cloud users will come together to learn from Google experts and partners on topics from generative AI to cloud operations and security. As organizations continue to expand within cloud-native environm ..read more

Lest readers believe that business digital transformation has fallen out of fashion, recent data suggests that digital transformation initiatives are still high on the agenda for today’s leaders. According to a recent Thomson Reuters survey, nearly 50% of C-level executives said that business digital transformation was their top priority over the next 18 months, followed by reducing costs (44%) and increasing customer satisfaction (44%). Business digital transformation and modernization are enduring objectives, despite the rise of generative AI as a priority over the past year. That’s why, ac ..read more

Knowledge is power. Knowing who is responsible for specific areas and services is a vital skill that saves time and hassle. This can quickly become overwhelming and hard to manage in today’s complex microservice environments. Combining services and responsible teams increases transparency, simplifies the identification of the right people, enables automation, and saves time and money. As always, work must be done before you can reap the rewards. This includes preparing the environment with the required metadata to provide the ownership information transparently and when needed. These efforts ..read more

The artificial intelligence (AI) age has arrived and the public sector is steadily moving forward with AI advancements. According to Gartner®, “annual global survey of over 2,400 CIOs and technology executives found that 25% of governments have deployed, or plan to deploy GenAI in the next 12 months. A further 25% plan to deploy in the next 24 months.”1 Within the U.S. federal government, agencies are leveraging AI tools for more than 700 use cases. There are many ethical, societal, and workplace-related questions about this technology. During Episode 79 of the Tech Transforms podcast, D ..read more

In a digital-first world, site reliability engineers and IT data analysts face numerous challenges with data quality and reliability in their quest for cloud control. Increasingly, organizations seek to address these problems using AI techniques as part of their exploratory data analytics practices. Exploratory data analytics is an analysis method that uses visualizations, including graphs and charts, to help IT teams investigate emerging data trends and circumvent issues, such as unexpected traffic spikes or performance degradations. Challenges to exploratory data analytics Among the challen ..read more

Today’s organizations flock to multicloud environments for myriad reasons, including increased scalability, agility, and performance. However, these environments can drown enterprises in data, forcing them to adopt multiple tools and services to manage and secure it. This fragmented approach adds complexity and opens the door to security vulnerabilities. In fact, according to recent Dynatrace research, 85% of technology leaders say the number of tools, platforms, dashboards, and applications they use adds to the complexity of managing a multicloud environment. Further, 84% of technology leade ..read more

While off-the-shelf models assist many organizations in initiating their journeys with generative AI (GenAI), scaling AI for enterprise use presents formidable challenges. It requires specialized talent, a new technology stack to manage and deploy models, an ample budget for rising compute costs, and end-to-end security. Many organizations haven’t even considered which use cases will bring them the biggest return on AI investment. This blog post explores how AI observability enables organizations to predict and control costs, performance, and data reliability. It also shows how data observabi ..read more

>> Scroll down to see predictive capacity management in action (14-second video) Our recent blog post, Stay ahead of the game: Forecast IT capacity with Dynatrace Grail and Davis AI, showed how Dynatrace Notebooks are used to predict the future behavior of time series data stored in Grail™. This follow-up post introduces Davis® AI for Workflows, showing you how to fully automate prediction and remediation of your future capacity demands. The anticipation of future capacity demands makes it possible to completely avoid critical outages by notifying you days in advance, well before ..read more