Systems | Development | Analytics | API | Testing

Is Your AI Ready for 2025? AI Automation Testing Strategies and Trends

Artificial Intelligence (AI) is revolutionizing industries worldwide, making it indispensable for modern businesses. However, this rapid growth brings a challenge—traditional testing methods are no longer sufficient to ensure the reliability and quality of complex, data-driven AI systems that are prone to bias. To succeed in 2025, organizations must adopt specialized AI automation testing strategies that validate performance and maintain consumer trust.

Nearly half of testers struggle with AI's learning curve

AI is transforming testing—but not without its challenges. According to the State of Software Quality Report 2025, 46% of testers cite the lack of skilled personnel or the steep learning curve as major barriers to adopting AI in software testing. In this insightful message, we highlight one of the most pressing issues in the QA space: while AI has immense potential to drive efficiency and quality, teams are struggling to fully capitalize on it due to limited expertise.

Train your own AI model: Know the Buts and Hows

Have you ever wondered how apps like Google Maps predict traffic, or how Netflix knows exactly what you want to watch next? Or better yet, how can chatbots (like ChatGPT!) carry on conversations almost like humans? The magic behind it all? AI models. But what exactly is an AI model? Is it some complex algorithm sitting in a dark server room somewhere? Or is it the new digital brain behind today’s smartest tools? In simple terms, AI models are like trained minds.

Automating Hybrid Cloud Storage with IaC, Red Hat Ansible and VSP 360

At the heart of any enterprise data management strategy is the driving principle of infrastructure as code (IaC), the practice of defining, provisioning and managing infrastructure through code, and a foundational approach for enabling continuous delivery (CD).

Confluent unites batch and stream processing for faster, smarter agentic AI and analytics

On Confluent Cloud for Apache Flink®, snapshot queries combine batch and stream processing to enable AI apps and agents to act on past and present data. New private networking and security features make stream processing more secure and enterprise-ready.

EP 24 | Why AI Agents Are the Future of Enterprise AI with Cloudera's Abhas Ricky

AI agents are quickly becoming one of the most powerful tools in the enterprise AI toolkit. According to Cloudera’s latest Agentic AI Survey, 96% of enterprises plan to expand their use of AI agents. But what’s driving this surge, and how can organizations turn hype into results? In this episode of The AI Forecast, host Paul Muller sits down with Abhas Ricky, Chief Strategy Officer at Cloudera, to explore the real momentum behind agentic AI. From healthcare to telecom, Abhas unpacks how AI agents are already transforming operations with speed, intelligence, and autonomy.

Confluent Current 2025 highlights

Current 2025 featured two days of engineers figuring out how streaming tech needs to evolve in an AI-driven world. Gone are the days when talks focused on basic Kafka setup. This year, everyone was tackling complex integrations, developer happiness, and practical AI implementation. Still, the event drew a range of people, with plenty of new faces stopping by the Lenses.io booth – clear evidence that Kafka and data streaming continue to attract newcomers.