Imagine you are a software developer and you are buzzing with excitement because you've just deployed a new feature at work. But suddenly, you hear whispers of a critical bug in the old functionality. That gut-wrenching moment reminds us how important it is to know when regression testing should be performed.

In the era of rapid digital transformation, it's easy to feel overwhelmed by the constant introduction of new tech trends and buzzwords. Amidst this rapid change, one term stands out as a cornerstone of this revolution: Enterprise Software Systems. These systems play a vital role in helping businesses adapt and thrive in the fast-paced world of digital innovation.

Our new research shows AI adoption is high across software testing workflows. But AI isn’t (yet) paying off in ways that matter to software teams using open-source testing frameworks like Selenium, Cypress, and Playwright. Open-source teams using AI are still spending just as much — if not more — time on painful test writing and maintenance tasks than ones not using AI. Contents Toggle.

Do you want the benefits of contract testing with much less effort? Are you convinced of the benefits of contract testing but think it’s just too difficult to roll out across your organization? You might worry that implementing Pact in your organization requires challenging changes to culture and process. In this article, I’ll show you a drastically simplified approach to contract testing that a single developer can bring online.

Large Language Models (LLMs) will be at the core of many groundbreaking AI solutions for enterprise organizations. Here are just a few examples of the benefits of using LLMs in the enterprise for both internal and external use cases: Optimize Costs. LLMs deployed as customer-facing chatbots can respond to frequently asked questions and simple queries.

Just like cloud computing changed digital businesses over time, the momentum of AI innovation foreshadows an evolution in business operations and decision-making. Instead of solely focusing on what happened or what might happen, AI will illuminate the actions that lead to better business outcomes. This is the next step in the AI evolution, one that will empower your business to shift from human-initiated, tech-assisted processes, to AI-initiated, human-supervised actions. In this world, AI is the new BI.

Your organization has decided to make the leap to SAP S/4HANA Cloud Public Edition, a strategic choice that offers improved performance, advanced analytics, and more efficient support for your business operations. While this cloud ERP offers streamlined processes and improved scalability, it also limits the level of customization that finance teams have come to rely on for tailored, actionable insights. The result post-migration?

In the modern digital landscape, data grows at an astonishing rate. Mid-sized enterprises struggle to manage its volume and velocity, let alone its value. Today’s complex data environments must span hybrid and multicloud setups. And as transformative technologies like generative AI (GenAI) matter more to business operations, they must manage skyrocketing demand for sophisticated, flexible and scalable data solutions.