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.

Gemini in BigQuery features are now generally available, including SQL and Python code generation and explanation, and the BigQuery data canvas.

What do developers really think about artificial intelligence (AI)? To many, AI is synonymous with innovation. While some noisy naysayers on the sidelines may cry the promise of generative AI (GenAI) is overhyped, the reality experienced by actual developers and leaders in the digital trenches working with GenAI and large language models (LLMs) tells a clear story: AI is a top priority in the enterprise today because it has already delivered real, tangible business benefits.

‍ The rise of artificial intelligence (AI) in testing is enabling more predictive and intelligent test generation, execution, and defect analysis. This shift aims to reduce the time and effort required for manual testing to enhance test coverage and improve the overall software quality. Justifiably, key insights from Fortune Business project the growth of the AI-enabled testing market from USD 736.8 million in 2023 to USD 2,746.6 million by 2030.

Every business has key customer behaviors it aims to drive — whether it's encouraging repeat purchases, promoting product upgrades or boosting add-on services. Yet, despite access to advanced marketing technology and rich customer profiles, most businesses still rely on broad, generalized lifecycle marketing campaigns that fail to engage with customers.