Originally posted on Forbes Technology Council As organizations increasingly embark on their digital transformation journey, IT is turning into a profit center, rather than a cost center. CIOs (chief information officers) are more than often referred to as chief innovation officers. New roles like chief data officer and chief analytics officer are rising to prominence. AI and data are at the center of this transformation, as CxOs are faced with daunting challenges in.

Writing clean and efficient code can be time consuming, but with the right tools, it can be much easier. In this blog post, we will explore how to use Copilot for code autocompletion and JFrog Artifactory for your package management.

IDC published a Market Perspective report discussing implementations to leverage Generative AI. The report calls out the Elastic AI Assistant, its value, and the functionality it provides. Of the various AI Assistants launched across the industry, many of them have not been made available to the broader practitioner ecosystem and therefore have not been tested. With Elastic AI Assistant, we’ve scaled out of that trend to provide working capabilities now.

Top tips is a weekly column where we highlight what’s trending in the tech world today and list out ways to explore these trends. This week we take a look at the effect of AI-related over-saturation and show you four ways to work around it.

Business-critical infrastructure and services generate massive volumes of observability data from many disparate sources. It can be challenging to synthesize all this data to gain actionable insights for detecting and remediating issues—particularly in the heat of incident response.

In the bustling world of customer service, where speed, personalization, and seamless interactions reign supreme, a new technological powerhouse has emerged to transform the support landscape—Artificial Intelligence (AI). AI effortlessly adapts to the ever-changing needs of customers and businesses alike, revolutionizing the way support is delivered.

Large Language Models (LLMs) can give notoriously inconsistent responses when asked the same question multiple times. For example, if you ask for help writing an Elasticsearch query, sometimes the generated query may be wrapped by an API call, even though we didn’t ask for it. This sometimes subtle, other times dramatic variability adds complexity when integrating generative AI into analyst workflows that expect specifically-formatted responses, like queries.