We’ve all been there: at that frustrating moment when you have a problem or an urgent need and the customer support line is busy. Endless searching for answers turns up nothing that matches your predicament. That’s the friction ServiceNow teams are diving into headfirst to provide each of our customers satisfactory answers with promptness that matches the urgency they feel. Generative AI is helping the teams improve customer support at a rapid pace.

Operational efficiency is as critical within the IT and engineering teams as any other part of the business. Automating repetitive tasks and reducing escalations within and to these teams is of immense value. While automation saves time and boosts productivity, the complexity of developing automation can be a limiting factor and bottleneck. Generative AI is a paradigm shift here, in that it brings consumer-style simplicity to assisting in the development of enterprise-grade automation.

Vertex AI is Google’s platform offering AI and machine learning computing as a service—enabling users to train and deploy machine learning (ML) models and AI applications in the cloud. In June 2023, Google added generative AI support to Vertex AI, so users can test, tune, and deploy Google’s large language models (LLMs) for use in their applications.

Are business outcomes, with the potential to make or break an organization’s future, becoming more important than they’ve ever been before? It sure seems that way. Embarking on the journey of business growth despite a treacherous path of challenges, all departments are biting at the bit for stability, solid strategies, and a reliable plan for what’s ahead.

Artificial intelligence is certainly a hot topic right now, but what does it mean for the networking industry? In this post, Phil Gervasi looks at the role of AI and LLM in networking and separates the hype from the reality.

As you’d imagine, generative AI has been a huge topic here at Grafana Labs. We’re excited about its potential role in bridging the gap between people and the beyond-human scale of observability data we work with every day. We’ve also been talking a lot about where open source fits in — especially if that Google researcher is right and OSS will outcompete OpenAI and friends. What role can we play to bring the community along?

As the Australian workforce braces for increased AI and automation, individuals must adapt by acquiring new skills. In doing so, Australians can expand the impact of their work, embark on more fulfilling career paths, and increase their income potential. Recent ServiceNow-commissioned research from Pearson points to major changes in the workforce—in Australia and around the world—due to the implementation of AI and other technologies.

Large Language Models (LLMs) have emerged as a powerful force capable of reshaping industries across the board. From small startups to multinational corporations, organizations are actively experimenting with LLMs, recognizing their potential to disrupt the market. This blog explores the predictions from major industry leaders regarding the future of LLMs and provides insights on how businesses can leverage this technology to gain a competitive edge.

Artificial intelligence (AI) has emerged as a transformative force, empowering businesses and software engineers to scale and push the boundaries of what was once thought impossible. However as AI is accepted in more professional spaces, the complexity of managing AI systems seems to grow. Monitoring AI usage has become a critical practice for organizations to ensure optimal performance, resource efficiency, and provide a seamless user experience.

In today's fast-paced business landscape, Large Language Models (LLMs) have emerged as powerful tools with unimaginable potential, revolutionizing various industries, driving innovation and efficiency. In this blog, we will delve into the enterprise use cases of LLMs, highlighting how companies like Walmart, Stellantis, and Commvault are leveraging this technology to enhance customer experiences, streamline processes, and democratize content creation.

Amid the whirlwind of today's job market and ever-evolving economy, one critical key to an organization's success emerges: empowering employees with growth opportunities that both entice top talent and secure the future of your business. As millennials and Gen Z progressively make up more of the workforce, investing in their career growth has become crucial to the future health of any organization.

Artificial intelligence (AI) isn’t the only thing at the heart of what organizations are doing to keep with digital transformation and drive business growth. People are, too. Development of AI actually began about 40 years ago, but for generative AI (genAI), that time is much less. The explosion of genAI has brought about an everlasting, first-of-its-kind innovation that’s accessible to just about everyone.

AI has revolutionized the way we engage online in 2023. From Chat GPT and AI Art Generators to healthcare, finance, and business, you can hardly read the news without reading the latest proclamation of how AI is poised to change every aspect of our lives. AI has brought fundamental changes to how we live and work, and we’re still scrambling to understand the impacts of these changes. Especially where their work is concerned, change can be difficult for people to embrace.

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.

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.