Building top-tier enterprise-grade intelligence using LLMs has traditionally been prohibitively expensive and resource-hungry, and often costs tens to hundreds of millions of dollars. As researchers, we have grappled with the constraints of efficiently training and inferencing LLMs for years.

New research shows overconfidence about AI could lead to a higher risk of failure and costly mistakes amounting to hundreds of millions of dollars.

Ever since the release of ChatGPT in November 2022, organizations have been trying to find new and innovative ways to leverage gen AI to drive organizational growth. LLM capabilities like contextual understanding and response to natural language prompts enable the development of applications like automated AI chatbots, smart call center apps, or for financial services.

For independent software vendors (ISVs), creating unique differentiators and value increasingly entails adopting the latest technologies to enhance your product experience, especially in an ever-evolving competitive landscape with multiple new tech solutions. AI analytics is the latest emerging field in business intelligence (BI) solutions that offers new sophisticated capabilities for the user experience (UX) of your product’s analytics component.

Hakkoda’s 2024 State of Data report found misalignment between data leaders on AI readiness.

The ever-growing tide of data, fueled by analytics and AI, places a significant strain on data center resources and increases energy consumption. With more stakeholder scrutiny and evolving regulations, such as the EU’s Corporate Sustainability Reporting Directive (CSRD), and U.S. regulations on the horizon, organizations are taking notice now more than ever before. As a result, organizations are prioritizing sustainability in their IT strategies.

Artificial intelligence (AI) is something that, by its very nature, can be surrounded by a sea of skepticism but also excitement and optimism when it comes to harnessing its power. With the arrival of the latest AI-powered technologies like large language models (LLMs) and generative AI (GenAI), there’s a vast amount of opportunities for innovation, growth, and improved business outcomes right around the corner. All of that technology, though, depends on data to be successful.

At Qlik, we're witnessing a thrilling shift in the landscape of data analysis, customer engagement, and decision-making processes, all thanks to the advent of generative AI, especially Large Language Models (LLMs). The potential for transformation across all sectors is enormous, but the journey toward integration can be daunting for many businesses with many leaders wondering where to start in integrating the exciting capabilities of AI into their daily workflows.

We are thrilled to announce Tricentis Copilot solutions, a collection of advanced generative AI capabilities available across our products that help customers boost their efficiency throughout the entire testing lifecycle. With Tricentis Copilot solutions, you can autogenerate manual tests from requirements, optimize your portfolio, autogenerate custom code, and get meaningful insights.

If you were to stop someone walking down the street and ask them how long artificial intelligence, or AI, has been a hot topic, they might say it’s something that’s emerged mostly in recent years. But AI has been around for a long time, with the term first being coined as long ago as 1955. Generative AI however is a different beast, and one that's largely responsible for moving the topic of AI to the tip of everyone’s tongues – from consumers to enterprises alike.