Generative AI’s (gen AI) capabilities seemed startlingly novel a year ago, when ChatGPT’s release led to an explosion of public usage and, simultaneously, intense debate about its potential societal and business impacts. That period of initial amazement and suspicion has given way to business urgency, as companies scramble to adopt gen AI in ways that leverage its potential for maximizing workforce productivity and profitability.

From personal AI to the data that’ll train it, browse our bets on how AI develops in 2024 and beyond.

Imagine your software transforming from merely a tool into a strategic partner that can automatically alert your users to trends, provide explanations of data with a click, and help you ask the right questions of your data-sets - in addition to delivering data-led insights. This is the power of AI analytics solutions for independent software vendors (ISV). Today's users demand more than just functionality. They crave intelligent software that analyzes data, surfaces insights, and empowers them to act.

Artificial intelligence (AI) is rapidly transforming the business world, and Application testing is no exception. AI-powered testing tools can help businesses to automate tasks, improve accuracy, and reduce costs. One of the most important benefits of using AI for Application testing is that it can help automate repetitive tasks. This can free up testers to focus on more complex and strategic tasks. For example, AI can automate creating test cases, executing test cases, and analyzing test results.

Microsoft Copilot is an LLM-powered AI assistant embedded across Microsoft software including its 365 productivity apps Word, Excel, PowerPoint and Teams. Copilot AI assistance features are designed to boost productivity and creativity by providing a natural language interface to knowledge, insights and workflows in Microsoft products.

AI was the hot topic of 2023 and will continue to reign in 2024: ChatGPT first launched at the end of 2022 and became a massive hit in just a few months. Google released Bard shortly after, and then, new AI tools just kept popping up, prompting marketers to learn how to leverage them to become more efficient and productive.

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. But a successful deployment of LLMs has to go beyond prototyping, which is where LLMOps comes into play. LLMOps is MLOps for LLMs. It’s about ensuring rapid, streamlined, automated and ethical deployment of LLMs to production. This blog post delves into the concepts of LLMOps and MLOps, explaining how and when to use each one.

Learn how Fivetran and Databricks work in unison to enable AI-powered upgrades to marketing ROI.

Investment in AI for manufacturing is expected to grow by 57% by 2026. That’s hardly surprising — with AI’s ability to augment worker productivity, improve efficiency and drive innovation, its potential in manufacturing is vast. AI’s predictive capabilities can help manufacturing leaders anticipate market trends and make data-driven decisions, creating financial opportunities for suppliers as well as customers.