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The manufacturing industry can benefit from AI, data and machine learning to advance manufacturing quality and productivity, minimize waste and reduce costs. With ML, manufacturers can modernize their businesses through use cases like forecasting demand, optimizing scheduling, preventing malfunctioning and managing quality. These all significantly contribute to bottom line improvement.

Providing your analytics users the ability to get answers from their data is useful, but only if the solution can guide them to ask the right questions. The rising integration of artificial intelligence (AI) and machine learning (ML) in many business intelligence (BI) solutions has enabled new innovative approaches in combining natural language techniques with self-service analytics, resulting in helpful NLQ tools.

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team. This is the foundation of a multi-agent system—networks of agents, each with a specific role, collaborating to solve larger problems. When building a multi-agent system, you need a way to coordinate how agents interact.

Fivetran becomes the only fully managed platform that can move trusted, governed data in any direction, powering real-time decisions, AI, and business operations.

Are you leading the development or marketing of an app or website that involves protected health information (PHI)? If you have not heard the term before, PHI involves information related to an individual’s health status, healthcare, and healthcare payments. The Health Insurance Portability and Accountability Act (HIPAA) protects said individuals and their health-related information, which you must comply with before progressing further with your product or service.

The architecture of the modern data lake offers considerable advantages over data warehouses.

Have you ever felt personally offended by a 4-star review, even after giving 110% to your customers? You’re not alone. Even the smallest product hiccup can spark a Reddit thread or a sarcastic TikTok. Getting customer engagement right is more challenging and important than ever. You will likely face ongoing challenges in customer engagement with new and evolving expectations, and meeting these needs is vital to fostering brand loyalty.

Just like every impressive building starts with a strong foundation, every remarkable capability in an AI agent can be traced back to its framework. AI agent frameworks or agentic AI frameworks make it possible to create smart, efficient AI agents that can serve as simple chatbots, facilitate agentic automation, or contribute to complex use cases in finance, supply chain, healthcare, manufacturing, and robotics as part of a multi-agent system. But what are AI agent frameworks?

Emerging technologies like generative AI (GenAI) and agentic AI are poised to significantly enhance IT operations. These advancements offer new, truly transformative ways to manage, optimize and automate IT environments, and are certain to improve efficiency and foster innovation. GenAI’s ability to process vast amounts of unstructured data and agentic AI’s autonomous decision-making capabilities span predictive analytics to automated problem-solving.