Systems | Development | Analytics | API | Testing

Artificial intelligence (AI) agents are everywhere. Salesforce has Agentforce, Google launched Agentspace, and Snowflake recently announced Cortex Agents. But there’s a problem: They don’t talk to each other. Your customer relationship management (CRM) agent doesn’t know what insights your data warehouse agent has. Your knowledge retrieval agent operates in isolation. Instead of having a connected AI ecosystem, we’re repeating history and creating AI silos.

We know the role that AI can play in modern business, and the benefits it brings to employees and customers. But launching and sustaining a successful AI project remains a critical challenge for many organizations. Technology leaders across the globe are being tasked with using AI to drive business success, and it is becoming a vital pillar in reaching strategic goals.

“AI is inevitable, but is your data ready for all AI has to offer?” That was the unspoken question every keynote, panel, and hallway conversation sought to answer at the 2025 Gartner Data & Analytics (D&A) Summit. Gartner’s response was loud and clear: AI can drive incredible value, but without a good data foundation, it’s garbage in, garbage out.

Last week, we got a question from one of our users asking us how to use Kotlin Apply. Specifically, the reader wanted to know whether it was best to use the apply function in their Android application, or another of the many Kotlin scope functions. So we got to thinking: Why not write an article about the whole topic of Kotlin scope functions? After all, they’re awesome: they let us write readable, concise code in Kotlin, and work with an object without the need for repeated references.

The Head of Enterprise Data, Analytics & AI at IFF sat down with Fivetran to unpack how to successfully implement AI while balancing both innovation and governance concerns.

AI is quickly transforming the way businesses operate, turning what was once futuristic into everyday reality. However, we're still in the early innings of AI, and there are still several key limitations with AI that organizations should remain aware of to ensure that AI is being leveraged in a safe and productive way.

The excitement around large language models (LLMs) and agentic AI is justified. These systems can summarize, generate, reason, and even take actions across APIs — all with minimal human input. However, as enterprises race to integrate LLMs into real-world workflows — especially when those enterprises operate in regulated environments and/or deal in sensitive data — one fundamental question looms large.