Operations | Monitoring | ITSM | DevOps | Cloud

“Don’t put all your AI eggs in one chatbot.” — A guiding principle for IT investment in 2026.

The AI landscape today feels a lot like the early days of the cloud: exciting, fast-moving, and completely fragmented. Every week, engineering teams are experimenting with dozens of large language models (LLMs) from providers like OpenAI, Anthropic, Google, Mistral, Meta, and beyond. They’re tweaking prompts, testing model performance, swapping context windows, and even running multiple models in parallel to figure out which one works best for each unique use case.

Artificial intelligence is changing the way we execute our everyday operations. AI assistants are incredibly intelligent; they can write code, explain complex concepts, and answer any question you throw at them. However, they can't execute actions on their own. If you ask your AI assistant to “create a backup of my database,” it may provide you with clear instructions, run the CLI commands directly or in some cases, even trigger actions through connected agent workflows.

As organizations continue to adopt OnPage across regions and operational environments, providing an experience that feels natural and intuitive for every user has become increasingly important. Clear communication is essential in time-sensitive workflows, and being able to use the app in one’s preferred language supports clarity, confidence, and consistency. To support our growing global user base, OnPage is introducing multi-language localization across its mobile applications.

Every breakthrough begins with a question. What if we looked beyond today’s tools, buzzwords, and hype and examined the design principles shaping tomorrow’s intelligent enterprises? The What If series explores those inflection points: moments where technology meets human judgment, where automation meets accountability, and where AI begins to resemble something more like understanding than output.

Ten years ago, Datadog was a single-product company focused on breaking down the silos between dev and ops. As the shift towards the cloud accelerated and organizations transitioned to the new DevOps model, we set out to develop an observability platform that would enable these teams to safely scale faster and answer the essential questions about their services: are they available, secure, compliant, performant, and cost-efficient?