Operations | Monitoring | ITSM | DevOps | Cloud

What it takes to prove AI value with LLM observability Over six months, the Elastic IT team ran internal AI applications that returned $2.5 million in operational time to the business.1 A conversational support assistant moved us from zero digital resolution, where anything complex became a ticket, to 30% of support interactions closing without one.

How context engineering creates production-ready agentic AI What if the AI strategy you spent the past year building is already being measured by a completely different set of rules? I recently joined Amy Machado, senior research manager at IDC and Jim Malone, senior contributing editor at CIO Marketing Services, for a webinar where we explored how buyer expectations, architectural requirements, and evaluation criteria are shifting as enterprises move from search-driven experiences to agentic AI.

As financial institutions move from AI experimentation to autonomous operations, trusted context, governance, and observability become the foundation for enterprise-scale Agentic AI. Artificial intelligence in financial services is entering a new era. Historically, financial services companies have focused on deploying generative AI to improve productivity, enhance customer experiences, accelerate software development, and streamline operations.