Video
Transforming Enterprise AI: Agile Operations in 2026
In this video, Broadcom’s Serge Lucio shares his 2026 outlook, explaining why true enterprise AI requires moving beyond basic chatbots to deploy domain-specific AI agents built on a foundation of highly accurate data. Learn how Broadcom is driving this transformation across its ValueOps, Network Observability, and Automation portfolios to help organizations conquer complexity, ensure data provenance, and align their execution with high-level strategy.
Video Transcript
Hello everyone. I'm Serge Lucio, the General Manager of the Agile Operations Division here at Broadcom. Welcome to the start of 2026. As we look ahead, I have to say our outlook for 2026 is incredibly optimistic.
We've all experienced the growing adoption of AI, and we've seen how it is challenging the status quo regarding how larger organizations are run. It is affecting the three areas of our portfolio serves in significant ways, and we're here to help you navigate that transformation.
Some vendors are just taking a very simple approach to joining the AI era, which is essentially slapping a chatbot on top of their UI. And yes, that provides some incremental value. But honestly, that is not enough to realize the full benefit of what AI can deliver.
Truly unlocking the potential of AI is about enabling agents where they sit, with the right kind of data, and the right kind of domain-specific intelligence. Simply put, AI without the right data doesn't work.
That is why we're taking a different path. We're focused on two things:
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Building software to ensure completeness, consistency, and accuracy of your data.
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Creating the right kind of agentic model so that people can ask meaningful, intelligent questions.
This focus extends deeply across our entire portfolio, from ValueOps, to Network Observability, and Automation. So let me break down exactly what it looks like for you.
ValueOps
Take ValueOps, our strategic portfolio management solution. We know the push for many of you is to align strategy with execution, but the data that you need to do this is extremely siloed. Historically, products like Clarity have sat at the intersection of the enterprise, which is why we acquired ConnectALL to harvest that data.
But Clarity's value goes far beyond just aggregation and normalization.
Its real power is ensuring that data is vetted, complete, and accurate. This foundation allows us to introduce the ValueOps AI Agent, or VAIA, our next-gen platform. VAIA lets you ask critical questions about capacity, budget, and timelines. But crucially, we're exposing all of this through MCP servers. This means if you're building your own agentic system, you can integrate your agents directly with ours.
Network Observability
In Network Observability, the challenge is complexity. To get a clear picture, we need to integrate fault, performance, flow, user experience, and configuration data. But data across different generations of technology comes in different forms, all the way from legacy SNMP to modern gNMI.
Frequently, this data overlaps. Configuration data obviously overlaps with SNMP and gNMI data. You need to be able to normalize all of this into one coherent system that spans all the way from layer two to layer seven.
That is exactly what we're building. Once you have this normalized data, you become much more effective at root cause analysis and automated triage. We've recently delivered pilots that demonstrate this successfully, and we plan to release these capabilities at scale mid-year.
Automation
Finally, let's talk about Automation. For years, large organizations have been layering different automation technologies, from the mainframe to traditional workload automation, to Airflow and other data pipeline automations like Confluent.
These islands of automation create challenges in governance, specifically regarding SLA management, cost management, and ensuring data is delivered when needed. For the last eight years, we've been building a platform that enables you to visualize your entire automation ecosystem through a single pane of glass.
We're evolving this platform to help you better understand costs and SLA management, but beyond that, we're increasingly focused on data provenance. This covers things like PII issues, regulatory compliance, or audits. We're building a control plane for the GenAI era. With data provenance to provide data control over where your data goes, ensuring compliance, and strictly managing what is shared with agents.
Wrapping up
We're building the reliable data foundation that makes AI work for the enterprise, and we're incredibly confident that these innovations will help you to meet and exceed your business goals in new ways. We're committed to this innovation, and we're committed to you. So let's make 2026 a transformative year.
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