Searching isn't the problem. Context is the problem.
Most companies think they have a search problem. That is not true. The information is there. The problem is that this information is scattered across systems that know nothing about each other.
Your service desk searches Topdesk for an incident, but the root cause is in the CMDB. Your engineer will find a configuration in Azure DevOps, but the associated documentation is in Confluence. Your manager wants control information, but must consult three systems and solve the puzzle himself.
Every time someone in your organization manually collects context, you pay for it. In time, in quality, in missed connections.
Three levels of data maturity
Where is your organization located?
Level 1: Search by system This is where most companies are located. Five systems, five search bars, the same frustration five times. Information is technically findable, but practically inaccessible. Your team develops workarounds. Some people “know where it is.” That is not a strategy. That is vulnerability.
Level 2: One layer of searching about everything. You implement a central search solution, such as Elastic Workplace Search, that unlocks multiple sources via one interface. No migrations, no months of integrations. Simply: one place to look. A huge step forward. But you're still dependent on the right search terms. And from a person who interprets the results.
Level 3: An AI agent who understands your data. This is where searching stops and interaction begins. You ask a question in normal language. The agent searches your systems, combines data from various sources and gives you an answer with context. Not ten search results to sift through. An answer.
The difference between levels 2 and 3 is not technical. It is fundamental. You go from “finding information” to “gaining insight.”
What we are building for a customer
With one of our customers, we are taking this step by step. We started with Elastic Workplace Search on SharePoint and Confluence, with a roadmap to OpenText, Azure DevOps, and Topdesk.
In parallel, we built a CMDB Search Agent. That agent gives you natural language access to your entire IT landscape. Think of asset information and inventory, ownership and governance, change management, security and compliance, data quality. All domains where you now manually click through dashboards or write queries.
With the agent, you simply ask a question:
- “What applications are running on this server?”
- “What changed last week and what does that mean for current projects?”
- “What incidents are related to this configuration and who owns it?”
Do not open dashboards. Do not write queries. Just ask and get an answer.
The next step is to connect these two worlds. Workplace Search unlocks your documents and process knowledge. The CMDB agent unlocks your technical configuration data. Combine the two, and you'll get questions like:
- “Which applications run on this server and where is the documentation?”
- “What incidents are associated with this configuration and where can I find the associated procedures?”
One agent who searches your technical data as well as documentation and process knowledge. That is not a piece of the future. This is the next release.
Why this doesn't happen by itself
Here is the crux. Most search projects stop at level 2. A nice search solution is implemented, everyone is satisfied, and then little changes structurally.
Why? Because searching feels like a solved problem once you have one interface. The real potential of AI agents that combine data, provide context and provide insight remains untapped. Not because it's technically impossible. But because no one asks the question: what would be possible if our data is not only discoverable, but understandable?
Towards an agentic AI framework
What starts as an IT Operations tool grows into something that affects the entire organization. The CMDB agent is now helping engineers. But combine that with Workplace Search, and your team leads get an overview without digging. Your managers receive management information without having to request reports. Your employees find what they need in seconds.
And the logical step after that: multiple agents working together. A CMDB agent, an incident agent, a compliance agent, a FinOps agent. Each specialized in their own domain, but able to combine data together and translate it into useful insights. An agentic AI framework, where you no longer work with separate tools and reports, but with one integrated network that provides real-time insight and allows you to proactively manage instead of analyzing afterwards.
What this means in concrete terms
Spend less time searching. That is the obvious advantage. But the real difference lies deeper.
Better decisions. You work with information that is current and has context. No more separate data points, but insight into how things are related.
More control over your IT landscape. Links between systems, configurations and incidents become visible. Bandages that you would never make manually.
More control over security and compliance. Find out in seconds which configurations are different and where the documentation is missing. No more manual audits to uncover that.
A solution that grows with you. The architecture scales within the Elastic Stack. Every data source you add makes the agent smarter. Without having to migrate existing systems.
Workplace Search lays the foundation. The AI agent makes it usable. The combination changes how you work with data.
From searching to understanding. From understanding to steering.
Do you want to know what this can mean for your organization?
We are happy to help you out. See what we do in terms of Elastic Search & AI or contact us directly for an interview.
