KScope26 brought together a highly engaged community of Essbase, EPM, analytics, and data platform users. Across the sessions and hands-on conversations, one theme stood out: teams want to keep the business value of Essbase while making it easier to work with modern data, AI-assisted workflows, and lakehouse architectures.
The interest was not only in modernization for its own sake. Attendees wanted practical ways to make familiar Essbase workflows more productive: asking questions in natural language, improving calculation development, connecting governed data, and understanding how Essbase can fit into broader AI and analytics platforms.
Below are three themes from the KScope26 sessions.
1. Essbase: Modernizing A Familiar Analytics Foundation
The Sunday Essbase Symposium reinforced why Essbase continues to matter. Customers and practitioners still rely on Essbase for dimensional modeling, hierarchies, calculations, what-if analysis, Smart View workflows, MDX, and business-user analytics. The discussion around modernization was centered on preserving those strengths while reducing operational complexity and opening the door to new AI-assisted experiences.
The AI feature interest was especially strong. The sessions highlighted areas such as natural-language query assistance, AI Query, Calculation Assistant, Ask Essbase, and MCP server patterns. These capabilities point to a more conversational and assistive way to work with Essbase: helping users translate questions into analytic queries, assisting developers with calc scripts, explaining logic, and giving AI tools governed access to Essbase context.
Two developer-facing topics were especially well embraced by the community: the Essbase MCP Server and the Essbase Python API for advanced analytic capabilities. The interest was easy to understand. MCP gives agentic tools a more structured way to interact with Essbase capabilities, while Python access helps developers, data scientists, and automation teams bring Essbase into broader analytical workflows.
For readers who want to explore more, see the Oracle blog posts on the Essbase MCP Server and the Essbase Python API for advanced analytic capabilities.
Essbase on Autonomous AI Lakehouse was another important part of the conversation. The concept is straightforward: bring Essbase closer to modern data platforms while keeping familiar tools and workflows such as Smart View, MDX, calc scripts, and REST APIs. The interest here is practical modernization: less operational overhead, better alignment with governed enterprise data, and a path for Essbase applications to participate in broader analytics and AI ecosystems.
The key takeaway: the community is interested in AI because it can make Essbase easier to use, easier to develop with, and easier to connect to modern analytics workflows.
2. Autonomous AI Lakehouse: Unlocking Enterprise Data For AI And Analytics
The Autonomous AI Lakehouse sessions and hands-on lab showed why the lakehouse conversation is becoming central to analytics strategy. Data teams are dealing with many systems, clouds, catalogs, and data formats. They want openness and interoperability, but they also need performance, governance, security, and operational simplicity.
Autonomous AI Lakehouse was presented around that balance: open lakehouse data, Oracle AI Database capabilities, governed catalog and semantic information, development tooling, and AI-powered analysis. The discussion around Iceberg, data cataloging, semantic context, and zero-copy style access resonated because teams do not want to keep copying data into new silos just to make it useful.
The hands-on lab helped make the story concrete. The lab flow focused on a data analyst experience: loading data, preparing it, and using AI-assisted tools to ask questions and analyze information. That is the right level of abstraction for many business and analytics users. They are less interested in platform plumbing and more interested in how quickly they can turn trusted data into answers.
For Essbase users, this matters because many analytical workflows depend on both business semantics and enterprise data. Autonomous AI Lakehouse can provide a modern data foundation, while Essbase can continue to provide dimensional modeling, financial intelligence, write-back, and familiar business-user interaction patterns.
The key takeaway: AI becomes more useful when it is grounded in governed data, shared semantics, and tools that analysts can actually use.
3. Essbase And FreeForm: Complementary Paths For Modern Planning And Analytics
Essbase remains important where teams need flexible dimensional applications, calculation logic, MDX, Smart View, APIs, federated data access, and integration with broader data and analytics ecosystems. The strongest message is not “Essbase versus FreeForm.” It is that the two can be understood as complementary options depending on the application pattern, administration model, and data architecture needs.
The Essbase and FreeForm session was a useful reminder that modernization is not a single path. FreeForm plays an important role for EPM Cloud use cases where customers want SaaS administration, planning and reporting capabilities, unified user management, Smart View access, and EPM platform services.
That complementary view becomes even more important as AI and lakehouse architectures mature. Some use cases fit naturally into EPM Cloud and FreeForm. Other use cases benefit from Essbase connected to Autonomous AI Lakehouse, especially when teams want open data access, governed lakehouse integration, AI-ready semantic context, or analytics that span planning, operational, and external data.
The key takeaway: the modernization conversation should help customers choose the right Oracle path for the workload, while keeping Essbase, FreeForm, and Autonomous AI Lakehouse aligned around business semantics and trusted analytics.
Closing Thought
KScope26 showed strong interest in making Essbase more connected, more AI-assisted, and more aligned with modern data architecture. The opportunity is to keep what users value about Essbase while making it easier to participate in AI Lakehouse, EPM, and enterprise analytics workflows.