Anyone who wants to make future-proof decisions today must understand the technical, strategic, and commercial layers of their system landscape in detail. How this clarity can be achieved in times of artificial intelligence, complex data landscapes and constant transformation was discussed by experts and practitioners at the DSAG Technology Days 2026 in Hamburg.
This year's theme "Lights on Layers. Clarity-by-Design?" underscores the necessity of systematically examining complex SAP topics at all levels. The event focuses on topic areas such as modernization as well as AI and data strategies. We were on site and share the most interesting insights with you in this article.
While the opening keynote by DSAG Technology Board Member Stefan Nogly offered little in terms of new insights, SAP CTO Dr. Philipp Herzig set the strategic direction with his message "All in on AI." Herzig emphasized that modernization and value creation must go hand in hand. SAP itself would actively pursue this path.
An exciting aspect was the honest insight that although SAP is still operating on a hybrid basis internally, it is already using AI intensively. Around 35,000 employees are actively working with Joule. In terms of overall direction, however, the presentation held few surprises. Anyone who has been following SAP's strategy over the past few months was already familiar with the message.
The subsequent panel discussion with Jessica Wolf, Marian Zeis and Hakan Keles was far more interesting. It addressed central topics such as AI, data and security, but above all, the current concerns of the community. It was the immediate practical relevance that made the panel one of the highlights of the first day. You could tell that real issues from business practice were being addressed and that the added value of the DSAG community was also evident.
For instance, Hakan Keles criticized the interoperability of the SAP Business Data Cloud (BDC). He noted that Delta Sharing is currently packaged in a proprietary way and is only truly usable via selected partner solutions. Furthermore, a convincing solution for on-premises scenarios is lacking. At the same time, he credited SAP Datasphere with good product maturity for many productive use cases. He predicted that AI agents on data platforms will be commonplace within one to two years. However, he also noted that the contractual arrangements around BDC can quickly become complex. A problem that many of us are familiar with.
The presentation by Thorsten Ammon and Jagen Handers on the SAP Business Data Cloud (BDC) remained at a very strategic level. The well-known cornerstones were re-examined once again. SAP positions the BDC as a comprehensive commercial product, leveraging partnerships with Databricks, Google, Microsoft and Snowflake to build an extensive data network that also incorporates external sources. The possibility of migrating existing BW landscapes into the BDC was also addressed.
However, anyone who had hoped for concrete news or fresh roadmap details was largely disappointed. In terms of content, the session remained a summary of what was already known.
There was not much new content on the topic of ‘Seamless Planning’ either. The limitation regarding the reuse of Datasphere dimensions in SAP Analytics Cloud planning still persists. A remedy was announced for the fourth quarter of 2026. In the meantime, dimensions must be manually replicated in SAC. What is new, however, is the ability to trigger Datasphere task chains from within SAC. This opens up new possibilities in modeling and data acquisition. For example, HR planning data can be leveraged as a basis for financial planning.
However, the preview of the Data Product Studio was a highlight. It left a promising impression and could fundamentally change the way companies provide and consume data. The development marks a paradigm shift in data provisioning. Instead of distributing data products centrally via a "push" mechanism, the focus is shifting to a “pull” model, in which end users take the initiative.
In the future, the business users should be able to independently find and use data products such as reports in the marketplace leveraging natural language. The concept is convincing, but patience is required. Initial previews of the Data Product Studio were already shown over half a year ago. It remains to be hoped that SAP will not keep us waiting much longer.
A real highlight was the session on planning and analytics with artificial intelligence in SAP Analytics Cloud (SAC). The concluding demo on "Conversational Planning" impressively demonstrated where the journey is heading. Instead of manually building data actions and planning logics, users simply define what they want to achieve. The AI assistant Joule takes over the implementation and independently creates the corresponding data actions, builds the script logics and generates reports as well as dashboards.
As promising as the demo appeared, some questions remained unanswered. First and foremost, pricing. In the current AI Units model of SAP Analytics Cloud, individual AI functions such as summarizing comments or generating advanced formula calculations are billed per request. How this is supposed to work with Conversational Planning, which potentially involves far more complex operations, is currently unclear. The only announcement was that initial functions will be available starting Q3 2026.
In our opinion, for AI-assisted features like the Story Generation Agent to gain traction, two things are needed. First, a community-driven prompt culture in which best practices are shared and pioneers can establish themselves. Second, early cost transparency on SAP's part. Without a clear, accessible pricing model, no CIO will allow their team to work exploratively with these features. SAP must lower the barrier to entry here, otherwise there is a risk of reluctance due to uncertainty about costs and benefits.
The presentation by Marian Zeis impressively demonstrated the extent to which AI agents are already capable of taking on the role of the developer in the SAP environment today. And that without SAP Joule and AI Units. Instead, open-source approaches are used, if necessary, also with local models, so that no data has to leave one's own ecosystem.
The demonstration showcased AI-powered development using MCP servers and large language models (LLMs), integrated into popular IDEs and AI tools such as Cursor, VS Code and Claude Code. In the live demo, applications were generated end-to-end and integrated directly into the SAP system. For ABAP logic, the same interface is accessed as Eclipse ADT itself uses.
The presentation made it very tangible that the role of the developer is already shifting. Away from classical, manual implementation, toward new core competencies: steering, reviewing, and deeply understanding the logic generated by AI.
Marian Zeis maintains a curated overview of available MCP servers for the SAP ecosystem on GitHub. This covers not only Fiori and ABAP but also Datasphere, Business Data Cloud and SAC, making the topic immediately relevant for the Data & Analytics domain as well.
We were also represented with our own session at the Technology Days. In our presentation "Maintenance of Hierarchies and Data Access Controls in SAP Datasphere – Self-Service Data Maintenance in Datasphere, Databricks and More with NextTables," we demonstrated how data management can be revolutionized through a genuine "business-first" approach. In a live demo, we showed typical use cases such as maintenance of hierarchies and data access controls. Participants were able to see live how changes immediately affect reporting.
With NextTables, users can easily create tables and maintain data, while technical validations are applied automatically. Additional features include object-based authorizations, comprehensive user and role management, as well as convenient search and display functions for master data.
Since the solution is seamlessly integrated into the existing system landscape and data is edited directly at the source, the single-source-of-truth principle is maintained at all times, providing seamless data governance as well as high level of data security. No data silos or shadow IT are created. As a result, the dependency on IT is massively reduced and error-prone manual Excel solutions are a thing of the past. At the same time, the processes of the business departments are significantly accelerated.
The DSAG Technology Days 2026 painted a nuanced picture. SAP's strategic direction is clear: "All in on AI" and the Business Data Cloud as the central solution for planning and analytics. However, in some areas there is still a noticeable gap between vision and implementation.
Whilst topics such as BDC and Seamless Planning offered little new insights, it was the practical sessions that proved particularly compelling. First and foremost, Conversational Planning in SAC and AI-supported development with MCP servers. They made tangible how profoundly AI is changing the way we work with SAP systems already today.
At the same time, it became clear that crucial prerequisites for broad adoption are still missing: cost transparency for AI features, genuine interoperability of data platforms and the courage to think beyond SAP's own ecosystem. The community contributions on the panel and in the sessions once again demonstrated that the honest exchange between practitioners and experts is the real added value of such events.
If you are currently evaluating your SAP, data, or AI strategy and want to turn these insights into tangible business value, we would be happy to support you. Whether it’s identifying the right use cases, navigating SAP’s evolving AI landscape, or building a future-proof data architecture – let’s start the conversation. Reach out to us to explore how you can move from vision to implementation with confidence.