At SAP Sapphire, LeanIX showcased their vision for enterprise architecture in the AI-native era.
As organizations accelerate into the AI-native era, enterprise architecture (EA) has never been more strategically relevant. Enterprise architects are being asked to manage and govern AI at scale, prove IT value in business terms, and maintain architectural clarity across increasingly complex landscapes, all at the same time.
The capabilities SAP LeanIX released at SAP Sapphire and in recent months are a direct response to that pressure and built around three practical areas where enterprise architects require the most support: collecting and maintaining architecture data while keeping it accurate, current and AI-ready, connecting architecture decisions to business outcomes, and taking ownership of -- and proactively operationalizing -- AI governance.
To be one of the first to get hands-on with these powerful tools, book a demo above, or read on to explore the new AI capabilities in SAP LeanIX that we announced at Sapphire.
Intelligent data by default
The quality of every architecture decision depends on the quality of the data behind it.
For most EA teams, keeping that data current is a persistent drain on time and attention. New AI agents available through the Enterprise Architecture Assistant address this directly. They support you by reading internal documents, PDFs, and sources such as Confluence, and then update application fact sheets automatically, without requiring manual transcription. These agents also pull live vendor and technology information from the web directly into SAP LeanIX, so architects have up-to-date market context without switching tools.
Getting accurate data from application owners has always been a challenge, and low survey completion rates are a direct symptom of that. The reimagined survey experience changes this in two ways. First, designing a survey now starts with a simple prompt describing what you want to learn; SAP LeanIX generates the full survey structure from that input, compressing what previously took hours into minutes.
Second, when respondents open the survey, they are not staring at blank fields. They see contextual suggestions drawn from existing data, which they can confirm, adjust, or override. The result is higher completion rates and better data quality with significantly less effort on both sides.
AI-assisted automations
Building automations and calculations in SAP LeanIX previously required JavaScript knowledge and deep familiarity with the product. AI-assisted automations remove that barrier. Users can now describe a business rule in plain language and SAP LeanIX generates a working configuration using live workspace context. The change is not just about convenience; it opens sophisticated automation to every EA team regardless of technical know-how.
Discovery of data products
Discovery of data products makes them a first-class object in SAP LeanIX. Dedicated discovery connects data products to the applications and agents that produce and consume them, making it possible to govern data dependencies at the portfolio level rather than tracking them separately. This matters particularly for AI governance, where knowing which data products your agents rely on, and whether those products are active and compliant, is a prerequisite for responsible deployment.
AI-assisted architecture guidance
We have also extended AI-assisted architecture guidance. Rather than surfacing observations from the inventory, SAP LeanIX now provides actionable, AI-assisted guidance that recommends specific decisions and next steps based on the current state of the landscape. Decisions are further supported by anonymized industry benchmark data that shows what comparable organizations use in similar contexts, making it easier to justify recommendations to business stakeholders and governance forums.
Evolved semantic search
By evolving the semantic search capability in LeanIX, we make all data accessible through natural language. This means, LeanIX understands the meaning behind a question and surfaces the right fact sheets across the full inventory without requiring knowledge of filters or data model structure. Semantic search is available through both the SAP LeanIX MCP server and the product UI, enabling both our customers and our team to build more powerful AI agents.
SAP LeanIX MCP Server
Our MCP server already has more than 200 customers querying their architecture landscapes conversationally, making the SAP LeanIX inventory a live context layer for any AI workflow rather than a siloed repository. This makes your EA data accessible to any business user within the organization, using rich architecture insights to answer typical questions that land on the enterprise architect’s desk.
Image: SAP LeanIX MCP Server
MCP Apps
LeanIX provides packaged interaction experiences for the most common EA and governance workflows with MCP apps. These apps give teams a structured way to query their landscape, review governance records, and surface architecture insights through any MCP-compatible assistant, whether that is Claude, Microsoft Copilot, SAP Joule, or whatever your organization has standardized on.
For architects who want to go even further, an open-source repository of pre-built agents and skills is available to accelerate that process. Rather than building AI agent integrations from scratch, they can use these as a working foundation and adapt them to their own workflows. The repository is built on top of the LeanIX MCP server, meaning the same architecture data that powers the organization is immediately accessible to any agent or skill built from it.
Accurate, accessible architecture data is the foundation. But data that does not change decisions is just overhead, and that brings us to the second area where SAP LeanIX has invested heavily.
Video: MCP Apps example using Claude
Focus on outcome-led architecture
An enterprise architecture only creates value if it influences decisions. Too often it does not because the outputs live in specialist tools that business stakeholders never open. Our recent releases in this area are focused on closing that gap.
Customized KPIs and vibe-coded reports
Customized KPIs enable EA teams to define the specific metrics that reflect their strategic priorities, whether that is AI readiness, cloud coverage, application redundancy, or ERP transformation status. Those metrics are surfaced proactively rather than buried in a report someone has to remember to run. The result is that leadership always has a current view of architecture health in the terms that are relevant to them, without the EA team having to prepare and distribute it manually.
For situations where a standard dashboard does not fit the reporting need, vibe-coded reports (as seen above) let architects describe what they want to see in plain language through the AI assistant of their choice. LeanIX generates the working configuration from that description. Any architect on the team can produce a custom portfolio view without needing deep product knowledge or waiting for a specialist.
Image: Example of a vibe-coded report
Target architecture planning
We have rebuilt our target architecture planning capability with a visual-first approach. Rather than starting from fact sheets and modelling forward, architects can now start from a target state diagram and work backwards into planning and execution. The flow moves from design to the comparison of options to new transformation initiatives within a single coherent journey, shrinking the gap between architectural thinking and delivery planning considerably.
Video: Target architecture planning
Making architecture decisions visible to the business matters a lot, and it becomes even more urgent when those decisions involve AI. Which brings us to the area that has seen more significant updates: AI governance.
EA as the AI governance anchor
Most organizations are experimenting with and deploying AI faster than they can govern it. Many have no systematic overview of which agents are running, what systems they touch, or who is accountable for them. This is the governance gap that SAP AI Agent Hub is designed to close, and LeanIX provides the architectural context for the AI Agent Hub that makes that governance meaningful at enterprise
SAP AI Agent Hub marchitecture
More than 180 organizations have already discovered over 180,000 agents in production, proving that the governance challenge is real and not a future problem. The scope of the AI Agent Hub covers not just SAP environments but also agents, MCP server and LLMs from any vendor or custom framework, giving EA teams a single view across the full multi-vendor AI estate and equipping them to manager and govern them across the lifecycle.
Why these updates matter now
Enterprise architecture is not a spectator in the AI-native era. The organizations that treat it as one will feel the consequences in ungoverned AI sprawl, architecture decisions made on stale, inaccessible data, and AI investments that cannot demonstrate return. The capabilities LeanIX has shipped address each of those problems directly and practically. There is more to come, but the foundation is in place, and it is available now.
LeanIX has been named a Leader in the Gartner Magic Quadrant for Enterprise Architecture Tools for five consecutive years, with the most recent widening the gap between LeanIX and other Leaders. That reflects something more than product investment; it reflects a sustained orientation toward understanding where enterprise architecture needs to go next. The most recent releases are the clearest expression yet of what that looks like in practice for our customers, and we look forward to navigating the AI-native era with you all.
To find out more about how these newly AI-empowered capabilities can support your enterprise architecture practice, book a demo:
