Introduction
It is no longer acceptable to freeze the cycle of innovation every time SAP needs to be upgraded.
Teams have historically delayed major SAP upgrades for five to seven years or more, accumulating a massive debt of customizations, many of which have been abandoned. When upgrade projects finally launch, standard readiness reports present hundreds of unprioritized findings. In today’s market where agility is paramount, this approach is no longer sustainable.
The challenge is that teams cannot easily distinguish between vital custom business logic and code that has not been executed in years. This lack of visibility forces teams to manually investigate a wall of technical issues, wasting scarce resources on low-value fixes rather than addressing the actual technical hurdles necessary for a successful go-live. This leads to long upgrade cycles requiring “double maintenance” to keep the upgrade landscape synchronized with daily production changes.
To solve this, leaders are adopting risk-based analysis with tools like Tricentis LiveCompare. This technology identifies exactly which customizations are technically impacted by the upgrade and cross-references those findings with actual production usage. By highlighting the objects that are both affected by the change and actively used, the system filters out the noise. This allows development teams to deprioritize non-impacting issues and focus entirely on the high-risk elements that threaten stability. And by automating retrofits of daily maintenance changes to the project landscape, parallel development can proceed without delays.
It is no longer acceptable to freeze the cycle of innovation every time SAP needs to be upgraded.
Converting Risk Intelligence into Targeted Validation
Testing everything is a symptom of uncertainty, not quality. True velocity requires the confidence to test only what matters.
Identifying high-risk objects is only valuable if it drives action. The real operational shift occurs when teams dismantle the traditional, bloated regression test strategy. Instead of testing vast swaths of the system to ensure safety, teams must adopt a precise approach that accelerates testing timelines.
Intelligent analysis maps high-risk objects directly to the test repository to generate a target scope for testing. This process identifies the minimum number of test cases required to cover the maximum amount of risk. It prevents teams from executing tests on unchanged areas while simultaneously flagging critical gaps where no test cases exist.
This approach fundamentally alters how changes are cleared for deployment. Release decisions move from subjective signoffs to empirical evidence of coverage. A high-risk change simply cannot proceed until its specific linked test case is executed, ensuring validation is tied directly to technical impact.
Testing everything is a symptom of uncertainty, not quality. True velocity requires the confidence to test only what matters.
Streamlining the Logistics of Parallel Landscapes
An upgrade landscape that drifts from Production creates delays during cutover. Continuous synchronization of maintenance changes paves the way to a smooth go-live.
For complex upgrades, many organizations spin up a temporary “N+1” landscape to isolate long-term project work from daily support. While this separation protects production stability, it requires a disciplined approach to prevent the two environments from drifting apart.
- Synchronize continuously. Teams cannot wait until the end of the project to merge changes. Every maintenance fix applied to production must be immediately “retrofitted” into the upgrade landscape. This prevents a massive double-maintenance backlog and ensures the upgrade environment always reflects the latest Production updates.
- Automate via the toolchain. Relying on manual spreadsheets to track these changes invites error. CoreALM transport management tools embed retrofit logic directly into ServiceNow, Azure DevOps, or Jira. When a maintenance change is deployed, the system automatically deploys the same fix to the project, ensuring zero drift with minimal manual overhead.
An upgrade landscape that drifts from Production creates delays during cutover. Continuous synchronization of maintenance changes paves the way to a smooth go-live.
Conclusion
We cannot accelerate SAP upgrades by simply working harder. We must fundamentally change the mechanics of how we measure risk and manage change.
The old-school approach to SAP upgrades does not support the speed required by modern digital businesses. Organizations that continue to rely on manual impact analysis and full regression test cycles will fail to keep pace. To shift to a continuous delivery model, leaders must implement these three changes immediately:
- Pivot to risk-based testing. Implement intelligent change impact analysis to identify the intersection of technical risk and actual system usage, reducing the remediation and testing effort by focusing only on what matters.
- Enforce evidence-based release decisions. Move beyond subjective approvals. Link high-risk objects directly to specific test cases. Ensure that no change moves to Production without empirical data proving that the specific risk has been validated.
- Integrate retrofit automation. If utilizing parallel landscapes, eliminate the manual tracking of dual-maintenance changes. Use transport management controls to automatically trigger retrofits in the upgrade path whenever a production fix is deployed.
By adopting these practices, organizations do more than just survive their next upgrade. They establish a repeatable, data-driven operating model that turns SAP maintenance from a disruption into a continuous improvement process.
