Many companies are currently migrating their legacy ERP systems to a modern, cloud-based environment, undergoing a profound transformation on multiple levels in the process. With Rise, they have access to a structured approach consisting of a standardized framework, an integrated toolchain, and guidance from experts\u2014an approach that simplifies and accelerates project workflows in line with the Clean Core principle.<\/p>\n\n\n\n
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Working Together to Tackle Pain Points<\/h2>\n\n\n\n
Specifically, the toolchain is designed to enable seamless collaboration between business departments and IT. It sets clear priorities and facilitates decision-making. And it is intended to help coordinate changes in the ERP landscape in a holistic manner. Specific AI-powered tools have been developed for each area\u2014people, processes, applications, and data\u2014and for each of the six project phases in Activate, from the initial assessment to ongoing operations.<\/p>\n\n\n\n
These include WalkMe for enablement, Signavio for process analysis and design, LeanIX for mapping the IT landscape, and Cloud ALM for managing and optimizing applications. They are complemented by partner tools. All of these can be flexibly deployed and integrated as needed\u2014right where the biggest pain points lie within a company. An important component of the integrated toolchain is SAP Cloud ALM, which acts as the glue that holds everything together. It should be viewed not so much as just another specialized tool, but rather as a controlling and structuring unit for planning, implementation, and operations.<\/p>\n\n\n\n
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Orchestration with Cloud ALM<\/h2>\n\n\n\n
To implement Cloud ALM, companies must establish the right prerequisites: clearly define functional and technical responsibilities, designate one owner per area (implementation, operations, monitoring), clearly assign all project roles (PM, IT, business unit, partners) and clarify their tasks, properly configure access and permissions, train personnel on their roles, and develop associated guides and best practices, as well as define all processes \u201eend-to-end.\u201c In addition, Cloud ALM should be planned for early in the project, linked to fixed milestones for activation and use, and aligned with the overall timeline.<\/p>\n\n\n\n
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Less Effort Thanks to AI<\/h2>\n\n\n\n
Artificial intelligence plays a major role in the toolchain. The technology is integrated into all tools and automates recurring tasks such as data analysis, data mapping, testing, and employee training. In the discovery phase, for example, the \u201eText-to-Process\u201c feature in Signavio enables existing process documentation\u2014whether in text or image form\u2014to be automatically converted into process models.<\/p>\n\n\n\n
This is particularly relevant for companies that have previously documented their processes in Word documents or graphics. In addition, the AI independently suggests key performance indicators\u2014for example, in accounts payable\u2014to specifically measure automation rates and optimize processes.<\/p>\n\n\n\n
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Reduce Costs, Improve Quality<\/h2>\n\n\n\n
During the design and build phase, Cloud ALM enables AI-driven generation and customization of test cases, derived directly from process innovations or changes. For example, when references have been lost, an object has been moved, or it has been renamed during development. In combination with Tricentis and its change impact analysis, the test scope can be automatically reduced based on configuration changes. This also works on a risk-based approach, with a focus on critical processes such as month-end closing.<\/p>\n\n\n\n
Since even small changes can quickly result in thousands of (manual) test cases\u2014especially when these changes occur simultaneously in both the old and new systems\u2014automation is extremely valuable here for reducing effort and costs while improving quality.<\/p>\n\n\n\n
Beyond pure test automation, the toolchain also supports test management throughout the entire project lifecycle. SAP Cloud ALM ensures end-to-end traceability of requirements, test cases, and defects. In collaboration with Tricentis, automated regression tests can be established that\u2014especially in the face of frequent cloud updates\u2014help significantly reduce testing effort while ensuring quality. This is complemented by structured test data management, which simplifies the provision of realistic test data that complies with data protection regulations.<\/p>\n\n\n\n
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Reduce the Number of Support Tickets<\/h2>\n\n\n\n
To provide automated, role-based training and support for employees in real time, there are AI-based apps that use data from Signavio or Cloud ALM to identify learning needs, suggest learning programs, create content, and answer context-specific questions. This intelligent support, provided directly within the workflow, improves data quality, reduces the number of support tickets, speeds up navigation, and makes interactions more personalized.<\/p>\n\n\n\n
In addition, this helps employees become more involved in the transformation and familiarize themselves with the changes.<\/p>\n\n\n\n
The future lies in AI agents\u2014that is, AI-powered agents that operate largely autonomously. However, to coordinate them safely, there are a number of considerations to keep in mind with regard to the EU AI Act. This is because the regulation establishes harmonized rules for AI systems within the European Union\u2014and thus clear guidelines for providers, developers, and users of AI.<\/p>\n\n\n\n
Although it has been in effect since 2025, few companies feel prepared for the regulation. While the majority are working on AI governance, implemented frameworks and dedicated governance for agents are still the exception.<\/p>\n\n\n\n
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Time to Take Action<\/h2>\n\n\n\n
Since August, high-risk systems have been fully regulated, subject to strict quality and risk management requirements, and starting next year, the same will apply to AI systems embedded in regulated products. By then at the latest, companies will need specific governance mechanisms that go beyond traditional machine learning operations and\u2014in addition\u2014full compliance, including GRC, MLOps, and data privacy tools.<\/p>\n\n\n\n
When discussing this topic, it is important to understand what AI agents are capable of\u2014which, in turn, depends on their level of autonomy. Agents can perform individual tasks or provide recommendations, carry out actions under supervision, and\u2014except in rare cases\u2014act independently or fully autonomously.<\/p>\n\n\n\n
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Differences from Traditional AI<\/h2>\n\n\n\n
This also highlights key differences from traditional artificial intelligence: Agents are capable of acting autonomously and emergently, can interact with external systems via APIs, behave dynamically, and make decisions very quickly. A practical example from the order-to-cash process illustrates this: When an order is placed, the order agent first checks whether the data is complete and whether the customer is creditworthy.<\/p>\n\n\n\n
The billing agent then generates the invoice. The payment agent monitors the payment; if it has been received, the agent forwards it to Finance; if not, the agent sends a reminder to the customer. All agents operate autonomously as part of a multi-agent system.<\/p>\n\n\n\n
However, there are still a few hurdles to overcome before agents can provide added value. So far, many agents have been working in an unstructured manner, isolated within individual apps, teams, and projects without clear responsibilities, or they have been hindered by system limitations.<\/p>\n\n\n\n
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AI Readiness Assessment<\/h2>\n\n\n\n
As a result, investments rise sharply, projects are abandoned, and only a few employees actually make use of them.<\/p>\n\n\n\n
To prevent this, the first recommended step is an AI Readiness Assessment to analyze where the company stands, the maturity level of its existing AI landscape, where risks exist, and where there are gaps in governance. In the second step, the framework is implemented into the toolchain using LeanIX, Signavio, and Cloud ALM\u2014during ongoing operations, it should be regularly reviewed, optimized, and updated to comply with regulatory requirements. The AI Agent Hub can serve as the control center for AI agents in the toolchain.<\/p>\n\n\n\n
With its unified interface, it provides an overview\u2014and thus transparency\u2014into how agents operate within processes, how they perform, what risk level they fall into, and what their deployment costs. Even with just a few agents, it\u2019s worth implementing the hub so that, as their numbers grow, they don\u2019t fall by the wayside and become a (regulatory) risk.<\/p>\n\n\n\n
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What can the toolchain do\u2014and what can't it do?<\/h2>\n\n\n\n
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What the Toolchain Does<\/strong><\/p>\n\n\n\n
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Centralized AI Agent Inventory and Discovery<\/li>\n\n\n\n
Risk Classification and Heat Maps<\/li>\n\n\n\n
BPMN Modeling with Human-in-the-Loop<\/li>\n\n\n\n
AI Agent Mining for Transparency<\/li>\n\n\n\n
Lifecycle: From Evaluation to Retirement<\/li>\n\n\n\n
Bidirectional Traceability<\/li>\n\n\n\n
User Guidance and AI Labeling in the UI<\/li>\n\n\n\n
Documentation for Audits and Compliance Verification<\/li>\n<\/ul>\n<\/div><\/div><\/div>\n\n\n\n
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What the toolchain can't do<\/strong><\/p>\n\n\n\n
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Algorithmic Audits and Bias Analysis<\/li>\n\n\n\n
In-Depth Explainability\/XAI Checks<\/li>\n\n\n\n
Formal Fairness Argumentation<\/li>\n\n\n\n
Decisions in Ethical Gray Areas<\/li>\n\n\n\n
PII Scanning and Data Anonymization<\/li>\n\n\n\n
ML-Ops (training, drift correction)<\/li>\n\n\n\n
Full compliance with the EU AI Act as a standalone solution<\/li>\n<\/ul>\n<\/div><\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n
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