t cht lk what the custom code does . The document is then reviewed by process owners to confirm that the AI has accurately interpreted the purpose of the custom code . So long as it has , the AI solution goes on to generate microservice code to run on SAP BTP , along with test scripts that can be executed against the new code to ensure smooth upgrades .
t cht lk what the custom code does . The document is then reviewed by process owners to confirm that the AI has accurately interpreted the purpose of the custom code . So long as it has , the AI solution goes on to generate microservice code to run on SAP BTP , along with test scripts that can be executed against the new code to ensure smooth upgrades .
The result is the ability to shrink migration projects that would have required thousands of days of tedious legacy app refactoring work into initiatives that organizations can complete in a fraction of that time . And , because our AI solution produces test scripts as well as the microservice code , it ensures that teams can efficiently test their extensions against future platform upgrades , too .
But again , building a solution like this requires much more than simply asking generic AI tools to refactor code . and rewrite code in contexts where generic AI software development tools , which are not tailored for legacy app modernization , would likely fall short .
We also designed our process to keep a ‘ human in the loop ’ by requiring manual review of the specification document before AI generates microservices code , as I mentioned . This is important for identifying and addressing situations where our AI tools misinterpret legacy code – which is rare , but which happens , and which could lead to major problems down the line if the resulting microservices failed to work as required .
Going forward , the ability to leverage AI to solve tough challenges like legacy app modernization is poised to become a key differentiator for businesses . Those that can successfully apply AI to such use cases will be in a much stronger position than their competitors to operate efficiently .
To ensure that the microservices code produced by our tools actually works as intended , we ' re developing an AI module for our Lemongrass Cloud Platform ( LCP ) tool , which we use in conjunction with third-party foundation models to modernize legacy apps . The custom AI module is a critical part of the equation because we fine-tuned it to adhere to key development and testing standards surrounding SAP applications . As a result , our solution can interpret
That said , AI-driven innovation requires careful planning and customization to produce reliable , high-quality output . It would be nice if AI innovation were as simple as feeding input into publicly available generative AI services . But my experience shows that some extra steps are required , at least when addressing complex use cases , which require investment in custom AI technology , as well as checks to validate the correctness of AI output . p
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