IBM Maximo Asset management platform is going through a radical change in terms of technical as well as functional architecture. Due to a technological platform change, the upgrade timeline for application has changed significantly. While doing the upgrade, we are also trying to ensure that our legacy codes built over last 15-20 years are also removed or retrofitted so that operational expenditure to manage the app reduces.
In order to address these challenges, we are building technical accelerator on WatsonX platform that contextualizes the code conversion from Java to Python in IBM Maximo specific libraries, reviews the code and recommends the improvements, reviews the code and identifies if the similar set of functionalities is delivered by Maximo in the latest version. In our recent POC, we found out that the model requires a little training and it started giving outputs with 80-82% accuracy. We only spent 10-12 hours in training and we are absolutely happy with the accuracy. The target audience (developers) can significantly benefit from this tool as it is going to reduce at least 40% of cycle time in reducing the system complexities and address any issues that comes up due to legacy code in older version of Maximo.
Pros
Code Conversion
Code Review
Code Generation
L1 queries on the product features
Cons
Should allow flexibility to handle unstructured data in a more robust data labeling and annotation tools
real-time collaboration features are minimal which causes development less efficient.
There is no clarity around automated model monitoring and retraining features
Lack of pre-built industry specific models requires more customization effort for certain use cases
Likelihood to Recommend
I have built a code accelerator tool for one of the IBM product implementation. Although there was a heavy lifting at the start to train the model on specifics of the packaged solution library and ways of working; the efficacy of the model is astounding. Having said that, watsonx.ai is very well suited for customer service automation, healthcare data analytics, financial fraud detection, and sentiment analysis kind of projects. The Watsonx.ai look and feel is little confusing but I understand over a period of time , it will improve dramatically as well. I do feel that Watsonx.ai has certain limitations from cross-platform deployment flexibility. If an organization is deeply invested in a multi-cloud environment, Watson's integration on other cloud platforms may not be seamless comported to other AI platforms.
VU
Verified User
Program Manager in Information Technology (Oil & Energy company, 10,001+ employees)
