BMC Helix focuses on providing an AI-powered platform designed to support enterprise IT service and operations management. The company addresses the challenge of automating and streamlining IT processes by deploying AI agents that assist in anticipating operational needs and delivering automated solutions. This approach aims to enhance productivity within IT teams by optimizing workflows and supporting efficient ServiceOps management without manual intervention.
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I like that the core modules, such as Incident, Change, Problem and Work Order, are stable and work well out of the box. The ticket console in particular is a standout. The filters are easy to manage and offer plenty of flexibility, which makes it much easier for teams to find and organise their work quickly.
Well the containerization is nice... that adds a lot of redundancy and reliability to the application. I have a love hate relationship with the ability to customize the app... its overall a benefit though and likely a key to the success of the product overall. But I'm sure people get themselves into trouble, as we have, going too far with it.
Some of the standout features would be 1) Modern SaaS platform and user experience, 2) Strong workflow structure and usability features and 3) integration and deployment.
What I dislike most is that customization relies heavily on Development Studio, which requires a lot of coding knowledge. This makes even small changes harder to manage and not very accessible for admins who aren't technical. On top of that, the BMC documentation isn't very straightforward. There is plenty of information, but it's not easy to find what you need, especially for customization work.
The upgrade process is in a state. Mistakes and issues in documentation makes us sad.
From my team's experience with BMC Helix, we had issues with the handling of Protected-B documents, access control limitations (like granting group access to specific sections of a ticket; sensitive fields for HR), dashboards are limited and sometimes slow (not as flexible as expected and performance is slow when dealing with large volumes of data and when filtering datasets).