AI-Powered B2BI Tuning Recommender

IBM Sterling B2BI provides a wide range of configuration options to maximize performance. As file volumes increase due to business growth, manually identifying and applying the right tuning adjustments becomes complex and time-consuming. 

Currently, tools like QueueWatcher, system logs, etc. are highly valuable for monitoring performance and identifying issues. Building on these capabilities, an AI-based tuning recommender would automatically identify the components under stress and suggest the appropriate tuning parameters. This would help ensure consistent performance even during periods of high load. All customers would benefit, as the system would continue to operate reliably under peak demand.

The business impact would be substantial. Customers would be able to focus more on their core operations, confident in system performance even as transaction volumes grow. This enhancement would also reduce performance-related support tickets, improve their end-user satisfaction, and lower support costs. It further strengthens long-term adoption of the platform by increasing its resilience and scalability.

AI is a key driver of automation across industries. Enabling an AI agent to analyze system behavior and recommend optimal tuning parameters would significantly reduce operational overhead for customers and IBM Sterling B2BI support teams. This enhancement complements existing tools such as QueueWatcher, logs, etc., builds on their value, and represents a high-priority improvement to ensure reliable performance under growing workloads.