Embedded AI/ML System for Automated Business Mapping Suggestions in Apptio Cloudability

The proposed feature introduces an embedded Artificial Intelligence and Machine Learning (AI/ML) engine within Apptio Cloudability that continuously learns from customer-specific resource tags and usage patterns. Based on this learning, the system will automatically generate intelligent suggestions for Business Mappings such as Owner, Team, Department, Environment, and other relevant dimensions.

Key Capabilities

- Tag Learning Engine: Continuously scans and learns from the customer’s existing resource tags across cloud providers.

- Automated Suggestions: Proactively suggests Business Mappings based on observed tag patterns and historical mappings.

- Adaptive Intelligence: Improves accuracy over time through feedback and reinforcement learning from user adjustments and confirmations.

Benefits:

- Accelerated Onboarding: Drastically reduces the time required for initial Business Mapping setup by auto-suggesting common and organization-specific rules.

- Operational Efficiency: Frees up FinOps teams from repetitive configuration tasks, allowing them to focus on optimization and strategic analysis.

- Scalability: Makes it easy for large enterprises with thousands of tags and accounts to adopt and maintain Business Mappings effectively.

Example Use Case:

A customer has a mix of tags like Team:Platform, Owner:j.smith, and Environment:Production across hundreds of cloud resources. 

The AI/ML system identifies these patterns and suggests:

- A mapping rule for Team = Platform based on tag Team

- A mapping rule for Owner = John Smith based on tag Owner

- A mapping rule for Environment = Production