Introduction to Composable AI: Building Blocks for Business Success

Benefits of a modular AI approach, including adaptability, scalability, and faster time-to-value.

Introduction:

Industries such as Oil & Gas, Petrochemicals, Chemicals, and Life Sciences are adopting digital solutions for predictive maintenance, emissions management, and process optimization. However, fragmented data systems and analytics across departments lead to inefficiencies, rising technical debt, and inconsistent insights. The Composable AI Smart Framework addresses these challenges by integrating Machine Learning (ML), Generative AI (Gen AI), and Knowledge Graphs to unify data, analytics, and workflows into a single, adaptive platform. This framework enables organizations to unlock the full value of their data and drive better decision-making.

Agenda: 

• Shortcomings of existing AI deployments.
• What is composable AI- new approach.
• How to identify reusable AI components and customize workflows for industry-specific applications.
• Discuss how to scale AI operations as business needs evolve, including managing data flow, processing power, and deployment.
• Composable AI case studies (AI-based emissions, process optimization and maintenance workflows).
  
  

Key Business Problems: 

Are your AI deployments truly unlocking the full potential of your data, or are they adding to the growing complexity and technical debt? Industries today are grappling with multiple challenges in their digital transformation efforts:

• Data Silos: Isolated datasets limit comprehensive insights and collaboration.
• Fragmented Analytics: Independent systems create redundancy and inefficiencies.
• Technical Debt: Siloed implementations increase costs and operational complexity.
• Inconsistent Decision-Making: Lack of centralized data impacts real-time, data-driven decisions.
• Regulatory Compliance: Siloed emissions monitoring hinders proactive environmental management.