Introduction to Composable AI: Building Blocks for Business Success

𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:

Industries like Oil & Gas, Petrochemicals, Chemicals, and Life Sciences face challenges like data silos, fragmented analytics, rising technical debt, and inconsistent insights, hindering predictive maintenance, emissions management, and process optimisation.

The Composable AI Smart Framework integrates Machine Learning (ML), Generative AI, and Knowledge Graphs to unify data, analytics, and workflows into an adaptive platform, driving better decision-making and unlocking the full value of organizational data.

𝐊𝐞𝐲 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬:

• Data Silos: Breaking down barriers for better collaboration and insights.
• Fragmented Analytics: Eliminating redundancies and inefficiencies.
• Technical Debt: Simplifying operations and reducing costs.
• Inconsistent Decision-Making: Enabling real-time, data-driven insights.
• Regulatory Compliance: Enhancing proactive environmental management.

𝐀𝐠𝐞𝐧𝐝𝐚:

• Identifying shortcomings in current AI deployments.
• Understanding the Composable AI approach.
• Building reusable AI components and industry-specific workflows.
• Scaling AI operations for evolving business needs.
• Real-world case studies on emissions management, process optimization, and predictive maintenance.