(AI strategy. Physics-based data contextualization. Model operationalization)
Picture your digital transformation journey with layers of onion: What steps are necessary to unveil the full potential of your manufacturing processes?
In this learn session, we'll peel back the layers of complexity surrounding AI implementation, providing you with actionable insights and strategies to maximize the value of your investments. No more feeling lost in the black box of AI. Discover how each layer contributes to the overall success of AI implementation in your organization.
- AI Strategy: Much like the outer layer of an onion, the AI strategy serves as the initial framework for implementing AI within an organization's processes. This layer involves defining the overarching goals, objectives, and methodologies for integrating AI into existing workflows. It encompasses aspects such as identifying business needs, setting strategic objectives, allocating resources, and establishing key performance indicators (KPIs) to measure success. 40% of oil & gas companies have AI pilots or proof-of-concept projects This shows early adoption but also highlights a lag in scaling to full organizational strategies. This suggests a gap between interest and comprehensive implementation.
- Physics-based Contextualizing: The contextualizing layer represents the deeper understanding of the underlying principles and mechanics behind AI applications. Just as the middle layer of an onion provides substance and context to the outer layers, contextualizing AI within the framework of physics-based principles involves understanding the mathematical and algorithmic foundations of AI models. This layer may include concepts such as machine learning algorithms, neural networks, optimization techniques, and statistical methods tailored to the specific domain or industry in which AI is being applied.
- Best Practices for Model Operationalization: The innermost layer of the onion represents the practical implementation and operationalization of AI strategies within an organization's processes. This layer involves translating theoretical concepts and strategies into actionable steps and workflows. It encompasses best practices for model deployment and integration, monitoring and maintenance, as well as continuous improvement and adaptation based on feedback and performance metrics.
Example use cases
- Decoking strategy for efficient furnace operations
- Optimal running of plant wide operations (Ethylene plant)
- Predictive maintenance of rotating assets – wet gas and recycle gas compressors
Transform your manufacturing processes and elevate your business to new heights with the power of AI! Join us for an exclusive webinar where we unveil actionable strategies for leveraging AI to optimize your processes and drive success.
𝐖𝐡𝐨 𝐬𝐡𝐨𝐮𝐥𝐝 𝐚𝐭𝐭𝐞𝐧𝐝?
- Manufacturing executives and leaders seeking to leverage AI for operational excellence.
- Process improvement professionals looking to enhance efficiency and productivity.
- Technology enthusiasts interested in understanding the practical applications of AI in manufacturing.
- Advanced process control professionals
