Compositional AI: The Missing Tie in Industrial Data Management

06.17.2024 | Chris Griffiths

The Power of Compositional AI

In the rapidly evolving landscape of industrial data management, generative AI has made waves, but it’s just the beginning. At SymphonyAI, we’re pushing boundaries with compositional AI—a powerful blend of generative AI, predictive models, and MLOps that unlocks new efficiencies and insights.

Why Compositional AI?

Compositional AI integrates generative AI, predictive analytics, and machine learning operations to build flexible, scalable, and precise systems. To break this down, we can think of generative AI as an English major, excellent for summarizing information or finding specific content in 100+ page OEM documents, but not an accurate resource for computations.

Predictive AI is a math major that can identify undetectable anomalies in equipment without time-consuming human analysis but provides complex results that are only interpretable by subject matter experts.

Computer vision technologies are powerful for identifying assets and sensors on a piping and instrumentation (P&ID) diagram or identifying quality issues on a production line. However, unless this information is tied back into an industrial knowledge graph, it lacks the overall context to relay the impact on industrial operations. Compositional AI allows these specialized AI technologies to work together and are ideal for managing complex industrial data.

The SymphonyAI Mission for Industrial Operations

Applying the right type of AI to industrial data management processes unlocks operational use cases to scale site-level excellence across the enterprise. Generative AI creates data and content, but when combined with knowledge graphs, predictive models, and MLOps, it transforms industrial data management. With compositional AI, generative AI copilots can interact with predictive AI models, providing unparalleled insights to democratize information for all industrial knowledge workers to take data-driven actions.

Compositional AI for industrial data management

1. Data Integration and Interoperability
  • Compositional AI improves data quality and consistency by combining generative AI with knowledge graphs. The combination enhances data management and interoperability, as highlighted by research firm Verdantix in a recent article, “Generative AI With Knowledge Graphs: A Giant Leap for Industrial Data Management.”
  • This integrated approach allows for seamless data aggregation from diverse sources, ensuring a comprehensive and accurate dataset. Unifying industrial data with automation is crucial for industries that rely on high-quality data for decision-making and operational efficiency.
2. Better Decision-Making
  • Knowledge graphs enhance data context, leading to more accurate predictions and improved decision-making. They provide a structured way to represent and analyze relationships between diverse data sets, facilitating deeper insights.
  • This enhanced contextual understanding allows businesses to anticipate trends, identify potential issues before they escalate, and make informed decisions that drive better outcomes. As industries become more data-driven, the ability to leverage comprehensive, context-rich data will be a crucial differentiator.
3. Scalability and Flexibility
  • Compositional AI adapts quickly to changing industrial needs, ensuring our solutions are agile and responsive. This flexibility is underscored in discussions on tailoring generative AI and knowledge graphs to various applications.
  • Whether scaling operations to meet increased demand or adapting to new regulatory requirements, Compositional AI provides the necessary tools to remain competitive and efficient. This adaptability is essential in dynamic industrial environments where agility can be a significant advantage.
4. Cost Efficiency
  • This approach reduces manual data processing, lowering costs and speeding up project implementation. Verdantix noted in its article that advanced AI technologies can significantly reduce operational burdens and costs.
  • By automating routine data management tasks, compositional AI frees up valuable resources that can be redirected toward more strategic initiatives. This reduces operational expenses and accelerates the time-to-value for AI-driven projects, enhancing overall return on investment.

Real-World Impact

SymphonyAI compositional AI is already making a significant difference across sectors, from predictive maintenance to supply chain optimization. For example, A midstream customer leverages SymphonyAI’s generative AI copilot, providing operators with an intuitive interface to diagnose system issues. An operator interacts with predictive AI using natural language through the AI copilot, describing the problem and providing relevant data. The predictive AI then uses historical data, along with audio and visual inputs, to analyze and compare against other systems and similar equipment. By referencing past good behavior, the predictive AI identifies the root cause of potential problems and suggests remediation. At the operator’s request, the system generates a work order, complete with an AI-generated modeled prediction of severity to help prioritize the problem, ensuring prompt and efficient resolution.

Companies adopting compositional AI have seen up to a 30% reduction in maintenance costs and a 20% increase in efficiency, aligning with Verdantix insights on the need for enhanced data management.

  • Predictive maintenance: By accurately forecasting equipment failures, companies can perform maintenance proactively, reducing downtime and extending the lifespan of assets.
  • Supply chain optimization: Improved data integration and predictive analytics enable more efficient inventory management, demand forecasting, and logistics planning, leading to cost savings and enhanced service levels.
  • Advanced analytics: Compositional AI enhances the depth and breadth of data analytics, enabling companies to uncover hidden patterns and insights. This leads to more informed strategic decisions, better risk management, and the ability to capitalize on new opportunities faster.

The Future of Data Management

As industries evolve, the demand for advanced data solutions will grow. SymphonyAI is leading this transformation by offering modular, scalable, and context-rich solutions with a powerful industrial DataOps platform, IRIS Foundry.

Compositional AI represents a significant leap forward in industrial data management, driving efficiency, innovation, and new possibilities. As we move towards a more data-driven future, SymphonyAI is ready to lead the way.

Compositional AI is not just managing data—it’s transforming data into a strategic asset that drives business success. By using the combined power of generative AI, predictive models, and MLOps, SymphonyAI Industrial is paving the way for a more innovative, efficient industrial future.

Learn more about how the combination of predictive and generative AI delivers business impact.

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