Operational AI: Syncing Data Back Into Business Apps

AI is no longer limited to predictions; it is now actively improving business operations. According to recent research, 87% of global organizations believe that using AI will give them a competitive advantage. In one survey, 94% of business leaders consider AI critical to their success in the next five years. 

To stay ahead, many companies are embedding AI into their routine tasks to improve operational efficiency. This shift has given rise to Operational Artificial Intelligence (AI) — intelligent systems that utilize AI algorithms in day-to-day corporate functions.  

If you’re ready to adopt operational AI, this article will guide you through its merits, underlying technology, functionality, practical use cases, and implementation steps. 

What Is Operational Artificial Intelligence?

Operational Artificial Intelligence refers to AI models that are integrated into an organization’s operations management. This facilitates real-time process automation, smart decision-making, and productivity improvements. 

Unlike machine learning (ML) and generative AI, which focus on predictive analytics and content generation, Operational AI specializes in boosting business processes. By deploying AI insights directly into activities, such as planning, scheduling, quality control, and logistics, Operational AI helps you enhance organizational workflows. 

With Operational AI in place, you can automate daily functions to increase business growth, optimize cost, and enhance customer satisfaction. 

Technology Behind Operational Artificial Intelligence

Operational Artificial Intelligence relies on several advanced technologies to automate processes and improve decision-making. Here are some of them:

• ML enables AI systems to analyze data, recognize patterns, and enhance performance over time through continuous learning.‍
• Natural Language Processing (NLP) helps AI systems to interpret human languages for tasks like customer support.
• ‍Computer vision facilitates image and video analysis, improving quality control and security monitoring.
• ‍Robotic process automation (RPA) streamlines repetitive routine tasks to increase operational excellence.
• ‍Cloud computing platforms provide the storage and computational power required to process large datasets and run AI models efficiently.
• ‍Workflow orchestration allows you to coordinate tasks, manage dependencies, and automate decision-making in various business functions. 

Together, these technologies drive smooth AI workflow automation and smarter business operations. 

Benefits Of Operational Artificial Intelligence

• Improved Customer Experience: AI agentic workflows or chatbots, recommendation systems, and automated support systems can help you enhance customer interactions across your business processes. By delivering personalized and relevant responses, your business can improve customer satisfaction and loyalty. ‍
• Real-time Monitoring and Adaptation: Operational AI allows you to continuously monitor the business processes by identifying inefficiencies and making real-time adjustments. This ensures optimal performance across supply chains, logistics, and production lines.
• ‍Risk Management: Utilizing Operational AI enables your business to follow industry regulations by automating compliance checks and detecting risks. It aids you in ensuring data accuracy and reducing legal or financial penalties.
• ‍Scalability: Operational AI systems let you scale with business growth, handling increasing workloads without affecting performance. As a result, your company can expand operations effectively while maintaining high standards of service.
• ‍Cost Optimization: With Operational AI, you can control costs by automating repetitive tasks, improving resource utilization, and minimizing manual effort. For instance, it helps you enhance supply chain performance through accurate demand forecasting and route enhancement, reducing transportation and inventory costs. 

How Does Operational Artificial Intelligence (AI) Work? 

Operational AI allows machines to function intelligently by automating processes in your business workflows. Unlike traditional computing systems relying on explicit instructions, AI systems learn from data, adapt to new inputs, and perform complex tasks with reduced human intervention. 

These AI systems utilize structures inspired by human neurons to interpret information. When data is fed into the system, artificial neurons activate and pass signals to other neurons through interconnected networks. This architecture enables AI to train on large amounts of data, detect patterns, and make decisions. 

The training phase requires significant computational resources, including high-end graphics cards. By iteratively fine-tuning parameters based on feedback, the ML or deep learning algorithm can improve its task performance. 

Once trained, you can integrate the AI system directly into your live business operations. This supports you in facilitating real-time analysis and decision-making while automating organizational functions. 

Use Cases For Operational Artificial Intelligence

Here are some use cases demonstrating how Operational AI is driving real-world business processes: 

Supply Chain Optimization

AI is critical in optimizing supply chain management by processing vast datasets in real-time and forecasting market shifts. It also enhances logistics by adjusting the routing and scheduling of shipments and deliveries dynamically. 

IBM implemented AI-powered supply chain solutions within its operations. This achieved USD 160 million in cost savings and maintained a 100% order fulfillment rate, even during the peak of the COVID-19 pandemic.  

AIOps

AIOps, or artificial intelligence for IT operations, leverages AI to automate IT services and streamline operational workflows. By continuously analyzing data from IT apps, performance monitoring tools, and infrastructure components, AIOps detect potential issues early. When an issue is identified, AIOps aids you in determining the root causes and alerts IT teams for automated resolution. 

Electrolux, a leading home appliance manufacturer, utilized AIOps to minimize IT issue resolution time from weeks to just an hour. By automating repair tasks, the company saved over 1,000 hours annually. 

How to Implement Operation Artificial Intelligence? 

Adopting Operational Artificial Intelligence (AI) requires a strategic approach to ensure effortless AI integration into your business processes. Here are the detailed steps for modeling Operational AI for your organizational needs: 

Data Collection and Integration

The initial step in implementing Operational Artificial Intelligence workflow is collecting data from various sources and standardizing it in a consistent format for AI analysis. To streamline this process, you can leverage a no-code data movement platform like Airbyte. It offers 550+ pre-built connectors to help you extract data from diverse sources and load it to a preferred destination. Once the data is in the target system, you can make it highly usable by applying custom transformation using Airbyte dbt integration. 

Here are some key features of Airbyte:

• AI-Powered Connector Builder: Apart from the Airbyte-native connectors, you will have the flexibility to build custom ones using its no-code Connector Builder, low-code CDKs, or language-specific CDKs. The no-code Connector Builder comes with an AI assistant feature, which auto-fills the required connector configuration fields.
• ‍Developer-Friendly Pipeline: Airbyte offers an open-source Python library, PyAirbyte. By utilizing Airbyte’s built-in connectors, PyAirbyte lets you extract data from different sources and write it into an SQL cache like Snowflake, BigQuery, or DuckDB. Once the data resides in the internal cache, you can transform it using Python-based libraries like Pandas, SQL-based tools, or AI frameworks such as LangChain or LlamaIndex. After transformations, you can then load the processed data into a data warehouse or data lake.
• ‍Vector Database Support: Besides data warehouse or data lake, Airbyte supports eight different vector databases, including Pinecone, Weaviate, and Milvus. You can load unstructured data into these vector store destinations and transform them using RAG-based techniques like OpenAI-enabled embeddings and LangChain-powered chunkings. Once processed, you can use these data to train AI or ML models.
• ‍Change Data Capture (CDC): Airbyte supports the CDC approach to sync data between different databases. This approach enables you to track the latest modifications in the source system and replicate them to the destination. 

Operational Artificial Intelligence Model Training

Once the data is collected, structured, and stored in a target system, the next step is developing and training AI models that power Operational Artificial Intelligence. This process involves using NLP techniques, ML, or deep learning algorithms to analyze the integrated data. As a result, the model can identify patterns and make predictions relevant to business operations. 

Model Deployment and Automation

After the training process, you can deploy the AI model into your business workflows to automate tasks and assist in decision-making. Whether you are managing supply chain operations, optimizing predictive maintenance, or automating customer support, the Operational AI system will help you streamline these processes.  

Continuous Learning and Improvement

The Operational AI system continuously learns from new data and user feedback. It refines its algorithms to improve accuracy, enhance automation, and adapt to changing business needs over time.

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Conclusion

Operational artificial intelligence focuses on automating and optimizing real-world business processes by embedding AI outputs into daily operations. Integrating AI into your organizational workflows ensures agility, scalability, and sustained competitive advantages in a rapidly growing business environment.