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Innovate and automate using AI services

Every application modernization activity leads to richer AI capabilities, and all types of business applications can benefit.

For example, you can use AI features in Microsoft Dynamics 365 to personalize your marketing and sales strategies based on customer segmentation, sentiment analysis, and sales forecasting. Or use AI to enhance existing enterprise resource planning (ERP) systems and optimize supply chain management, financial planning, and human resources. AI can predict maintenance needs, optimize production schedules, and improve resource allocation.

Consider the following ideas:

  • Integrate AI with existing apps. Use Azure App Service to deploy applications that work with Azure AI models. For instance, you can develop a Blazor web application that interacts with Azure AI services to generate responses or automate content creation.
  • Add sophisticated AI features. Enhance your applications with vision, speech, language, and decision-making features. Azure AI services provide APIs image recognition, natural language processing, and anomaly detection, enabling you to easily add dynamic AI features.
  • Build custom copilots. Assist your users by creating custom copilots that offer suggestions, generate content, and answer questions. Using Azure AI Studio, you can connect your data and build generative AI applications.
  • Process and enrich content. Transform raw content into searchable data using Azure AI Search text extraction, entity recognition, and image analysis. Or use vector databases and Retrieval Augmented Generation (RAG) to deliver high-quality responses that improve search relevance and user satisfaction.
  • Build AI-driven solutions. Combine Azure AI Search with Azure Machine Learning and other related services to build entirely new intelligent solutions.

The following sections suggest a few more ways to work with AI:

Automate data processing using Azure AI Document Intelligence

In a world full of data, any solution that makes it easier to process and analyze data is a win. You can automate the process of extracting information from documents using Azure AI Document Intelligence, part of Azure AI services. Prebuilt models make it easy to extract text, key-value pairs, and tables from invoices, receipts, forms, and other documents.

As a modernization strategy, automated document processing can help you significantly reduce manual data entry and improve accuracy. For more control, you can train custom models, tailoring them to your specific document types to extract relevant information more accurately.

Document intelligence solutions require robust data management. We recommend using Azure Database for PostgreSQL, a managed relational database service. It works well with the other services that support data ingestion, enrichment, querying, and analysis.

A document intelligence workflow includes four key areas:

  • Data ingestion. Store documents in Azure Blob Storage, then use Azure Data Factory to move data from storage to Azure Database for PostgreSQL.
  • Document processing. Use Azure AI Document Intelligence to extract data from documents, then store the data in Azure Database for PostgreSQL.
  • Data enrichment. Work with language models and other AI capabilities directly in your database using Azure AI Services and the azure_local_ai extension for Azure Database for PostgreSQL. You can use Azure AI Language for summarization, sentiment analysis, and key phrase extraction.
  • Query and analysis. Use Microsoft Fabric to perform advanced analytics on the data stored in Azure Database for PostgreSQL. Then visualize the results using the interactive dashboards and reports in Microsoft Power BI.

Automate business processes using AI

AI-driven automation can replace tedious manual tasks so you can focus on more high-value work across your organization. For a quick win, identify repetitive, time-consuming tasks that are prone to human error. Common examples include data entry, invoice processing, and customer support.

The following Azure services can help you get started:

  • Azure Logic Apps. Automate workflows and integrate apps, data, and services across organizations. Azure Logic Apps provides connectors for Microsoft Office 365, Microsoft Dynamics 365, and hundreds of other platforms.
  • Microsoft Power Automate. Consider using robotic process automation, and let bots do the work. For example, you can create automated workflows between your apps and services to extract data, fill out forms, generate reports, synchronize files, get notifications, and collect data.
  • Azure Machine Learning. Build, train, and deploy machine learning models to automate decision-making processes. For example, use predictive analytics to forecast demand and optimize inventory management.
  • Azure AI services. Use prebuilt AI models for language understanding, speech recognition, and image analysis so you can automate customer interactions and document processing.
  • Azure Monitor. Analyze monitoring logs and data to optimize the performance of automated workflows. For example, you can use the performance and health metrics from Azure Monitor to identify bottlenecks and areas for improvement, analyzing the large datasets using Microsoft Fabric or other analytics tools to get actionable insights.

Build responsible, secure AI systems

As you invest in AI, make sure to establish ethical standards for responsible AI systems. Consider following Microsoft responsible AI principles to ensure that the systems you build and deploy are ethical, secure, and compliant with data protection standards.

Working with AI systems and large language models present unique security challenges. As with any applications you design, AI systems require secure data transmission, encryption, and access controls on an architecture robust enough to withstand adversarial attacks that attempt to manipulate or misclassify data.

We recommend a layered approach to security:

  • Follow the Microsoft Secure Development Lifecycle and integrate security tools and practices throughout development and operation—from secure coding to vulnerability scanning and regular security testing.
  • Develop a threat model to identify potential security risks and plan mitigations.
  • Implement continuous monitoring and auditing to detect and respond to security incidents in real-time.
  • Adopt a Zero Trust security strategy—verify explicitly, use least-privilege access, and assume breach.

Next steps

Continue the journey and review innovation best practices.