Introduction
According to a recent report by Gartner, over 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That is almost an eightfold jump in just a single year, and most of this shift is happening on the platforms that enterprises already use to ship their software, with one of the biggest being .NET.
Until early 2025, most AI features in .NET applications depended on a separate Python service for the model layer and often a third system for vector search. With the launch of .NET 10 in November 2025 and the production release of Microsoft Agent Framework 1.0 in April 2026, C# teams now have native APIs for agent orchestration, multi-provider model access, and middleware for safety, logging, and compliance. Combined with ML.NET, Semantic Kernel, Azure AI services, and Microsoft Foundry.
For engineering leaders, that means less infrastructure to manage, a single security and compliance review, and AI features that can ship with the rest of the application.
This guide walks you through the eight key use cases of AI in .NET that are catching the trend in 2026, with details on the tools behind them, and the outcomes teams are seeing.
Top 8 Use Cases of AI in .NET in 2026
Here are the eight use cases of AI in .NET that are trending in 2026 across financial services, healthcare, retail, and manufacturing.
1. Intelligent Process Automation and Workflow Orchestration
Many business processes, like loan reviews, claims triage, and vendor onboarding, take days because they need to pass through multiple teams and systems.
But after .NET 10, .NET teams can now build agents with Microsoft Agent Framework 1.0 that start the moment a request comes in, work through each step, and hand the task to another agent when a different skill is needed. The agents run inside the same .NET application, so logging, access control, and compliance work the same way as the rest of the system. This cuts cycle time from days to hours and frees up the operations team from chasing manual approvals.
Example: A regional bank can use a .NET-based AI agent to review loan applications. The agent will check the applicant’s credit data, run the risk score, and prepare the file for the underwriter, cutting a two-day process down to under an hour.
2. Predictive Analytics and Data-Driven Decision Making
With ML.NET, teams can train forecasting and classification models directly in C# and run them inside the same .NET application that uses the predictions.
There is no separate Python service to maintain, no second deployment pipeline, and no waiting on the analytics team for routine model updates. And, inventory, staffing, and lending decisions can happen in the live application instead of in a weekly report.
Example: A retailer can use ML.NET to forecast weekly demand for each product, with the .NET ERP picking up the forecast and generating purchase orders automatically.
3. Personalization Engines for Customer-Centric Applications
With Azure OpenAI and Azure AI Search, an ASP.NET Core application can personalize content and product recommendations without a separate recommendation engine running alongside it. Azure OpenAI turns user behavior into vectors, Azure AI Search finds the closest matches, and the ASP.NET Core page renders the result for that session.
This setup works for tens of thousands of users at the same time, and the benefit shows up in conversion rates and average order value.
Example: A large e-commerce company can personalize homepage layout and product recommendations per session, leading to an increase in conversion rate and average order value after implementation.
4. Generative AI and Conversational Interfaces
Semantic Kernel allows integrating chatbots powered by LLMs into the business application, connecting them to business data and plugins, and supporting multiround dialogues authenticated with Microsoft Entra ID.
This makes it a good use case for IT support, HR self-service, and internal knowledge requests, where employees would otherwise file a ticket or wait for a human reply.
Example: An HR system can use a Semantic Kernel assistant to answer employee questions about leave balances, policies, and reimbursements. This can help reduce the number of routine helpdesk tickets.
5. Software Development and AI Augmented Code Intelligence
.NET-based AI/ML agents can integrate with the GitHub Copilot SDK to scan a repository on every pull request, flag security anti-patterns, and generate unit tests where coverage is missing. The agent runs as part of the CI pipeline, so the review feedback shows up on the PR itself instead of in a separate tool.
This is one of the more practical use cases of AI and ML in .NET for engineering teams maintaining large or older codebases, where manual review struggles to keep up with the volume of changes.
Example: A DevOps team can build a .NET agent that reviews every pull request for OWASP-style anti-patterns and writes the missing unit tests.
6. Intelligent Image Processing and Computer Vision
With the Azure AI Vision SDK, a .NET application can run optical character recognition, object detection, and defect classification without building a separate computer vision service. The output (text, object labels, or defect flags) goes straight into the ERP or quality management system, with no separate database to maintain.
This is a common use case of AI in .NET in manufacturing and logistics, where visual inspection happens at speed, and every event needs to be logged for audits.
Example: An electronics manufacturer can use a .NET computer vision pipeline with Azure AI Vision to scan circuit boards on the production line, classify defects, and write the inspection record straight into the ERP.
7. AI-Powered Threat Detection and Cybersecurity
ML.NET and Azure Monitor can work collaborate with Microsoft Sentinel to train custom models for anomalies based on an organization’s traffic baseline.
ML.NET will train the anomaly model on the organization’s actual traffic patterns, Azure Monitor collects the telemetry from the application, and Microsoft Sentinel handles incident response. The model learns what normal looks like for that specific business, so it raises fewer false alarms than a generic security tool.
Example: A fintech company can integrate threat detection into its .NET application to monitor every API session for unusual activity.
8. AI-Driven Health Care and Compliance-Oriented Solutions
A Semantic Kernel agent built on .NET can transcribe a doctor’s conversation with a patient as it happens, draft the diagnosis codes from the transcript, and store everything in HIPAA-compliant Azure infrastructure. Because the agent runs as part of the hospital’s .NET system, audit logging, role-based access, and data retention rules apply to it the same way they apply to every other module.
This is one of the more impactful use cases of AI in .NET for healthcare providers, where physicians lose hours each day to documentation and where coding errors directly hurt insurance reimbursement.
Example: A regional healthcare service provider can deploy the agent across its outpatient clinics, so physicians spend less time on documentation and more on patients.
Conclusion
AI in .NET has reached a point where teams can build agents, predictions, and assistants directly into the applications they ship every day, without a separate machine learning team and without a second technology environment to maintain.
The eight use cases of AI in .NET covered in this article are where .NET investment is heading in 2026: workflow automation, forecasting, personalization, internal assistants, code review, visual inspection, threat detection, and clinical documentation.
For companies running on .NET, the next step is choosing where to start and finding the right people to build it. The tools are good, but they only work well when the team using them knows what they are doing. That is why companies should hire .NET developers with direct experience in ML.NET, Semantic Kernel, and Microsoft Agent Framework 1.0 to lead the implementation of AI in .NET.