Companies urgently need to modernize their legacy systems by integrating AI to remain competitive and drive digital transformation. Currently, 42% of organizations are incorporating AI into their existing applications as part of their modernization efforts. Microsoft accelerates AI innovation by providing a comprehensive cloud and AI platform that facilitates this transition. Their strategy involves assessing, modernizing, and infusing AI into applications, then optimizing them for enhanced performance. By 2025, businesses can significantly improve operational efficiency and unlock new revenue streams through these transformations, leveraging Microsoft’s crucial cloud partnerships. Cloud migration is a foundational technological initiative that supports future growth, especially within AI-first paradigms. The global AI market is poised for substantial expansion for businesses that embrace these changes. The projected benefits are illustrated below:Key Takeaways* Companies must update old apps with AI. This helps them stay competitive. Microsoft Cloud offers tools to make this easier.* Check your old apps first. See if they can use AI. Microsoft Azure Migrate helps find AI chances and fix old code.* Move apps to Azure. You can move them as they are or change their code. Tools like .NET Core and GitHub Copilot make this faster.* Add AI features using Azure AI services. Build custom AI models for your business. Use Azure data platforms to manage AI data.* Use AI wisely. Add it step by step. Keep AI systems safe. Teach your team new skills to manage these changes.Assessing Legacy for AI ReadinessFirst, companies must check their old apps. They need to see if these apps can use AI. This is a very important step. It means looking closely at all current systems. Microsoft has tools like Azure Migrate. These tools use new AI features. They make checking apps much faster. The AI helps guide the process. It does many tasks automatically. This makes migration quicker and smarter. It does not need much new training. Azure Migrate also understands apps well. It sees all apps in a company. It also looks deep into each app. This includes how apps connect. This helps make smart choices. These choices are based on facts. This happens during the whole update process.Identifying AI OpportunitiesFinding chances for AI in old systems needs a clear plan. Teams must look at old tech. They need to find slow spots. They must check for security holes. They also need to fix connection problems. They also need to check data quality. They must make sure data is clean. Data needs to be in a standard form. It must connect from all places. This helps AI work well. Linking AI chances to business goals is key. This matches AI uses to company aims. It puts important things first. This is based on how much value they bring. It also looks at how easy they are to do. It can change old code. It can make it new. It can also speed up testing. It makes checking for errors faster. It finds problems quickly. It makes connecting systems easy. It brings different data together. Also, AI watches system performance. It does this all the time. It can guess and stop problems. It makes apps better for edge computing. This helps them grow easily.Technical Debt and ModernizationFixing technical debt is a big part. It is key to updating any app. Signs of technical debt include complex logic. They also include repeated patterns. Old libraries are another sign. Software that is no longer supported is too. Architecture drift is a sign. Hardcoded settings are also. Weak connections are another. Missing documents show it too. Security gaps are a big risk. These include known flaws. Old ways of logging in are risky. We can measure technical debt. Chronology shows when code was last changed. Coverage shows how much is tested. Caliber means code quality. Codependence shows how things rely on each other. Consumption means resources used. Contention shows areas changed often. These help find where debt builds up. Watching code complexity is important. Watching how much code changes is too. Checking test coverage is vital. This helps with good updates.Data Strategy for AIA strong data plan is key for AI. This means finding all data. It means checking data sources. It means seeing how data moves. It means seeing who uses data. It also finds hidden data. It finds old data sources. Data integration needs automated ways. These ways bring in data. They change data. They line up data from many places. This includes matching data types. It includes making formats standard. Data governance sets rules. It checks data quality. It makes sure data is trusted. It makes sure data is correct. This includes who can see data. It tracks rules. It finds strange data. Metadata management keeps info. It tracks where data comes from. This is for each piece of data. This makes checking easier. It builds trust. It makes data reusable. Labeling and training are next. This means making ways to tag data. This is for unstructured info. It defines categories. It sets labeling rules. It checks data for AI models. This plan makes sure data is ready for AI.Modernizing and Moving Apps to AzureThis part shows how to move old apps to Azure. Companies pick different ways. This depends on what they need. It also depends on their goals.Move Apps to Azure IaaS/PaaS“Lift-and-shift” means moving apps. They go to Azure Infrastructure as a Service (IaaS). Or they go to Platform as a Service (PaaS). You change the code very little. This gets apps to the cloud faster. It saves money on hardware. You pay for what you use.Making things work better is a main goal. Just moving apps does not always use all cloud features. Many think moving to Azure makes things faster. But apps made for old systems might not use cloud power fully.Apps moved this way might not use all Azure features. This includes PaaS or serverless options. It also includes cloud scaling. This limits how much they can grow. It limits how strong they are. It limits how much money they save. But it is still better than old systems.Think about the cost of moving apps:Azure PaaS lets you pay by the hour or month. This is different from big upfront costs. IaaS is usually cheaper than PaaS. This is true for similar work. But IaaS might need more software. You buy this from other companies. This can cost more than PaaS. Also, fixing the basic parts costs money. This adds to the total cost. Old systems cost a lot. They need huge money at every step. Cloud systems like Azure IaaS/PaaS save companies money. They also save them from managing things. They cut costs of buying gear. They cut costs of hosting things yourself. This first step to the cloud helps with future updates.Changing Code to Cloud-NativeRefactoring means making code better. It does not add new things. This helps update old code. It makes it less messy. It gets work ready for Azure. This is good if code costs a lot to keep. It is good if code is messy. Azure SDKs or services can also make things better. They can help you see how things are working..NET Core helps a lot with updates. It makes it easy to improve old apps. It does this without big problems. It works on many systems. It is open-source. It fixes problems of the older .NET Framework. It is a great choice for better systems. This is true if you do not need the newest .NET features. For old apps that need to last, .NET Core 3.1 is good. It works with old .NET framework code. It works with many libraries.Here are good things about .NET Core for updating apps:* Cross-platform support: New .NET lets you build apps once. They run on Windows, Linux, or macOS. This makes work smoother. It makes development easier.* Cloud-native development: It is perfect for containers. It is perfect for small services with .NET Core. It is perfect for quick growth.* Robust Security: Security is built-in. It gets constant security fixes. It has tools for rules. This makes it good for strict industries. These need safe data. They need less risk.* Improved Performance and Efficiency: Updating to the newest .NET versions helps apps. They use new features. They run faster. They run better. This is true with new multi-core chips.* Better Memory Management: Updates use better code and libraries. This means memory is used better. There are fewer memory problems. Things run smoother.* Improved Scalability and Agility: Updates let you use cloud systems. It helps break apps into smaller parts. This makes them easier to grow. It makes them easier to update. It makes them easier

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