6 legacy modernization challenges solved by Contextualized AI

Cover image of the content on Legacy Modernization Challenges, featuring a Neural Network Programmer developing an AI data processor.
The legacy modernization challenges can be overcome with the help of contextualized AI, bringing more efficiency and agility to the development cycle.

The legacy modernization challenges are many  — from the process complexity to developer experience. But contextualized artificial intelligence and its many possibilities can help your organization overcome these challenges.

At this point, generic AI tools don’t offer all the necessary answers adapted to the company’s context, such as code language, stacks, structures, etc. 

Hence, contextualization is key for AI to provide solutions focused on legacy modernization challenges and adapted to the reality of each company.

These are some of the challenges that were overcome with the assistance of contextualized generative AI.

Why adopt contextualized AI in a modernization process?

Before considering what to adopt, we must understand what we need to modernize: legacy systems. These platforms use obsolete technologies, such as outdated programming languages that are difficult to maintain. 

Apart from all the manual work and technical debt, the use of monoliths, incompatibility with modern systems, and the lack of a mobile version hinder the software’s scalability.

Therefore, contextualized AI like StackSpot AI provides major benefits for the modernization journey, such as:

  • faster and more convenient code generation;
  • better readability and easier code maintenance; 
  • support for code quality and security actions, 
  • help with documentation, among others.

Equipped with company’s information and data, AI has the necessary context to offer more assertive solutions to developers, enhancing the work of professionals who also have the mission of reviewing the results obtained by AI.

Legacy modernization challenges that can be solved by a contextualized AI tool

A contextualized code assistant perfectly combines the solution to the main legacy modernization challenges. Explore the topics that support this claim:

1 – Converting code from outdated to modern technology

Legacy systems have obsolete technology with programming languages that are seldom used, which can lead to

  • software scalability issues;
  • longer time to market;
  • negative customer experience; 
  • high security costs;
  • software instability;
  • negative experience for developers;
  • lack of technical documentation, and more.

In this sense, contextualized artificial intelligence can provide quick and user-friendly assistance. First, you need to feed the AI assistant with all the context it needs to deal with the project. 

Then, break down the problems into smaller steps, and AI can be encouraged to explore different angles to solve them.

Finally, simply use a prompt to convert a specific programming language to a more up-to-date one for the developer to review and approve.

Check on our blog an example of legacy modernization in a mobile project using StackSpot AI.

The StackSpot AI video shows how this process takes place:

2 – Migrating your environment to the cloud

The benefits of migrating to the cloud are well known, such as improved performance, lower costs, and stronger governance and compliance. 

Artificial intelligence facilitates the integration of agile development, making maintenance of migration processes more efficient and minimizing errors, leading to faster and more assertive outcomes.

With the support of contextualized AI code assistants, code can be created to speed up the software development cycle. Based on context, knowledge sources, and technological decisions about standards, AI produces more assertive code to create a new cloud environment with greater quality.

Importantly, the cloud environment often coexists with the legacy environment during the migration to the modernized environment. When modernization is complete, it is still necessary to migrate information (for example, about customers and historical operations) from the legacy system to the modern one. Contextualized AI can help reduce the team’s cognitive load in these situations. 

Finally, artificial intelligence also processes large amounts of data, supporting structured work and proper analysis. Hence, AI offers the accuracy of structured processes and procedures, which make a stark difference in efficiency and productivity.

3 – Modernizing monolith applications for microservices

In practical terms, artificial intelligence in microservices can be integrated into predictive analysis and natural language processing. In particular, contextualized artificial intelligence and DevOps practices highlight points that can be improved in the software development cycle, such as automation, data-based decision-making, and predictive insights.

With the introduction of AIs, manual tasks can be automated, from testing to monitoring, making processes more efficient and with fewer errors. In StackSpot AI, you can create “Quick Commands,” a set of customized prompts, to eliminate repetitive AI tasks.

This optimization of workflows can identify problems before they happen and even predict results, delivering significant gains in software quality.

4 – Developer integration and overcoming technical debt

When a new developer joins a team, this person has much to learn about the company’s software. All the information is centralized in a developer platform, and a code wizard helps to onboard new people to the team. 

With this new flow of platforms and tools, everyone on the team —regardless of seniority — can become more productive while maintaining quality and safety standards in software development.

By providing StackSpot AI with all the content needed to create Knowledge Sources, our artificial intelligence offers contextualized solutions to reduce technical debt and improve the health and efficiency of your software.

For example, AI prevents accumulation of technical debt in the long term and maintains software health by:

  • automating testing and quality processes;
  • establishing CI/CD pipelines; 
  • analyzing code for errors (code review and code smell);
  • helping with the software’s technical documentation;
  • predicting and preventing problems;
  • encouraging innovation and promoting good results.

5 – The slow modernization process

Research by McKinsey found that generalist AI speeds up software development by up to 45%, enabling a range of automation and ensuring more of developers’ time is devoted to business solutions.

In other words, StackSpot AI can speed up development even more as a contextualized tool with Knowledge Sources adapted by the company.

The tool has generated multiple opportunities in the software development flow, such as helping to extract information, generating technical documentation, accelerating source code for more modern architectures, building new platforms, migrating to the cloud, and more. 

These activities can be done with AI, reducing manual work by devs. The entire development cycle is accelerated and quality is gradually improved because organized information makes devs’ routines much easier.

6 – Cost of the modernization process

When you consider modernizing an entire system, you also consider how much it will cost. So, the sixth legacy modernization challenge is the cost of this process.

The good thing is that artificial intelligence can provide modernization plans, refactoring, testing and validation, data analysis, pattern identification and more to accelerate system modernization efforts.

In fact, Gartner forecasts that by 2027 generative AI tools will be used to create suitable replacements for legacy systems, reducing modernization costs by 70%. 

Therefore, an AI tool that is contextualized and adapted to the reality of each company has great potential to optimize the costs of a process as complex as software modernization in a more objective manner.


Modernizing legacy systems is a significant challenge. Contextualized artificial intelligence tools can be great allies in making development flows faster, healthier, and more efficient.

With automation, code corrections, language conversion, migration to the cloud, documentation generation, and more functionalities, developers don’t have to work on the code manually. They can devote their time to strategic tasks that target software scalability and customer success. 

In addition, information centralization and complete documentation produced with the help of AI facilitate developers’ work. Such actions improve outcomes as well as the developer experience. Now that you’ve learned about legacy modernization challenges, find out how StackSpot AI can help your company. Get in touch with our team of experts now.

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