Ispirer vs. Generative AI for Database Migration and Application Conversion

Generative AI is clearly in the spotlight. It is actively discussed at conferences, in technical blogs, and in internal strategy sessions as a way to accelerate modernization. Software engineers use it to understand legacy systems faster, generate example code, analyze unfamiliar SQL dialects, and explore possible migration paths. That is why conversations around generative AI for database migration and application conversion appear so often when organizations start planning modernization initiatives.

At the same time, enterprise modernization is not driven by trends alone. It is a structured, multi-stage process where predictability, control, and risk management matter just as much as speed. Database schema migration, data migration, and application conversion are closely connected, and issues in one layer often cascade into others. This is where the difference between database and generative AI approaches and purpose-built migration tools becomes visible.

Ispirer enables modernization by separating responsibilities across three products. SQLWays handles database schema and SQL objects, Ispirer Data Migrator focuses on data migration, and CodeWays is used for application conversion. This separation allows developers to combine automation with expert oversight instead of relying on ad-hoc scripting.

Ispirer and generative AI for database migration

When working with database migration tasks, generative AI can quickly explain how a construct in one database works, suggest equivalent syntax in another dialect, or help engineers reason about edge cases they do not encounter often. For early investigation and learning, this shortens the time needed to understand unfamiliar platforms.

However, database migration is not a collection of isolated translations. Enterprise schemas encode dependencies between objects, execution order, procedural logic, and performance assumptions that span the entire database. Generative AI does not maintain a persistent model of the schema, which limits its ability to reason about the database as a system.

Database schema migration: comparison

How schema migration works in practice

SQLWays approaches schema migration as a structured engineering task. It provides a comprehensive analysis of the database, identifies object dependencies, and applies predefined transformation rules consistently across the schema.

Generative AI supports this process mainly at the analysis stage. It can help engineers review complex procedures, understand legacy constructs, or assess how risky certain SQL features may be on the target platform. Once migration starts, however, these decisions need to be applied consistently across the entire schema, which is where a database migration tool becomes essential.

Manual work remains part of the process, but it is focused. Experts review results, resolve edge cases, and verify behavior instead of rewriting SQL object by object. Compared to approaches resembling an AI database migration converter, this produces more stable outcomes at scale.

Procedural SQL and complex cases

Procedural SQL is often the most fragile part of database migration. Cursors, exception handling, transaction control, and vendor-specific extensions rarely map cleanly between databases. Even when similar constructs exist on the target platform, their behavior may differ in subtle but important ways.

Generative AI can be helpful at this stage by explaining how specific constructs behave, outlining possible alternatives, or highlighting areas that deserve closer attention. This is especially useful during review, when unfamiliar procedural logic or legacy patterns need to be understood quickly.

However, ensuring that behavior remains correct still requires careful validation. Every execution path, error condition, and side effect needs to be checked to confirm that nothing has changed unexpectedly. SQLWays reduces this effort by applying conversion logic refined through real migration projects, handling common procedural patterns consistently and leaving fewer corner cases for manual review.

Handling change propagation and re-runs

In real projects, schema migration rarely happens in a single pass. After the initial conversion, testing often reveals issues that require changes to mappings, procedural logic, or individual objects. As a result, migration steps need to be rerun more than once.

SQLWays is built with this reality in mind. Conversion rules and mappings can be adjusted and then reapplied across the entire schema in a controlled way. This makes it possible to refine results based on testing feedback without rebuilding the migration process from scratch.

When Generative AI is used for schema conversion, reruns usually require more manual coordination. Converted objects need to be tracked individually, updates must be applied consistently, and previous results need to be checked for unintended differences. As the number of objects grows, keeping everything aligned becomes increasingly difficult.

Ispirer Data Migrator and data migration at scale

After the schema is migrated, the focus shifts to data. At this stage, challenges change completely: data volume, system availability, correctness, and timing become the primary risks.

Generative AI can contribute during preparation by helping developers think through migration strategies, identify potential data quality issues, or draft migration scripts. This fits naturally into AI data migration and automation scenarios where AI supports decision-making rather than execution.

Data migration: comparison

What makes data migration different

Data migration usually happens while systems are still in use. Tables continue to change, users continue to work, and integrations keep running. Ispirer Data Migrator is designed for this reality, supporting parallel data transfer and continuous synchronization so that migration does not block business operations. 

Generative AI does not observe runtime behavior or manage failures. This is why enterprise Data Migration Tool remains essential. A dedicated AI data migration tool reduces risk by automating monitoring, validation, and recovery, while engineers supervise the process instead of manually reacting to issues.

Managing live data changes during migration

Data migration rarely takes place against a static source. In most enterprise systems, data continues to change while migration is in progress, and business operations cannot simply be paused.

Ispirer Data Migrator is designed for this situation. It supports continuous synchronization between source and target databases, allowing ongoing data changes to be captured and applied during the migration process. This helps reduce downtime and makes the final cutover more predictable.

Generative AI does not handle this aspect directly. While it can help plan migration steps or draft scripts, tracking live changes, applying them reliably, and validating results still require dedicated mechanisms. Without this support, migration often depends on longer freezes or manual reconciliation.

Ispirer and Generative AI in application modernization

Application modernization is where Generative AI often delivers the most visible value. It can read legacy code, summarize business logic, explain control flow, and generate modern equivalents for individual components. This significantly reduces the effort required to understand large or poorly documented applications.

Generative AI is also useful when discussing refactoring options or architectural changes. It can suggest alternative designs, highlight potential simplifications, or point out areas of technical debt. In this sense, it plays an important role in generative AI app modernization by accelerating analysis and exploration.

Application conversion: comparison

Application conversion with CodeWays

CodeWays treats application conversion as a system-level task. It analyzes how modules interact, how data access is implemented, and where business logic resides. This allows conversion decisions to be applied consistently across large codebases, even when multiple technologies are involved.

Automation significantly reduces manual rewriting, but human involvement remains essential. Experts review converted code and handle complex cases, while Generative AI assists in understanding and guiding refactoring decisions.

Ensuring consistency across large codebases

In application conversion, consistency matters just as much as correctness. When similar code patterns are converted in different ways across an application, the result becomes harder to understand and maintain over time. 

CodeWays applies the same conversion rules across the entire codebase. Similar constructs are transformed in a consistent manner, regardless of where they appear. This simplifies code review and reduces the risk of subtle behavioral differences between modules.

With Generative AI, consistency depends largely on how prompts are written and reused. Small variations in input can lead to different results for similar code fragments. In larger applications, keeping conversions uniform therefore requires additional review and follow-up refactoring.

Ispirer Toolkit for embedded SQL and business logic

Embedded SQL often links application code directly with the database. Queries are mixed into control flow and error handling, so even small changes can affect how core processes behave at runtime. Issues like this may only appear after deployment, which can make them time-consuming to fix.

Ispirer Toolkit addresses this by converting embedded SQL consistently across the entire codebase while preserving business logic automatically. Similar queries and database calls are rewritten using the same rules everywhere, helping ensure core processes behave as expected and simplifying testing and maintenance. This approach is unique – most migration tools do not migrate business logic automatically, leaving much of the work to developers.

Generative AI can assist by rewriting individual SQL fragments or clarifying how a statement should change. However, it does not provide the structured, system-wide control needed to ensure consistent behavior. Without a dedicated tool like Ispirer, inconsistencies can still appear later, requiring additional effort to resolve.

Are AI tools for data migration enough?

Given the popularity of Generative AI, it is natural to ask whether AI-based tools are sufficient on their own for enterprise migration projects.

In practice, Generative AI excels at assistance: understanding systems, reducing uncertainty, and supporting analysis. What it does not do is manage large-scale execution, validation, or recovery. Structured tools like SQLWays, Ispirer Data Migrator, and CodeWays automate repeatable work and deliver predictable results, while experts remain responsible for decisions and validation.

Successful modernization projects combine structured automation with selective use of Generative AI – not because AI is limited, but because complex systems require both insight and control.