Leaders know that migrating to the cloud is key to a successful long-term digital strategy. Whether you want to enhance your software portfolio’s scalability, flexibility, and cost-effectiveness, harness cutting-edge services and tools, or facilitate the integration of emerging technologies for an enhanced user experience – or all of the above – cloud adoption is essential. With all that’s on the line, why do so many migrations fall short?

Like so many other technology problems, it really boils down to complexity. Typically, cloud migration encompasses many activities, including (but not limited to) application and portfolio assessment, technology mapping, code and configuration translation, data migration, security compliance, testing, and employee upskilling. The sheer magnitude of these tasks has traditionally made cloud migration a daunting and resource-intensive endeavor that can sometimes take years to complete. And that’s not even considering common obstacles, like coordinating diverse teams, juggling competing business priorities, ensuring data security, managing legacy code, and navigating unfamiliar codebases – sometimes even the absence of source code.

Generative AI Can Make All the Difference

This may seem discouraging, but there’s light on the horizon. Recent breakthroughs in generative AI technology are poised to transform the cloud migration landscape. Large Language Models (LLMs) possess the capability to scrutinize and translate source code, ensuring compatibility with the requirements of cloud platforms. Moreover, LLMs can autonomously generate code, tests, scripts, or configuration files, thereby substantially reducing manual labor. They can even intelligently recommend migration strategies and, with the proper guidance, design the required architecture for application migration. The result? Migrations that can be completed in weeks – or in the future, minutes (or even less).

And the possibilities don’t stop at the mechanics of a cloud migration. Since LLMs can analyze and understand the intricacies of different cloud platforms to facilitate cloud migrations, they have the potential to untether organizations from a single vendor and empower them to choose the best services from a cloud provider, reducing the risk of vendor lock-in and radically changing how organizations approach their cloud strategy.

Our Cooperative Model Experiment

At Nuvalence, we’re exploring how Large Language Models (LLMs) can be applied to the many tasks involved in cloud migration. Early results are positive, especially in the LLM’s capacity to comprehend and translate source code, as well as adapt Infrastructure as Code (IaC) to the target cloud. We’ve also learned that a human-in-the-loop approach is indispensable in specific scenarios to ensure the accuracy and reliability of the model.

Using the 6-category taxonomy for generative AI use cases as inspiration, we decided to build a Cooperative Model (Level 3) toolkit to leverage LLMs in cloud migrations.

Prediction: Game-Changing Outcomes

Based on our experiments so far and the velocity and trajectory of the AI community, we expect the following outcomes when applying generative AI to the cloud migration problem space:

  • Accelerated Migrations. By automating and streamlining cloud migration tasks— including codebase analysis, code translation, and generation of pipeline scripts for the target cloud, LLMs unlock the ability to execute potentially unlimited parallel migrations. This translates into a dramatic reduction in migration time for complex application portfolios, potentially bringing the migration time down from years to just weeks, thanks to the ability to parallelize many migrations. Migrating a large portfolio can essentially be governed by the duration of the most complex application migration.

  • Human Resource Optimization. With AI handling most tasks, the human resources required for migrations are reduced by an order of magnitude. Instead, a specialized enabling team can manage critical and complex tasks, and handle the migration of tens, if not hundreds, of applications.

  • Opportunity Cost Capture. An additional benefit of human resource optimization is the opportunity for organizations to redirect their top talent’s efforts towards higher-value work that requires uniquely human skills.

  • Enhanced Accuracy. Generative AI models can also detect and correct configuration errors, thereby increasing the accuracy and integrity of the migration.

Incorporating generative AI in cloud migration processes presents an unprecedented opportunity for organizations to streamline their migration activities. By combining the automation and code transformation capabilities of AI with the expertise of human SMEs, we can not only accelerate the migration process, but also boost its reliability and efficiency. Moreover, with the continual evolution and enhancement of these models, we will be able to apply generative AI more effectively and across additional functions, further streamlining the cloud migration process. 

Looking ahead, the adoption of generative AI in cloud migrations is poised to revolutionize how organizations approach cloud services by mitigating or eliminating vendor lock-in constraints, thus providing organizations with greater flexibility and control over their cloud strategies.

Follow Along With Our Discoveries

As we continue to leverage AI in cloud migrations, we will produce and maintain application templates and other artifacts to guide the LLMs in generating and validating migration content. This ever-growing “knowledge center” results in a compounding effect that further streamlines migrations and provides unprecedented value for our clients.

Stay tuned for a series of blog posts, where we will continue to explore the usage of AI in cloud migrations and share our learnings along the way. We will discuss our prompt engineering practices, comparisons between the available LLMs, and our approach to breaking up the cloud migration process into LLM-friendly steps.

Let’s talk about your future.