Cloud migration has become a continuous balancing act to ensure companies can effectively manage costs, sovereignty and AI workloads, says Chris Badenhorst, Head of Azure Core Services at Braintree.
Six percent. That’s how many database migrations have finished on time. Cloud migrations are even more complex, moving entire workloads across environments in processes that are anything but straightforward.
Transferring complex infrastructure, modernising legacy applications and migrating critical databases has traditionally required months of planning, extensive manual work and significant technical risk. But what if AI could change this conversation?
AI tools such as Azure Migrate and GitHub Copilot are accelerating and simplifying the process of migration, providing AI-led guidance, connected workflows and automated assessments that make it a lot easier for companies to move faster and smarter.
And you can do all of this without having to rebuild your skills or retool your systems. Microsoft is changing migration from manual work into a smarter experience that uses AI to automate key functions such as discovery, dependency mapping, wave planning and even return on investment (ROI) business cases so you can assess, plan and execute cloud migrations extremely quickly.
Microsoft isn’t the only company paying attention to how AI is changing the game. AWS, Google Cloud and Oracle are also investing in agentic AI capabilities that allow teams to manage their workloads, discovery, costs and migration pathways. The trend, it seems, has become a reality.
There are multiple benefits. First, agentic AI guidance can reduce risk because security and governance can be embedded into the migration flow, which means you’re improving resilience and compliance from the outset.
And you’re setting up the right foundations for your company’s ability to scale AI workloads in the future. The second benefit is discovery. AI-driven discovery, readiness scoring and cost-model simulations in Azure can cut the amount of time spent on planning and assessment by weeks.
Think minutes as opposed to hours, and then there’s the operational savings that come with that time compression. You can reduce the cost of your entire migration expenditure thanks to predictive rightsizing and the ability to optimise consistently.
At its core, AI-assisted migration replaces static tools and manual scripting with AI-driven agents that can analyse infrastructure, recommend architectures and generate deployment templates that are aligned with best practices.
For companies that have accumulated years of technology complexity, this creates a powerfully simple way of starting out on a modernisation journey.
However, migration isn’t just about moving infrastructure, it’s also about transforming applications and data so they perform effectively in cloud environments.
You don’t want old problems in a new house; you want optimised systems. And AI-assisted migration supports this with several key capabilities.
Infrastructure migration is the first step where AI tools can analyse virtual machines, servers and networks within existing environments like VMware or Hyper-V and automatically generate migration blueprints.
These recommendations include Microsoft’s Cloud Adoption Framework (CAF) and Well-Architected Framework to ensure your proposed architecture aligns with established cloud best practices. Recent updates to Azure Migrate include agentless discovery of servers and virtual machines and other key recommendations aligned to CAF.
This introduces a new paradigm, known as infrastructure as code, where instead of configuring elements manually, you can define the architecture of your infrastructure through code-based templates. This approach ensures consistent deployments, stronger governance and easier long-term management.
Application modernisation is another key part of this process. Many companies are still relying on legacy applications built on outdated platforms or monolithic architectures, so AI tools like GitHub Copilot can analyse existing codebases and recommend changes that will allow these applications to run within modern cloud platforms.
This can include refactoring code, supporting containerisation and helping to transform traditional monolithic applications into scalable microservices architecture.
Database migration is perhaps one of the most technically demanding parts of cloud transformation. AI-assisted tools are simplifying this challenge as well, with advanced migration assistants analysing database structures and resolving compatibility issues when moving between different environments.
By analysing workload patterns, peak usage and resource consumption, AI tools can recommend the correct sizing of compute resources, databases and storage.
This ensures organisations achieve the right balance between performance and cost efficiency once they are operating in the cloud.
These capabilities are particularly valuable when paired with managed services. While AI accelerates analysis and automation, experienced cloud partners remain essential to guiding the overall strategy.
Managed services providers also help organisations interpret the insights produced by AI tools and translate them into sustainable operational environments – migration recommendations generated by AI agents are reviewed and validated by senior architects who ensure that the proposed design aligns with the organisation’s broader technology objectives.
The result is a migration process that is faster, more predictable and significantly less prone to human error. For organisations navigating the complexity of digital transformation, this combination of AI-driven automation and managed service expertise changes the story, turning cloud migration into a strategic opportunity to modernise infrastructure and build a foundation for the next generation of intelligent business systems.#
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