Enterprise leaders are increasingly feeling the urgency for AI transformation as they grapple with unprecedented economic changes and heightened competitive pressures. According to a report by Accenture, technical debt in the U.S. has surged to $2.41 trillion annually, with $1.52 trillion needed for remediation. Organizations that leverage intelligent agents to address these challenges not only stand to cut costs but also to gain competitive advantages that could reshape entire industries.
In my decade-long tenure at AWS, I have witnessed a profound shift in how enterprise customers approach AI. Businesses are now more aware that adopting AI can enhance time-to-market, business intelligence, and innovation speed. However, the initial step for unlocking these benefits often involves migrating mission-critical applications and data to the cloud.
This necessity led to the development of AWS Transform, which the company touts as the world’s first agentic AI service designed specifically for enterprise transformation. Unlike traditional migration tools, AWS Transform fundamentally redefines modernizing enterprises to meet today’s competitive demands.
The urgency for effective modernization is exemplified by a recent engagement with a large enterprise customer facing a significant crisis. With 6,000 virtual machines running critical applications and an impending expiration of their VMware licenses, the company needed more than just a simple server migration; they required the migration of complex networking configurations and long-established security policies. Such migrations are often tedious and prone to errors, resulting in project delays.
A Revolutionary Three-Layer Approach
AWS Transform distinguishes itself by operating on three interconnected layers that facilitate a more streamlined transformation process. The first layer consists of specialized AI agents that are not generic scripts but rather digital specialists trained to handle specific transformation challenges. These agents can modernize legacy programming languages like COBOL into contemporary languages like Java, facilitate the migration of complex data center configurations from VMware to AWS, and update .NET applications to current frameworks.
The second layer features an intelligent coordination system that manages workflows, resulting in a significant acceleration of the transformation process. This system autonomously manages complex workflows, deciding which expert agents to deploy, when to execute processes in parallel, and how to manage dependencies. For instance, in the case of the enterprise customer with 6,000 VMs, the agentic AI simultaneously orchestrated various specialized agents, completing tasks that typically would take two weeks of manual work in just eight hours.
The third layer integrates natural language interfaces that facilitate collaboration between technical and business teams. Migration projects inherently involve multiple stakeholders, and these natural language interfaces translate technical complexities into easily understandable business terms, promoting real-time collaboration among architects, developers, and business leaders.
The results of this new approach have been striking. Projects that once took 12 to 18 months are now completed in mere weeks. Customers report transformation speeds up to five times faster than traditional methods. In VMware migrations specifically, AWS Transform has achieved acceleration rates of up to 80 times by running multiple tasks in parallel. These metrics translate into profound operational efficiencies for businesses.
For example, global data and technology company Experian modernized seven internal applications running on outdated .NET frameworks using AWS Transform. The project allowed them to reduce developer effort by approximately 40%, translating to a savings of around 300 engineering days. Principal Director of Technology & Software Engineering, Anup Pancholi, highlighted that this effort supported their goal of embedding agentic AI and automation within their teams.
Another compelling case comes from CSL, a global biotechnology leader, which accelerated its planning process across 5,000 servers in 29 data centers by a factor of ten, reducing application discovery time from hours to just five minutes. Mark Hill, their Chief Digital Information Officer, remarked that modernizing legacy applications enhances their capability to build AI solutions and expedite the delivery of life-saving medicines.
As organizations increasingly recognize that migration and modernization are catalysts for business transformation rather than endpoints, they are establishing long-term competitive advantages. My vision for AWS Transform is to create a continuous modernization process, adapting to evolving security requirements and business needs without disruption.
Success stories across various industries illustrate the transformative potential of this technology. BMW Group, for instance, reduced testing time by 75% while increasing test coverage by 60% in mainframe modernization efforts, significantly lowering risk. Similarly, Air Canada upgraded thousands of Lambda functions using AWS Transform, achieving a remarkable 90% efficacy rate and an 80% reduction in time and costs.
As organizations grapple with a staggering $2.41 trillion in technical debt, the window for seizing these transformative opportunities is rapidly closing. Businesses that implement agentic AI now can gain operational advantages, not only reducing costs but fundamentally transforming their operational frameworks. The benefits of AWS Transform are evident in the results from companies like Experian and CSL, reinforcing the case for swift action in modernizing technology stacks. The call to action is clear: organizations must act decisively to remain competitive and innovative in an increasingly complex landscape.
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