AWS re:Invent 2025 - A leader's guide to cloud-native application modernization (SNR302)

Modernizing Legacy Applications: A Holistic Approach

Why Modernize?

• Most enterprises have a significant portion of their workloads (70%) and legacy software (70% of Fortune 500 companies) that are over 20 years old.
• This creates a major gap between what's possible with modern technology and what most companies are actually using.
• Key drivers for modernization include:
  • Improving customer experience, which degrades over time with legacy systems
  • Reducing operational costs of legacy systems
  • Attracting digital-native talent
  • Improving reliability, availability, and performance
  • Enabling AI-first business models and leveraging data for AI/ML
  • Transitioning to a cloud-first technology strategy

What to Modernize: The Cloud-Native Approach

• Modernization requires a comprehensive, holistic approach across multiple dimensions:
  • Architecture: Transitioning from monolithic to microservices-based
  • Technology stack: Selecting mature, stable, and widely adopted technologies
  • Ways of working: Shifting to iterative, customer-outcome focused, DevOps practices
  • Organization: Establishing cross-functional, autonomous "two-pizza" teams
  • Leadership: Embracing a VUCA (volatile, uncertain, complex, ambiguous) mindset
  • Features and functionality: Focusing on business value, not just feature parity

How to Modernize: A Structured, Adaptive Approach

Bootstrapping Phase

• Start with 2-5 teams: 1 platform team and 1-4 product teams
• Platform team sets up production-ready AWS environment, CI/CD, monitoring, security
• Product teams deliver first business components into production

Scaling Phase

• Use a "cell division" approach to scale teams in a structured, adaptive manner
• Split teams every quarter, balancing scaling, knowledge transfer, quality, and delivery
• Maintain a stable "baseline" team to track progress metrics

Prioritizing Features

• Categorize capabilities by technical risk and business value
• Start with high-risk, high-value features, then move to lower-risk, high-value
• Use Wardley mapping to visualize the current and future state of the value chain

Measuring Progress

• Focus on business outcomes (revenue, conversion rate, basket size) rather than just feature completion
• Track key metrics like training duration, team split cadence, external/internal ratio, and generative AI usage

The Role of Generative AI

• Generative AI can be a "force multiplier" in modernization efforts
• McKinsey found 50% reduction in timelines and 40% reduction in costs when using AI tools
• AWS Transform is a service that provides AI-powered agents to assist with modernization tasks

Benefits and Results

• The Hartford's modernization journey:
  • Improved application rewrite speed from 35-45 days to under 2 weeks
  • Increased modernization scores by nearly 2x over 3 years
  • Leveraged AWS Accelerate to the Cloud program to close data centers
• Key principles for successful modernization:
  1. Focus on outcomes, not outputs, and measure business value
  2. Embrace generative AI as an accelerant
  3. Transform the entire system - people, process, and technology

Call to Action

• Leverage resources like "The Future of Software" and "The Octopus Organization" books
• Engage with AWS to explore modernization acceleration programs like Experience-Based Accelerator and AWS Transform