Introducing AWS ‘Move to AI’ Modernization Pathway: Transforming Your Application Portfolio with AI

We are excited to announce the launch of ‘Move to AI’ Modernization Pathway. It provides organizations with a structured approach to identify and implement high-impact artificial intelligence (AI) opportunities within their application portfolio. This addition to our comprehensive suite of Modernization Pathways provides an effective way to unlock innovation, automate workflows, and enhance customer experiences.

Opportunities in the AI Modernization Journey

Organizations are discovering exciting opportunities to optimize with the use of AI across their application portfolios with data as the foundation for value creation. The growing accessibility of AI and machine learning (ML) technologies open new possibilities for innovation, while teams develop expertise across business and technology domains. Through careful planning and robust governance frameworks, organizations can integrate AI strategically into existing systems. Successful pilots are providing valuable insights for enterprise-wide scaling, positioning companies to capture the transformative potential of this rapidly evolving technology landscape.

While AI presents tremendous opportunities, our experience working with thousands of customers has shown that many initial AI pilots do not make it to production due to misalignment with business priorities. This underscores the importance of strategic AI adoption that focuses on concrete business outcomes rather than technology for technology’s sake.

Modernization pathways

AWS offers a suite of modernization pathways designed to simplify decision-making and accelerate the modernization journey. As illustrated in Figure 1, with the addition of the new ‘Move to AI’ pathway, AWS now provides seven powerful paths to help you drive and scale digital transformation. Let’s explore each pathway:

Figure 1: Modernization pathways

1. 1. Move to AI (New!): Adopt cutting-edge AI solutions to unlock innovation, automate processes, and transform applications. Services like Amazon Bedrock and Amazon SageMaker AI help you to enhance customer experiences, accelerate business outcomes with advanced analytics insights, and optimize business processes with automation.
   2. Move to Cloud Native: Break down monoliths into microservices, building agile and scalable architectures.
   3. Move to Containers: Modernize through containerization, using the fully managed Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS).
   4. Move to Managed Databases: Transition to fully managed Amazon Relational Database Service (Amazon RDS) and the portfolio of purpose-built databases on AWS.
   5. Move to Open Source: Shift existing .NET and other licensed workloads to open-source solutions, amplified with scalable infrastructure and managed services on AWS.
   6. Move to Modern Analytics: Embrace modern data lake initiatives, bringing data closer to advanced analytics capabilities using Analytics on AWS.
   7. Move to Modern DevOps: Integrate modern DevOps practices and automate pipeline deployment using the suite of developer tools from AWS.

Each pathway is designed to address specific modernization needs, helping you to align to the unique requirements of your organization. Adding the new ‘Move to AI pathway’ helps you to navigate and simplify your modernization journey with confidence and agility.

Unlocking Value Through ‘Move to AI’ Pathway

While many organizations recognize the transformative potential of AI, they often struggle to identify opportunities and find solutions that truly fit their unique needs. The ‘Move to AI’ pathway addresses these challenges by offering a graduated approach to AI adoption that leverages existing application investments. Rather than starting from a greenfield, this approach helps organizations identify AI opportunities within their current application portfolio, ensuring faster paths to production and stronger alignment with established business priorities and customer outcomes. This iterative approach helps businesses start with straightforward and business relevant use cases, and progressively builds toward more transformative AI capabilities tailored to their specific requirements.

The key aspects include:

1. AI Opportunity Mapping provides a systematic analysis of your application portfolio. This analysis helps to identify where AI can deliver significant value, both through direct integration and by preparing applications for future AI agent integrations.
2. Modernization Assessment helps identify and prioritize AI opportunities and develops a roadmap for implementation.
3. Staged Implementation Approach often begins with basic foundation model (FM) inference tasks using Amazon Bedrock, such as content generation and summarization. As teams gain confidence, they can progress to more complex implementations like using AI agents and custom knowledge bases.
4. Organizational Readiness and Governance establishes necessary oversight frameworks for compliance, policies, and operations to enable successful enterprise-wide AI adoption.

Success stories

1. Carrier Global attains 90% efficiency boost. Using generative AI built on Amazon Bedrock, Carrier created a solution to ingest utility data and combine it with historical trends. With this data foundation, they were able to use predictive analytics to provide insights that help their customers manage the energy consumption and reduce their carbon emissions. With the new solution, customers upload utility bills in their local languages, to generate actionable energy-saving insights. The team used Amazon Textract, and Amazon Bedrock with the Claude 3 foundation model to build the initial proof of concept. In this POC, the processing time reduced by 90% while achieving 100% accuracy across six languages.

2. PGA TOUR Enhanced Media Management with AI Solution. PGA TOUR’s Media team needed to improve their transcription accuracy for 215,000+ hours of golf content. The existing solution showed 12-15% inaccuracies in golf-specific terms, player names, and sponsor references. Following comprehensive training, proof of concept, and architecture reviews with AWS Specialists, they integrated Amazon Bedrock into their Amazon Transcribe workflows. The integration improved transcription accuracy, reducing data inaccuracies to only 2-5%. This created a foundation for scaling the solution to include translation features for international teams and to improve object/logo detection capabilities for enhanced monetization opportunities.

Getting started with ‘Move to AI’ Pathway

The Move to AI modernization pathway is now used as part of the AWS Modernization Experience-Based Acceleration (ModAx) program delivered by your AWS account team and AWS partners. Through immersive workshops, guided implementations, and collaborative sessions with AWS experts and industry peers, your teams will gain practical experience with proven patterns and practices. Contact your account team today to get started with ModAx and the Move to AI pathway to accelerate your organization’s modernization journey.

In addition to ModAx, beginning your AI modernization journey involves several key activities that can be pursued flexibly based on your organization’s needs and readiness. Contact your account team to start conducting an AI readiness assessment using the AWS AI Readiness Assessment Tool. This will help you understand your current position and identify potential AI use cases within your application portfolio. During this phase, evaluate data readiness and quality, existing AI/ML capabilities, and establish clear objectives and success metrics that align with your organizational goals.

As you progress, take advantage of the comprehensive learning resources from AWS to help increase your team’s modernization proficiency, including AI topics in the Move to AI modernization pathway. The AWS Modernization Ramp-Up Guide, AWS Skill Builder courses, and hands-on labs in AWS Workshop Studio provide structured learning paths. Focus on understanding AWS purpose built AI services to address your specific use cases. This would include Amazon Bedrock for generative AI, and Amazon SageMaker AI for machine learning development.

We recommend starting with manageable pilot projects that can demonstrate quick wins while building team confidence. Consider implementing ready-to-use AI assistants, deploying pre-built solutions from the AWS Solutions Library, or adding natural language capabilities to existing applications using Amazon Q Business. Start with focused implementations that deliver immediate business value. This could be using proven patterns for enhancing customer service with AI assistants or implementing intelligent search solutions with Retrieval Augmented Generation (RAG). Thereafter, you can scale successful patterns across your organization. This “start small, think big” approach helps you to validate your AI initiatives through quick wins while laying the foundation for more sophisticated implementations. These initial successes create momentum for broader adoption and help build organizational confidence in transformation pathways using AI.

To continue with sustainable growth, establish strong governance and responsible AI practices early in your journey. Integrate AI-specific security and compliance controls into your existing risk management framework, and extend your monitoring and observability practices to encompass new AI capabilities. Use your existing DevOps frameworks to create repeatable patterns for AI model deployment and continuous improvement. Leverage the AWS Well-Architected Framework including the Generative AI Lens.

Throughout your journey, AWS provides comprehensive support through our Solutions Architects and Professional Services teams. This includes AI Services Overview, AWS Skill Builder courses, Machine Learning Blogs, and Workshop Studio materials to help guide your transformation.

Embarking on your AI modernization journey with the AWS ‘Move to AI’ pathway opens up a world of possibilities for innovation and transformation. By following these structured steps—from initial assessment through skill development, hands-on experimentation, and foundation building—you’re setting your organization on a path to success. AI adoption is an iterative process that relies on continuous learning and adaptation. With a comprehensive set of resources from AWS, expert support, and the flexibility to start small while thinking big, you will be well-equipped to navigate AI-driven modernization.