Imagine a world-renowned logistic company with a history of groundbreaking achievements. Despite this legacy, the company faces challenges in digital transformation. Legacy systems, once the operational backbone, now pose barriers to cost-effectiveness, centralized control, and scalability. This reflects a universal dilemma: balancing technological advancement with the perceived risks of change. Consequently, modernizing legacy applications has shifted from a luxury to a necessity for maintaining competitiveness.

However, the path to modernization is far from straightforward.Organizations frequently face resistance to adopting new technologies, accentuated by security concerns, slow learning curve and inadequate infrastructure.

Artificial Intelligence (AI) is the catalyst for overcoming application modernization challenges, driving efficiency and innovation through secure, collaborative platforms. 

As per studies, 67% of leaders feel that  AI will fundamentally change the nature of their business over the two-year horizon. 

By integrating AI-powered automation and analytics, businesses can build resilient systems that anticipate future needs and facilitate data-driven decision-making.

This blog post will explore the challenges, opportunities, and best practices in modernizing legacy applications for AI initiatives, with a focus on how Google Cloud solutions can pave the way for this transformative journey.

Application modernization is the process of updating existing legacy applications to new computing approaches, including new languages, frameworks, and infrastructure platforms. In the context of AI, it goes beyond mere technical updates – it’s about reimagining applications to leverage the power of AI and cloud technologies.

The primary drivers for modernization in the AI era include:

• Scalability: AI applications often require processing vast amounts of data in real-time. Legacy systems may struggle to handle this load, whereas modernized applications can scale dynamically in the cloud. 
• Speed to Market: In today’s fast-paced business environment, the ability to quickly develop and deploy new features is crucial. Modernized applications facilitate rapid development and deployment cycles.
• AI Integration: Legacy systems often lack the architecture to seamlessly integrate AI capabilities. Modernization creates a foundation that’s AI-ready, allowing for the incorporation of machine learning models and AI-driven insights.

According to recent trends highlighted by Google Cloud, organizations that successfully modernize their applications see an average of 14% revenue growth and 13% increase in customer satisfaction. These numbers underscore the tangible benefits of embarking on a modernization journey.

While the benefits of modernization are clear, the path is not without its obstacles. Organizations face several challenges when updating their legacy systems for AI integration:

1. Knowledge Gap and Resistance to Change

One of the primary obstacles in modernizing legacy applications for AI is the significant knowledge gap within organizations. Many IT teams lack the specialized expertise required to implement and manage AI technologies effectively. This gap often leads to a natural resistance to change, as team members may feel overwhelmed or threatened by the prospect of adopting new, complex systems. 

As per studies, 41% of executives say that workforce issues, such as training, culture, or change in work are among the top-five challenges their organizations face in using AI.

To address this challenge, organizations need to invest in comprehensive training programs and potentially bring in AI specialists. Creating a culture of continuous learning and innovation can help ease the transition and reduce resistance among team members.

2. Security Concerns

As legacy applications integrate AI capabilities, they become more vulnerable to new security risks. Outdated systems often lack the robust security features necessary to protect against modern threats, especially when dealing with the vast amounts of data required for AI operations.

Organizations must prioritize security upgrades alongside AI integration. This involves implementing advanced encryption methods, enhancing access controls, and regularly updating security protocols to safeguard both the AI systems and the valuable data they process.

3. Integration Complexities

Integrating AI capabilities into existing workflows can be a complex endeavor, particularly when dealing with legacy systems that use proprietary data formats or outdated APIs. These integration challenges can lead to disruptions in business operations and may require significant re-engineering of existing processes.

To overcome this, organizations should consider adopting a phased approach to integration, starting with less critical systems. Utilizing modern integration tools and platforms can also help bridge the gap between legacy applications and new AI technologies, ensuring smoother transitions and minimal disruptions.

4. Data Quality and Accessibility

The effectiveness of AI algorithms heavily depends on the quality and accessibility of data. Legacy systems often store data in incompatible or siloed formats, making it difficult for AI systems to access and process the information effectively.

Tackling this challenge requires a comprehensive data strategy. This may involve data cleansing, standardization of data formats, and the implementation of data lakes or warehouses to centralize information. Ensuring data quality and accessibility not only benefits AI integration but also improves overall operational efficiency.

Modernizing legacy applications eliminates barriers to adoption, making systems more secure, efficient, and adaptable. By taking this step, organizations build a solid foundation for growth while reducing operational friction.

Stronger Security and Reliability: Legacy applications often struggle to meet today’s security requirements. A modernized platform strengthens security with automated threat detection, built-in compliance, and reliable safeguards—reducing the burden on teams while ensuring better protection.

To know more about how to build secure and scalable platforms with AI explore our ebook in detail here.

Improved User Experiences: Modern applications enhance performance, making systems more responsive and scalable. They create a seamless experience for both customers and internal teams, ensuring better engagement and higher satisfaction.

Faster, More Informed Decisions: By breaking down data silos, modernized applications provide better visibility into operations. Teams can access real-time insights, streamline workflows, and make smarter decisions without delays.

Greater Agility and Adoption : Cloud-ready, flexible applications allow organizations to move faster, experiment confidently, and scale seamlessly. A secure, adaptable platform removes friction, accelerates approvals, and simplifies adoption across teams.

By modernizing applications, organizations remove the complexities of outdated systems, improve security and efficiency, and create an environment where innovation happens faster and with less resistance and Future growth.

To successfully modernize legacy applications for AI initiatives, consider the following best practices:

1. Assessment and Planning

Begin with a thorough assessment of your current application landscape. Identify which applications are critical for AI integration and prioritize them based on potential business impact.

2. Choosing  the Right Modernization Strategy

There’s no one-size-fits-all approach to modernization. Options range from rehosting (lift-and-shift) to complete rebuilding. Choose the strategy that best aligns with your business goals and technical requirements.

3. Embracing Cloud-Native Technologies

Leverage cloud-native technologies like containers and microservices. These technologies provide the flexibility and scalability needed for AI-driven applications.

4. Prioritizing Data Management

Implement a robust data management strategy. Ensure that your data is clean, accessible, and structured in a way that’s conducive to AI algorithms.

5. Focus on Security from the Start

Incorporate security measures throughout the modernization process. Adopt a DevSecOps approach to ensure that security is built into every stage of development and deployment.

6. Workforce Empowerment

Provide training and resources to your team to bridge the knowledge gap. Foster a culture of continuous learning to keep pace with rapidly evolving AI technologies.

At 66degrees, we’ve revolutionized the modernization process through our unique visual approach. By leveraging diagrammatic representations, we collaborate effectively with clients and stakeholders, ensuring buy-in from key subject matter experts. This visual demonstration is the cornerstone of our strategy, illustrating our secure environment and AI enablement process.

Building on a Solid Foundation:Our modernization journey begins with establishing a robust foundation. We prioritize essential pillars such as network security, secure access controls, high availability, and data resilience. This comprehensive approach creates a secure and controlled environment for application development and AI enablement, safeguarding the confidentiality and integrity of your data.

Empowering Through Collaboration:We believe in the power of collaboration. Our subject matter experts work hand-in-hand with your stakeholders throughout the AI enablement process. This close partnership, coupled with our commitment to continuous training and knowledge transfer, empowers your team to eventually take ownership of AI solutions and develop their own visions based on company insights.

Accelerating Adoption with Innovative Approaches: To streamline adoption, we employ Infrastructure as Code (IaC) and simplified deployment pipelines. This approach ensures effortless implementation and scalability of AI solutions. We’ve also established streamlined, content-based approval processes that reduce bottlenecks and accelerate the adoption of AI technologies.

By creating a secure platform with repeatable deployment patterns, we enable developers to quickly deploy and test applications while adhering to strict security protocols. This innovative approach not only increases development throughput but also ensures that AI solutions can be developed without compromising on security.For more details on how we enabled app modernisation, explore our case study. 

At 66degrees, we empower organizations to make the right choices when it comes to cloud modernization and setting the stage for AI integration. Our strategic Google Cloud consulting services help align your technology needs with your business objectives, ensuring a robust, future-proof AI infrastructure. By making use of our deep expertise in cloud, data and AI engineering, we guide you through every step of your transformation journey. Connect with us to learn how we can help.