Accelerating AI innovation through application modernization

However, achieving measurable business value from AI-driven applications requires a new game plan. Traditional application architecture simply cannot meet the high demands of AI-enhanced applications. Instead, now is the time for organizations to modernize their infrastructure, processes, and application architecture using cloud-native technologies to stay competitive.

It’s time to modernize

Today’s organizations live in an era of geopolitical change, increasing competition, supply chain disruptions and changing consumer preferences. AI applications can help by supporting innovation, but only if they can flexibly scale when needed. Fortunately, through application modernization, organizations can achieve the agile development, scalability, and fast computing performance they need to support rapid innovation and accelerate the delivery of AI applications. David Harmon, director of software development at AMD, said the company “really wants to make sure they can migrate their current [environment] And take advantage of all hardware changes where possible. The result is not only a shortened overall development life cycle for new applications, but also the ability to respond quickly to changing world circumstances.

In addition to quickly building and deploying smart applications, modernizing applications, data and infrastructure can significantly improve customer experience. For example, consider Colesis an Australian supermarket that invests in modernization and uses data and artificial intelligence to deliver a dynamic e-commerce experience to its online and in-store customers. With Azure DevOps, Coles has moved application deployment from monthly to weekly, while shaving hours off build time. What’s more, by aggregating customer feedback from multiple channels, Coles is able to provide a more personalized customer experience. In fact, according to a 2024 CMSWire Insights Reportthe use of artificial intelligence in digital customer experience toolsets has increased significantly, with 55% of organizations now using it to some extent, and more starting to use it.

But even the most carefully designed applications are vulnerable to cybersecurity attacks. Given the opportunity, bad actors can extract sensitive information from machine learning models or maliciously inject corrupted data into AI systems. “AI applications are now interacting with your core organizational data,” Surendran said. “Having the right guardrails is important to keeping your data secure, and being built on a platform that allows you to do that.” The good news is that modern cloud-based architectures can provide strong security, data governance, content security, and more AI guardrails to protect AI applications from security threats and ensure compliance with industry standards.

The answer to AI innovation

From demanding customers to malicious hackers, new challenges require new approaches to application modernization. “You have to have the right underlying application architecture to keep up with the market and bring applications to market faster,” Surendran said. “Not having that foundation slows you down.”

Enter cloud-native architecture. As organizations increasingly adopt artificial intelligence to accelerate innovation and stay competitive, it becomes increasingly urgent to rethink how applications are built and deployed in the cloud. By embracing cloud-native architecture, Linux, and open source software, organizations can better drive AI adoption and create flexible platforms built for AI and optimized for the cloud. Harmon explains that open source software creates options, and “the entire open source ecosystem thrives because of it. It makes new technologies work.”

Application modernization also ensures optimal performance, scale, and security for AI applications. That’s because modernization isn’t just about lifting and moving application workloads to cloud virtual machines. Instead, cloud-native architecture is essentially designed to provide developers with:

• Flexibly scale to meet changing needs
• Better access to the data needed to power smart applications
• Access the right tools and services to easily build and deploy smart applications
• Embed into applications to protect the security of sensitive data

Together, these cloud capabilities ensure organizations get the most value from their AI applications. “At the end of the day, it’s all about performance and safety,” Harmon said. The cloud is no exception.