Yet realizing measurable business value from AI-powered applications requires a new game plan. Legacy application architectures simply aren’t capable of meeting the high demands of AI-enhanced applications. Rather, the time is now for organizations to modernize their infrastructure, processes, and application architectures using cloud native technologies to stay competitive.
The time is now for modernization
Today’s organizations exist in an era of geopolitical shifts, growing competition, supply chain disruptions, and evolving consumer preferences. AI applications can help by supporting innovation, but only if they have the flexibility to scale when needed. Fortunately, by modernizing applications, organizations can achieve the agile development, scalability, and fast compute performance needed to support rapid innovation and accelerate the delivery of AI applications. David Harmon, director of software development for AMD says companies, “really want to make sure that they can migrate their current [environment] and take advantage of all the hardware changes as much as possible.” The result is not only a reduction in the overall development lifecycle of new applications but a speedy response to changing world circumstances.
Beyond building and deploying intelligent apps quickly, modernizing applications, data, and infrastructure can significantly improve customer experience. Consider, for example, Coles, an Australian supermarket that invested in modernization and is using data and AI to deliver dynamic e-commerce experiences to its customers both online and in-store. With Azure DevOps, Coles has shifted from monthly to weekly deployments of applications while, at the same time, reducing build times by hours. What’s more, by aggregating views of customers across multiple channels, Coles has been able to deliver more personalized customer experiences. In fact, according to a 2024 CMSWire Insights report, there is a significant rise in the use of AI across the digital customer experience toolset, with 55% of organizations now using it to some degree, and more beginning their journey.
But even the most carefully designed applications are vulnerable to cybersecurity attacks. If given the opportunity, bad actors can extract sensitive information from machine learning models or maliciously infuse AI systems with corrupt data. “AI applications are now interacting with your core organizational data,” says Surendran. “Having the right guard rails is important to make sure the data is secure and built on a platform that enables you to do that.” The good news is modern cloud based architectures can deliver robust security, data governance, and AI guardrails like content safety to protect AI applications from security threats and ensure compliance with industry standards.
The answer to AI innovation
New challenges, from demanding customers to ill-intentioned hackers, call for a new approach to modernizing applications. “You have to have the right underlying application architecture to be able to keep up with the market and bring applications faster to market,” says Surendran. “Not having that foundation can slow you down.”
Enter cloud native architecture. As organizations increasingly adopt AI to accelerate innovation and stay competitive, there is a growing urgency to rethink how applications are built and deployed in the cloud. By adopting cloud native architectures, Linux, and open source software, organizations can better facilitate AI adoption and create a flexible platform purpose built for AI and optimized for the cloud. Harmon explains that open source software creates options, “And the overall open source ecosystem just thrives on that. It allows new technologies to come into play.”
Application modernization also ensures optimal performance, scale, and security for AI applications. That’s because modernization goes beyond just lifting and shifting application workloads to cloud virtual machines. Rather, a cloud native architecture is inherently designed to provide developers with the following features:
- The flexibility to scale to meet evolving needs
- Better access to the data needed to drive intelligent apps
- Access to the right tools and services to build and deploy intelligent applications easily
- Security embedded into an application to protect sensitive data
Together, these cloud capabilities ensure organizations derive the greatest value from their AI applications. “At the end of the day, everything is about performance and security,” says Harmon. Cloud is no exception.
