Yet realizing measurable business value from AI-based applications requires a new game plan. Legacy application architectures simply cannot meet the high demands of AI-enhanced applications. Rather, now is the time for organizations to modernize their infrastructure, processes and application architectures using cloud-native technologies to remain competitive.
Now is the time to upgrade
Today’s organizations exist in a time of geopolitical change, increasing competition, supply chain disruption and evolving consumer preferences. AI applications can help by driving 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 computing power needed to support rapid innovation and accelerate the delivery of AI applications. David Harmon, director of software development for AMD, says that companies “really want to make sure that they can migrate their current (environments) and take advantage of all the hardware changes as much as possible.” The result is not only a reduction in the overall development life cycle of new applications, but also a rapid response to changing world conditions.
In addition to rapidly building and deploying intelligent applications, modernizing applications, data and infrastructure can significantly improve the customer experience. Take Coles, an Australian supermarket that has invested in modernization and uses data and artificial intelligence to deliver dynamic e-commerce experiences to its customers online and in-store. With Azure DevOps, Coles moved from monthly to weekly application deployments while reducing build times by hours. What’s more, by aggregating customer views from different channels, Coles was able to provide a more personalized customer experience. In fact, according to the CMSWire Insights 2024 report, there has been a significant increase in the use of AI across the digital customer experience toolkit, with 55% of organizations now using it to some degree and more starting their journey.
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 populate AI systems with corrupt data. “AI applications are now interacting with your core organizational data,” says Surendran. “Having the right guardrails is important to make sure that data is secure and built on a platform that allows you to do that.” The good news is that modern cloud architectures can provide robust security, data management, and AI guardrails such as content security that protect AI applications from security threats and ensure compliance with industry standards.
The answer to AI innovation
New challenges, from demanding customers to malicious hackers, require a new approach to application modernization. “You need to have the right underlying application architecture to be able to keep up with the market and bring applications to market faster,” says Surendran. “Not having that foundation can slow you down.
Enter the cloud-native architecture. As organizations increasingly embrace artificial intelligence to accelerate innovation and remain competitive, it is increasingly urgent 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, cloud-optimized AI platform. Harmon explains that open source software creates possibilities: “And the whole open source ecosystem just thrives on that. It allows new technologies to come into play.”
Application modernization also ensures optimal performance, scale and security of AI applications. That’s because modernization goes beyond simply lifting and moving application workloads to cloud virtual machines. Rather, cloud-native architecture is inherently designed to provide developers with the following features:
- Flexibility to scale to meet evolving needs
- Better access to the data needed to drive intelligent applications
- Access the right tools and services to easily build and deploy intelligent applications
- Security built into the application to protect 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 security,” says Harmon. The cloud is no exception.