Yet realizing measurable business value from AI-powered applications requires a brand new game plan. Legacy application architectures simply aren’t able to meeting the high demands of AI-enhanced applications. Relatively, the time is now for organizations to modernize their infrastructure, processes, and application architectures using cloud native technologies to remain 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 may help by supporting innovation, but only in the event that they have the pliability 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 speed up the delivery of AI applications. David Harmon, director of software development for AMD says corporations, “actually need to be certain that that they’ll migrate their current [environment] and benefit from all of the hardware changes as much as possible.” The result will not be only a discount in the general development lifecycle of recent applications but a speedy response to changing world circumstances.
Beyond constructing and deploying intelligent apps quickly, modernizing applications, data, and infrastructure can significantly improve customer experience. Consider, for instance, Coles, an Australian supermarket that invested in modernization and is using data and AI to deliver dynamic e-commerce experiences to its customers each online and in-store. With Azure DevOps, Coles has shifted from monthly to weekly deployments of applications while, at the identical time, reducing construct times by hours. What’s more, by aggregating views of consumers across multiple channels, Coles has been in a position to deliver more personalized customer experiences. In actual fact, in response to a 2024 CMSWire Insights report, there’s a major rise in the usage of AI across the digital customer experience toolset, with 55% of organizations now using it to some extent, and more starting their journey.
But even essentially the most rigorously designed applications are vulnerable to cybersecurity attacks. If given the chance, bad actors can extract sensitive information from machine learning models or maliciously infuse AI systems with corrupt data. “AI applications are actually interacting along with your core organizational data,” says Surendran. “Having the suitable guard rails is essential to be certain that the info is secure and built on a platform that lets you do this.” The excellent news is modern cloud based architectures can deliver robust security, data governance, and AI guardrails like content safety to guard AI applications from security threats and ensure compliance with industry standards.
The reply to AI innovation
Latest challenges, from demanding customers to ill-intentioned hackers, call for a brand new approach to modernizing applications. “You will have to have the suitable underlying application architecture to have the option to maintain up with the market and produce applications faster to market,” says Surendran. “Not having that foundation can slow you down.”
Enter cloud native architecture. As organizations increasingly adopt AI to speed up innovation and stay competitive, there’s a growing urgency to rethink how applications are built and deployed within the cloud. By adopting cloud native architectures, Linux, and open source software, organizations can higher facilitate AI adoption and create a versatile platform purpose built for AI and optimized for the cloud. Harmon explains that open source software creates options, “And the general open source ecosystem just thrives on that. It allows latest technologies to return 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. Relatively, a cloud native architecture is inherently designed to offer developers with the next features:
- The flexibleness to scale to satisfy evolving needs
- Higher access to the info needed to drive intelligent apps
- Access to the suitable tools and services to construct and deploy intelligent applications easily
- Security embedded into an application to guard sensitive data
Together, these cloud capabilities ensure organizations derive the best value from their AI applications. “At the tip of the day, all the pieces is about performance and security,” says Harmon. Cloud isn’t any exception.
