Business applications powered by AI are revolutionizing customer experiences, accelerating the speed of business, and driving employee productivity. In fact, according to research firm Frost & Sullivan’s 2024 Global State of AI report, 89% of organizations believe AI and machine learning will help them grow revenue, boost operational efficiency, and improve customer experience.
Take for example, Vodafone. The telecommunications company is using a suite of Azure AI services, such as Azure OpenAI Service, to deliver real-time, hyper-personalized experiences across all of its customer touchpoints, including its digital chatbot TOBi. By leveraging AI to increase customer satisfaction, Naga Surendran, senior director of product marketing for Azure Application Services at Microsoft, says Vodafone has managed to resolve 70% of its first-stage inquiries through AI-powered digital channels. It has also boosted the productivity of support agents by providing them with access to AI capabilities that mirror those of Microsoft Copilot, an AI-powered productivity tool.
“The result is a 20-point increase in net promotor score,” he says. “These benefits are what’s driving AI infusion into every business process and application.”
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.
What’s more, Surendran notes that “when you leverage a cloud platform for modernization, organizations can gain access to AI models faster and get to market faster with building AI-powered applications. These are the factors driving the modernization journey.”
Best practices in play
For all the benefits of application modernization, there are steps organizations must take to ensure both technological and operational success. They are:
Train employees for speed. As modern infrastructure accelerates the development and deployment of AI-powered applications, developers must be prepared to work faster and smarter than ever. For this reason, Surendran warns, “Employees must be skilled in modern application development practices to support the digital business needs.” This includes developing expertise in working with loosely coupled microservices to build scalable and flexible application and AI integration.
Start with an assessment. Large enterprises are likely to have “hundreds of applications, if not thousands,” says Surendran. As a result, organizations must take the time to evaluate their application landscape before embarking on a modernization journey. “Starting with an assessment is super important,” continues Surendran. “Understanding, taking inventory of the different applications, which team is using what, and what this application is driving from a business process perspective is critical.”
Focus on quick wins. Modernization is a huge, long-term transformation in how companies build, deliver, and support applications. Most businesses are still learning and developing the right strategy to support innovation. For this reason, Surendran recommends focusing on quick wins while also working on a larger application estate transformation. “You have to show a return on investment for your organization and business leaders,” he says. For example, modernize some apps quickly with re-platforming and then infuse them with AI capabilities.
Partner up. “Modernization can be daunting,” says Surendran. Selecting the right strategy, process, and platform to support innovation is only the first step. Organizations must also “bring on the right set of partners to help them go through change management and the execution of this complex project.”
Address all layers of security. Organizations must be unrelenting when it comes to protecting their data. According to Surendran, this means adopting a multi-layer approach to security that includes: security by design, in which products and services are developed from the get-go with security in mind; security by default, in which protections exist at every layer and interaction where data exists; and security by ongoing operations, which means using the right tools and dashboards to govern applications throughout their lifecycle.
A look to the future
Most organizations are already aware of the need for application modernization. But with the arrival of AI comes the startling revelation that modernization efforts must be done right, and that AI applications must be built and deployed for greater business impact. Adopting a cloud native architecture can help by serving as a platform for enhanced performance, scalability, security, and ongoing innovation. “As soon as you modernize your infrastructure with a cloud platform, you have access to these rapid innovations in AI models,” says Surendran. “It’s about being able to continuously innovate with AI.”
Read more about how to accelerate app and data estate readiness for AI innovation with Microsoft Azure and AMD. Explore Linux on Azure.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.