Deloitte Managed Service Helps Build Cloud-Native AI Apps

Deloitte this week launched a managed service, based on a microservices architecture, that enables organizations to build cloud-native applications infused with artificial intelligence (AI).

Jagadish Bandla, national leader of Deloitte’s SAP S/4HANA analytics capability, says the AIOPS.D service will enable organizations to build and deploy microservices-based applications that take advantage of the CortexAI platform Deloitte launched in 2020 to help its clients infuse machine and deep learning algorithms into business processes. The goal is to make it possible to deploy a microservices-based application infused with AI capabilities in less than 90 days, notes Bandla.

Most AI applications today are based on microservices constructed using containers. The challenge organizations face is they often lack both the microservices and AI expertise required to build those applications quickly.

Pricing for the AIOPS.D service is subscription-based and requires a minimal upfront investment. Deloitte then works with individual organizations to define pricing for the application development initiative based on the business outcome.

It’s not clear to what degree these emerging microservices and AI technologies—that are challenging to master—will drive more organizations to consume IT-as-a-service. However, because the bulk of next-generation enterprise applications require some type of AI capability, it’s becoming increasingly difficult for internal IT teams to keep pace. This is especially difficult as the rate at which business leaders want to build and deploy the software to drive digital business transformation initiatives increases.

The rate at which organizations are embracing various forms of AI increased considerably in the aftermath of the COVID-19 pandemic. The need to automate processes within the context of a digital business transformation initiative became a higher priority. Since then, challenges in hiring and retaining staff during the Great Resignation has made AI-infused applications an even bigger imperative.

The issue that many organizations struggle with, however, is that most AI models are created by teams of data scientists that work at a much different pace than application developers. Aligning the delivery of AI models with the pace at which applications are developed within a DevOps workflow requires the blending of two very different IT cultures.

The most important thing, however, is to simply get started, says Bandla. Organizations that gain AI experience sooner rather than later will have a material advantage over organizations that are forced to try and catch up, he notes.

It’s not clear at what rate existing legacy applications might be replaced by a modern equivalent infused with algorithms that automate a process. However, there are very few new application development initiatives being launched today that don’t incorporate some type of AI model. Whether it’s a consumer or business application, the expectation is that algorithms, overall, provide a better end-user experience. The decision organizations need to make is determining to what degree they want to deploy and maintain the platforms required to build those applications themselves versus consuming them as a service provided by an external entity.

Mike Vizard

Mike Vizard is a seasoned IT journalist with over 25 years of experience. He also contributed to IT Business Edge, Channel Insider, Baseline and a variety of other IT titles. Previously, Vizard was the editorial director for Ziff-Davis Enterprise as well as Editor-in-Chief for CRN and InfoWorld.

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