Advanced Computing in the Age of AI|Wednesday, April 8, 2020
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Is It ‘Do-or-Die’ for AI-Driven Digital Innovation? 

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A wholesale shift is underway to agile IT microservices as enterprises look to stay a step ahead of emerging business models along with the distributed applications and services that are driving a broad-based digital transformation.

A key to that transition is adoption of AI technologies and the deployment of machine learning tools in the workplace. That combination has spawned a closer look at how those technologies can be quickly deployed across enterprises while adhering to security and ethical standards.

A pair of studies released this week seek to shed more light on the consequences of failing to keep pace with IT innovation and how technologies like machine learning will shape the workplace of the not-so-distant future. Those structural changes including automated decision-making and other analytics tools are expected to redefine the workplace while raising a host of ethical issues surrounding data usage and privacy, according to new study released Wednesday (Dec. 11) by business consultant KPMG.

A separate study released by IT services platform specialist Kong Inc. predicts that many companies have a roughly three-year window to make the strategic transition to microservices and other agile IT tools. The alternatives, the vendor argues, are going out of business or being acquired by a more agile competitor.

“U.S. companies are facing a do-or-die moment when it comes to digital innovation, and most have a few short years to get it right before they fall too far behind to remain viable,” argued Augusto Marietti, Kong’s CEO and co-founder.

“The shift to microservices is inevitable and already underway among most companies,” Marietti continued. Companies “are waking up to the fact that they need tools that make it easier to manage and secure distributed applications across old and new software architectures and platforms so they can focus on more strategic initiatives.”

Open source technologies are increasingly seen as the quickest path to transformation, and the Kong survey found that public companies have emerged as the biggest users of distributed IT architectures and microservices such as application containers, databases and container orchestration. Among the incentives are flexibility and accelerated DevOps.

Beyond cost, the top reason for embracing microservices was security, cited by 56 percent of those polled in the Kong survey. That sentiment highlights growing awareness for the need to button down popular frameworks like the Kubernetes cluster orchestrator as more workloads move to production.

Still, the Kong survey revealed a range of challenges associated with deploying microservices. While improved security was cited as a key incentive for adoption, a plurality of respondents also said they remain concerned about the runtime security of cloud-based applications. The other leading challenge was the difficulty of integrating microservices with monolithic infrastructure. Among the concerns is preserving investments in legacy platforms.

Emerging tools for transforming digital business models include machine learning frameworks that will play a wider role in a range of business processes. The KPMG study emphasizes a code of ethics for AI deployment built around new policies for human-machine interactions in areas like data analytics and automated decision-making. The guidelines must include standards for data privacy and security.

Another pillar of responsible deployment is developing secure algorithms with a “strong ethical compass,” the AI study concludes. “When creating algorithms to deploy AI responsibly, security and governance of the data is crucial to the overall integrity of the model, as well as establishing clear lines of ownership to generate accountability.”

Getting all those moving parts to mesh appears to be among the biggest challenges for digital enterprises in the new decade, industry observers note. “The first part of the problem, where do you want to go with your digital transformation, is neither easy nor intuitive to answer,” said Tony Saldanha, a consultant and former manager of IT operations for Proctor & Gamble (NYSE: PG). “It seems to be a moving target.”

Saldanha reckons that 70 percent of digital transformation efforts fail. Part of the reason is “there’s way too much emphasis being placed on ‘digital’ and not enough on ‘transformation’,” he adds.

In many cases companies are placing the cart before the horse: Applying machine learning and other AI tools constitutes business process automation, but not full-blown transformation, Saldanha argued.

Adoption of microservices and automation tools remains several steps from what the IT consultant considers “an enterprise-wide digital platform or new business model [that] has fully taken root.”

The ultimate goal is where a digital “transformation has become perpetual” and “you’re a disciplined innovator,” Saldanha said.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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