July 24, 2020 By Candice Zheng 2 min read

For any enterprise that wants to gain or keep competitive advantage today, AI is a transformational technology that cannot be ignored.

Artificial intelligence (AI) has the ability to augment human decision-making with speed and scale, drastically improving business operations and driving critical outcomes. IDC predicts that by 2025, AI-powered enterprises will see a one hundred percent increase in knowledge worker productivity, resulting in shorter reaction time, greater product innovation success, and improved customer satisfaction. Organizations worldwide are adopting AI as part of digital transformation journeys to drive agility, resiliency, innovation, and ability to scale.

Businesses today are using AI to accelerate and automate IT operations in complex modern data centers and cloud environments. According to IDC’s AI Strategies View study, the top enterprise use cases for AI solutions are IT automation, automated customer service agents, automated human resources, intelligent process automation, and IT threat intelligence and prevention. However, in order to integrate AI effectively, organizations need to establish a trusted and unified information architecture for their data.

Data is the fuel and foundation of today’s digital economy, and enterprises have seen an explosive growth in the volume and sprawl of data as they continue to modernize. As data creation and variety expands exponentially, organizations need to properly store, secure, and analyze data to make it trusted and ready for AI. A unified information architecture that fully integrates data and renders it effective and usable is a top priority for businesses that want to drive IT transformation.

Establishing a data-centric IT architecture that is ready for AI involves modernizing IT environments. Cloud computing provides vast benefits that drive agility, flexibility, scalability, and automation, and a hybrid multicloud provides further benefits that can elevate your business. Organizations can mitigate cost and risk by using data and applications running in multiple clouds. Security, compliance, and latency issues can also be addressed by utilizing different cloud data centers and a mix of public and private clouds. An integrated hybrid multicloud creates a unified information architecture that improves the portability and interoperability of applications and data.

Key considerations when building a hybrid multicloud

  • Modernize applications by leveraging containers, microservices, common APIs, orchestration tools (e.g., Kubernetes), and open source technologies (e.g., Linux, Red Hat OpenShift).
  • Standardize and automate processes.
  • Establish best practices and teams with new organizational structure for DevOps and MLOps.
  • Focus on portability, interoperability, and management.
  • Establish a single dashboard to monitor, provision, manage, and secure all clouds.
  • Incorporate public clouds, private clouds, and even legacy data centers.

With a successful hybrid multicloud deployment, organizations can achieve 360-degree visibility, effectively monitoring, managing, and securing all applications and data across cloud environments. Data can be accessed and analyzed from diverse sources, allowing for the application of AI to further drive deeper insights, faster deployment, and lower costs. 

To learn more about future-proofing your IT architecture and business with hybrid multicloud and AI, read this IDC infobrief.

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