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Business Transformation

How Artificial Intelligence Enables CIOs to Transform the Business

No longer reserved for leading-edge innovators and risk takers, artificial intelligence is now within reach of mainstream enterprise organizations—adding to the long list of technology implementation and strategy challenges modern CIOs face. With a growing appetite for AI-driven data, the need to cull it into actionable insights across all lines of business cannot be ignored. For instance, AI-assisted resume vetting enables HR departments to be more productive—and a variety of AI tools similarly drive productivity for marketing and sales, cybersecurity, operations, customer service, and product development. All of which leaves CIOs to address culture and organizational shifts within IT tools and initiatives that close education gaps and support companywide AI-driven innovation.

Artificial Intelligence: The Data Conundrum

Companies participating in the app economy have access to a virtual sea of incoming data. This is true whether developers are creating apps in-house or companies are using third-party apps to conduct business. However, the value of that data is often hidden due to challenges in storing, sorting, evaluating, understanding, communicating, and acting upon it in a strategic, cost-effective way. With mounting complexity in data sets, hiring talent to cull information into actionable insights is no longer viable from a cost and productivity perspective, especially when speed of innovation matters. Forward-thinking IT leaders recognize this challenge as an opportunity for AI to assist—and most C-suite leaders agree.  

AI Strategy Optimization: Addressing Common Challenges

The heart of IT transformation is data understanding1 and AI continues to play a critical role. That said, as the rate of AI-driven innovation accelerates and CIOs accommodate strategies, several key points are worth pondering:

  • AI applications can demand special data-centric considerations that some traditional CPU-based servers and commodity networking can’t handle. Therefore, AI apps are typically cloud-native with multiple microservices and complex workflows, which complicates data center management and security. Without integration, silos lack proper IT standards for visibility.
  • To avoid large investments and the need for specialized staff to build an AI platform in-house, companies are partnering to build production environments on third-party platforms. Doing so enables evolution through scalable AI solutions that can be folded into existing infrastructure.
  • Leveraging an infrastructure built on virtual machines enables scalability and pooling and aggregating resources based on AI processing demands—ultimately allowing technologists to bring AI to the data, rather than the other way around.
  • Security can make or break an AI strategy. Implementation teams should include experts from IT security and privacy teams to ensure successful security measure protect all lines of business.

Integrating AI processes with mainstream workloads across virtualized systems helps allocate resources, access data faster, and better manage AI applications. To learn more about these and other strategic considerations download:

1. VMware. “CIO Essential Guidance AI For the Enterprise.” 2022.