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Concerns Mount Among IT Leaders Regarding Digital Infrastructure's Compatibility with AI


Today's technology advancements are brimming with potential and driving IT decision-makers to explore how best to harness anticipated benefits, promptly. One of those advancements is AI, led specifically by generative AI’s breakthrough since late last year. With said breakthroughs, IT decisionmakers plan to utilize AI across key business areas.

So, they are doing just that. According to an Equinix 2023 survey, 85% of respondents listed IT operations and 81% listed cybersecurity as the top business functions for which companies across the globe use or plan to use AI in the quest to remain at the forefront of technology.

Yet despite AI's promises, leaders have varying comfort levels with adopting it, along with other challenges affecting implementation. The reason for that, according to the survey, is that more than four in 10 IT leaders believe their existing IT infrastructure are not fully prepared for the demands of AI technology, despite its widespread adoption across industries.

AI applications require robust computational power, storage capabilities and network bandwidth to process large amounts of data in real-time, leading to uncertainties about whether the current infrastructure can meet these requirements.

The complexities of AI infrastructure also go beyond technical aspects, as seamless connectivity between data sources, algorithms and computing resources is altogether crucial for successful deployment, posing a challenge for IT leaders.

Additionally, the potential need for infrastructure upgrades or investments (such as hardware, software or cloud services) raises financial considerations that IT leaders must navigate while balancing budget constraints and the need to stay technologically advanced. (With sustainability in the mix too, of course.)

"Successful development of accurate AI models depends upon secure and high-speed access to both internal and external data sources that can be spread across multiple clouds and data brokers," said Kaladhar Voruganti, Senior Technologist at Equinix. "Furthermore, we are entering an era where more data is being generated at the edge. To create scalable AI solutions, businesses must consider whether their IT frameworks can accommodate the required data ingestion, sharing, storage and processing of massive and diverse data sets, while keeping sustainability in mind."

It makes sense that IT leaders are concerned with their existing IT infrastructure not being prepared for the demands of AI technology. That wasn’t the only concern they had, however.

The survey also revealed concerns IT leaders have when adopting new technologies:

  • The potential increase in operational expenditure costs is a worry for 46% of respondents, as they fear the financial burden associated with implementing and maintaining AI systems.
  • 42% of IT leaders express concerns about the lack of internal knowledge and expertise needed to effectively leverage AI, as the complexity of these systems outpaces organizational capabilities and results in a shortage of skilled professionals.
  • 37% of respondents worry about the slow implementation of new technologies (i.e. with integrating AI into existing infrastructure and workflows posing substantial challenges and causing delays in realizing the benefits).
  • 25% of IT leaders highlight the potential negative impact on reputation as a barrier to technology adoption. In turn, organizations are prioritizing caution due to increased public scrutiny surrounding data privacy, security breaches and ethical considerations that could undermine brand image and customer trust.

Technology leaders worldwide recognize AI as an essential capability for enabling intelligent and autonomous systems that drive modern businesses. With that said, organizations need to better prepare their infrastructure to be more comfortable in adopting the new tech. If not, they risk not fully leveraging AI’s potential, ultimately falling behind their counterparts.

Edited by Alex Passett
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