Everpure and WWT Explain Real AI Infrastructure Needs in 2026

70% of AI projects fail due to poor data quality, highlighting the need for clean, governed data in AI infrastructure, according to Everpure and WWT.

Introduction to AI Infrastructure

The recent partnership between Everpure and WWT aims to provide data-ready AI infrastructure to enterprises, emphasizing the importance of clean, governed, and well-understood data for successful AI deployment. This shift in focus is redefining the role of partners in the industry, as noted by Hope Galley, Vice President of Americas partner sales at Everpure.

Current Challenges in AI Adoption

• 56% of companies struggle with data quality issues, which can lead to 40% of AI projects being abandoned.
• 30% of enterprises face significant challenges in integrating AI into their existing infrastructure.

"The first wave of enterprise AI concern was straightforward. It was simply employees pasting sensitive data into public AI tools. Security teams responded with usage policies, domain blocks, and data loss prevention rules. That response made sense at the time. It doesn't fit the problem anymore." - The Hacker News

What the Sceptics Say

Some critics argue that the focus on data-ready AI infrastructure might be overemphasized, given the existing security measures in place. They suggest that Shadow AI's real threat is access control, and that the industry should focus on developing more robust access control mechanisms rather than solely relying on data quality.

What This Means for the Industry

Companies like Hyundai, which recently acquired Boston Dynamics, will likely prioritize data-ready AI infrastructure in their future projects. Meanwhile, countries like Norway are imposing restrictions on AI use in elementary schools, indicating a growing need for responsible AI development. Over the next 6-12 months, we can expect to see a significant increase in investment in AI infrastructure, with a focus on data governance and security.

Key Takeaways

1. Engineers: Prioritize data quality and governance when developing AI infrastructure to ensure successful deployment.
2. Investors: Consider investing in companies that focus on data-ready AI infrastructure, such as Everpure and WWT.
3. Business Leaders: Develop strategies for integrating AI into existing infrastructure, with a focus on data governance and security.
4. Consumers: Be aware of the potential risks associated with AI adoption, including data breaches and access control issues.

Sources

• SiliconANGLE: Everpure and WWT say data-ready AI infrastructure starts with clean, governed data
• The Hacker News: Forget Data Leakage: Shadow AI's Real Threat Is Access Control
• The Hacker News: Salesforce Disables Klue App Integration After OAuth Token Abuse Exposes Customer Data
• BleepingComputer: Nintendo confirms data stolen in WebMD subsidiary cyberattack