Artificial intelligence in 2025 is no longer an emerging trend—it’s a business imperative. According to Hitachi Vantara’s State of Data Infrastructure Global Report 2024, AI adoption is widespread, with 76% of organizations rolling out AI for critical or widespread use cases.
However, as AI reshapes industries, it also places unprecedented demands on data infrastructure. Based on insights from 1,200 IT leaders across 15 countries, this new report reveals the challenges and strategies that define the future of AI-driven data management.
AI: A Game-Changer with Complex Challenges
AI’s integration into business processes is growing rapidly. Nearly four in ten organizations (37%) now consider AI critical to their operations. Despite this enthusiasm, AI implementation faces significant hurdles. While many IT leaders report successful AI outcomes, only 42% trust AI outputs more than half of the time, and 21% encounter inaccurate results.
The gap between ambition and execution often stems from data quality issues. High-quality data is essential for effective AI, yet only 38% of organizations prioritize improving training datasets. Additionally, 24% of IT leaders fail to review their datasets for accuracy, and 37% neglect to tag data, contributing to the widespread presence of “dark data.”
Skyrocketing Storage Needs
Data storage is becoming a critical bottleneck as AI adoption accelerates, according to the report. The average large organization currently manages 150 petabytes (Pb) of data, a figure expected to exceed 300Pb by 2026. This growth is not just about volume—unstructured data now accounts for over half of what’s stored, increasing complexity and management challenges.
These demands strain traditional data infrastructure, requiring businesses to rethink their approach. Proactive investment is essential, with IT leaders predicting a whopping 226% increase in AI-related storage investments over the next two years.
Balancing Speed, Security, and Sustainability
AI’s rapid advancement has introduced new concerns around security, sustainability, and ROI. IT leaders are wary of AI’s potential misuse and vulnerabilities. Protecting data from external threats and ensuring compliance with regulations like GDPR are top priorities.
Sustainability also remains a pressing issue. The high energy consumption of AI training models is often overlooked, even as organizations grapple with mounting costs. While speed and cost optimization are currently prioritized over sustainability, the long-term implications of ignoring environmental factors could be significant.
The Road Ahead: Building a Stronger Foundation
For IT solution providers, the report offers clear takeaways. The success of AI hinges on laying a robust data foundation. This means addressing data quality gaps, investing in scalable storage solutions, and implementing security measures that can keep pace with AI-driven innovation.
Organizations must also focus on monitoring and improving AI model accuracy. Real-time monitoring, feedback loops, and continuous learning are critical for achieving reliable results. Despite current challenges, the rewards of well-implemented AI are undeniable.
Take the Lead
While AI is transforming how businesses operate, success depends on strategic decisions about data infrastructure. Solution providers who want to lead the way should be ready to help organizations build scalable, sustainable, and secure data infrastructure. Trustworthy data is the key to driving AI’s transformative power.
Download the full report from Hitachi Vantara.
Image Credit: Hitachi Vantara