Artificial intelligence has become more than a transformation initiative. It is now a productivity catalyst with measurable returns when built on sound data and infrastructure foundations. According to IBM’s Institute for Business Value in the new report, The 2025 CDO Study: The AI Multiplier Effect, organizations that redesign their data practices for AI are realizing significantly stronger business outcomes. You can read the full study from IBM or download the complete PDF.
This aligns directly with what Jeskell sees across Federal and commercial environments. Many organizations want to scale AI initiatives, yet most are still working to unify data, modernize storage, and establish governance that allows AI to be secure, reliable and repeatable. These foundational capabilities are the true AI force multipliers because they determine whether AI models have the performance, accuracy and resilience required to produce meaningful ROI.
Building Data Confidence as the Foundation of AI Success
The report underscores that the most successful AI leaders share one major trait. They have strong confidence in their data. This confidence stems from consistent governance, clear data ownership and a reliable mechanism for discovering and classifying sensitive information.
IBM calls attention to the importance of a modern data governance framework that uses automation to maintain quality at scale. Solutions such as IBM Discover and Classify, often paired with IBM Guardium, help organizations understand where their sensitive data lives and how to apply consistent controls across hybrid cloud environments. As AI models rely on both structured and unstructured data, this visibility becomes a critical factor in producing trustworthy outcomes.
Jeskell helps clients strengthen this foundation by aligning data governance with practical infrastructure strategies, including storage tiering, immutability, archive planning and high throughput systems designed for AI data pipelines.
Modern Infrastructure as the Enabler of the AI Multiplier
One of the major insights in the 2025 CDO Study is that AI performance is increasingly tied to infrastructure readiness. The organizations reporting the highest return on AI investments have already modernized their storage and compute architecture to handle high volumes of data with speed and integrity.
Hybrid cloud storage and scalable file systems are essential for supporting model training, inference and continuous learning. IBM technologies like IBM Storage Scale, IBM Storage Ceph and high performance IBM FlashSystem arrays provide the throughput and resilience required for enterprise AI workloads. These platforms help eliminate data silos, streamline access and maintain end to end protection across the data lifecycle.
Jeskell’s 35 year history implementing IBM infrastructure gives our clients a significant advantage as they move from small pilot AI projects toward production environments. We help teams design storage architectures that keep pace with expanding datasets while maintaining security, governance and cost efficiency.
Turning Data into an AI Advantage Across the Lifecycle
The CDO study points out that organizations are shifting from viewing AI as a tool to viewing AI as an integrated capability that transforms the entire data lifecycle. Achieving this requires more than model development. It involves reshaping how data is ingested, secured, cataloged, stored, accessed and archived.
Jeskell works with clients to establish this lifecycle holistically so they can activate the AI multiplier effect with confidence. Whether supporting high performance environments for model training or delivering cyber resilient storage for long term data protection, our goal is to ensure every part of the data lifecycle strengthens AI outcomes.
AI is not just about algorithms. It is about infrastructure that eliminates fragmentation, enforces governance and accelerates decision making. The organizations that invest in these fundamentals today will be the ones that realize exponential value tomorrow.