Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Steve Wallo, CTO, Vcinity
The Reality of AI-Driven Environments
Over the last few years, artificial intelligence (AI) has
made significant strides, moving from conceptual and experimental phases to business-critical
roles in sectors ranging from healthcare to finance to manufacturing.
Generative AI continues to dominate headlines, empowering organizations to
unlock new levels of efficiency, innovation, and competitiveness. From
revolutionizing supply chains to reshaping customer experiences, AI has brought
a new era of possibilities. Yet, beneath this lies a persistent challenge: data
access. As AI models grow more complex and the infrastructure to support them
becomes increasingly distributed, organizations face great difficulty in
feeding their AI systems with the right data at the right time.
AI’s Growing Appetite
AI models are only as good as the data that feeds them. For
large language models (LLMs) and other advanced systems to thrive, they require
a steady diet of fresh, diverse, and relevant data. This is no small task. In
today’s digital landscape, data is generated everywhere-across hybrid cloud
environments, at the edge, and in enterprise systems. The sheer volume and
dispersion of this data make it increasingly challenging to access and move at
the speed and scale required for training AI models. Even for those models
without a targeted launch data to meet, each time a new set of data must be
ingested for additional training compounds to significantly extend the training
phase. Without robust data pipelines, organizations risk starving their AI
systems-whether in infancy or deployed-of information needed to optimize
performance.
The High Stakes of Bad Data
Besides the speed of data acquisition, the quality,
relevance, and diversity of training data aren’t just important-they’re the
foundation for AI systems to function accurately and reliably when it matters
most. As AI adoption grows, so does the risk of “hallucinations”-outputs that
are inaccurate, misleading, or altogether incorrect. These errors often stem
from poor data inputs: stale, incomplete, or non-representative datasets that
distort AI’s decision-making capabilities. In environments where decisions are
critical, such as autonomous vehicles, fraud detection, or medical diagnosis,
such mistakes can lead to devastating repercussions.
While much of today’s focus tends to be on training AI
models, the AI journey doesn’t stop there. Once a model is trained, it enters
the deployment and inferencing phases, where AI systems use pre-trained models
to analyze new data and generate insights in real time. The effectiveness of the
AI insights and outcomes hinges entirely on the quality of the data used during
the model’s training phase. If a model is trained with poor data inputs, the
results will match. For example, a fraud detection system trained on outdated
transaction patterns may fail to identify emerging threats, while a healthcare
model trained on incomplete datasets could lead to incorrect diagnoses. The
quality and relevance of the data used to train AI are not just important-they
are mission-critical.
Preparing for the future
The evolution of AI is shifting from a handful of
organizations, like Meta and Hugging Face, leading the creation of foundational
models to a broader wave of market adoption. Industries such as financial
services, retail, and healthcare are beginning to develop business-specific AI
models and workflows, leveraging foundation models and enhancing them with
domain-specific data. This shift further emphasizes the demand for fast,
seamless data access as more industries adopt AI at scale.
In 2025 and in the world of AI, the organizations that will
rise above the rest will be the ones that address data access challenges such
that they aren’t hindered by data silos, sprawled infrastructure, and latency
when feeding the AI beast. That means investing in high-performance data access
technologies capable of overcoming geographic and infrastructural barriers. The
winners of 2025 will leverage next-generation data access technology to
instantly and securely connect data at the edges, cores, and across clouds to
GPUs and LLMs-regardless of its location, latency, or scale. This enables
organizations to capture the diversity and scale of data needed by those LLM’s-and
bridge vast distances and deliver data when and where it’s needed without
compromise.
As we look ahead, the message is clear: high-performing data
access solutions are no longer just an option in the AI era-they have become
the prerequisite for effectively training and leveraging AI to its full
potential. Whether it’s training the next generation of LLMs or powering
autonomous decision-making systems, the journey starts and ends with the right
data.
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ABOUT THE AUTHOR
Steve
Wallo currently serves as Vcinity’s CTO, overseeing resources related to the
insertion of advanced technologies and strategies into customer architectures
and future IT decision methodologies. He is responsible for bridging future IT
trends into the company’s existing portfolio capabilities and future offerings.
Prior to Vcinity, Wallo was the CTO at Brocade Federal, responsible for
articulating Brocade’s innovations, strategies, and architectures in the
rapidly evolving federal IT space for mission success. Wallo has served the U.S
Government as the chief architect for the NAVAIR Air Combat Test and Evaluation
Facility High Performance Computing Center.
