NetApp sponsored research by leading market
research firm identifies key components of implementing successful
AI initiatives, citing data infrastructure as foundational to
scaling AI responsibly
NetApp® (NASDAQ: NTAP), the intelligent data infrastructure
company, today unveiled insights from its latest report on the
evolving landscape of AI in the enterprise. The IDC White Paper,
sponsored by NetApp, “Scaling AI Initiatives Responsibly: The
Critical Role of an Intelligent Data Infrastructure*,” reveals the
various challenges and business benefits at different levels of AI
maturity and provides insights into the successful strategies
adopted by leading organizations in their efforts to responsibly
scale AI and GenAI workloads. By spotlighting actionable
approaches, the report aims to help organizations avoid common
pitfalls, ensuring that their AI initiatives are not one of the 20%
that are likely to fail. The report also introduces a detailed AI
maturity model developed to assess organizational progress based on
their approach to AI, from AI Emergents and AI Pioneers, to AI
Leaders and AI Masters
Intelligent Data Infrastructure is the Foundation of AI
Success
The IDC White Paper found that:
- AI Masters optimize their data infrastructure for
transformational AI initiatives by facilitating easy access to
corporate datasets with minimal preparation and designing a
unified, hybrid, multicloud environment that supports various data
types and access methods.
- AI Masters have more ambitious AI goals and yet experience
data-related failures including infrastructure-based data access
limitations (21%), compliance limitations (16%), and insufficient
data (17%).
- AI Emergents note similar challenges but also experience budget
constraints (20% Emergents vs 9% AI Masters), insufficient data for
model training (26% vs 17%) and business restrictions on data
access (28% vs 20%).
According to the findings, organizations need an intelligent
data infrastructure in order to scale AI initiatives responsibly.
Where a company falls on the AI maturity scale is determined by the
level of infrastructure they have in place that will not only drive
the long-term success of AI projects, but also of their associated
business outcomes. Those organizations that are just beginning or
have recently begun their AI journey typically have disparate data
architectures or plans for a more unified architecture, while AI
Leaders and AI Masters are likely already executing on a unified
vision. As a result, organizations with the most AI experience are
failing less.
“This IDC White Paper further solidifies that companies need
intelligent data infrastructure to scale AI responsibly and boost
the rate of AI initiative success,” said Jonsi Stefansson, Senior
Vice President and Chief Technology Officer at NetApp. “With
intelligent data infrastructure in place, companies have the
flexibility to access any data, anywhere with integrated data
management to ensure data security, protection, and governance and
adaptive operations that can optimize performance, cost and
sustainability.”
Data Infrastructure Flexibility is Crucial for Data Access
and AI Initiative Success
The IDC White Paper found that:
- 48% of AI Masters report they have instant availability of
their structured data and 43% of their unstructured data, while AI
Emergents have only 26% and 20% respectively.
- AI Masters (65%) and AI Emergents (35%) reported their current
data architectures can seamlessly integrate their organization’s
private data with AI Cloud services.
According to the research, AI Masters know that their data
architecture and infrastructure for transformational AI initiatives
must offer ease of access to corporate data sets without any—or
with only minor—preparation or preprocessing.
“Infrastructure decisions made during the design and planning
process of AI Initiatives must factor in architecture flexibility,”
said Ritu Jyoti Group Vice President, Worldwide Artificial
Intelligence and Automation Research Practice, Global AI Research
Lead, at IDC. “The dynamic nature of data inputs to AI and GenAI
workstreams means easy access to distributed and diverse data—both
structured and unstructured data sets with varying
characteristics—is critical. This requires a flexible, unified
approach to storage, a common control plane, and management tools
that make it seamless for data scientists and developers to consume
data with MLOps integrations.”
Effective Data Governance and Security Processes Drive AI
Success
The IDC White Paper found that:
- The inability for AI Emergents to progress is often due to a
lack of standardized governance policies and procedures; only 8% of
AI Emergents have completed and standardized these across all AI
projects, compared to 38% of AI Masters.
- While 51% of AI Masters reported they have standardized
policies in place that are rigorously enforced by an independent
group in their organization, only 3% of AI Emergents claim
this.
The study found that effective data governance and security are
crucial indicators of organizational maturity in AI initiatives.
Managing data responsibly and securely remains a key issue for
enterprises, because AI stakeholders often try to shortcut security
processes to accelerate development. Feedback from organizations
that have become more successful at delivering positive outcomes
from their AI initiatives demonstrates that governance and security
are not merely cost centers but vital enablers of innovation. By
prioritizing security, data sovereignty, and regulatory compliance,
organizations can mitigate risk in their AI and GenAI initiatives
and ensure that their data engineers and scientists can focus on
maximizing efficiency and productivity.
Efficient Use of Resources Important for Scaling AI
Responsibly
The IDC White Paper found that:
- 43% of AI Masters have clearly defined metrics for assessing
resource efficiency when developing AI models that were completed
and standardized across all AI projects compared to 9% of AI
Emergents.
- 63% of all respondents reported the need for major improvements
or a complete overhaul to ensure their storage is optimized for AI
and only 14% indicated they needed no improvements.
As AI workflows become increasingly integral to almost every
industry, it’s critical to acknowledge the impact on compute and
storage infrastructure, data and energy resources, and their
associated costs. A key measure of AI maturity is the definition
and implementation of metrics to assess the efficiency of resource
use in the creation of AI models.
Methodology
In December of 2023 and January of 2024, IDC conducted 24
in-depth interviews and 1,220 quantitative interviews by web survey
with global decision makers involved in IT operations, data
science, data engineering and software development related to AI
initiatives. These interviews revealed in-depth information about
the state of AI initiatives today including the array of
challenges, numerous business benefits, and best practices that
leading organizations have taken to achieve success.
In conducting this analysis IDC has developed an AI maturity
model where organizations fall into one of four maturity levels
based on their current approach to AI in terms of data and storage
infrastructure, data policy and governance, resource efficiency
focus, and stakeholder enablement and collaboration. These maturity
levels are AI Emergents, AI Pioneers, AI Leaders, and AI
Masters.
*Source: IDC White Paper sponsored by NetApp, "Scaling AI
Initiatives Responsibly: The Critical Role of an Intelligent Data
Infrastructure," Doc #US52048524, April 2024
Learn more at:
https://www.netapp.com/pdf.html?item=/media/107000-wp-scaling-ai-initiatives.pdf
Join NetApp and a guest speaker from IDC on Wednesday, May 22 at
11am ET for the “Scaling AI Initiatives Responsibly: The Critical
Role of an Intelligent Data Infrastructure” Webcast:
https://www.netapp.com/forms/ai-thought-leadership-webinar/
About NetApp
NetApp is the intelligent data infrastructure company, combining
unified data storage, integrated data services, and CloudOps
solutions to turn a world of disruption into opportunity for every
customer. NetApp creates silo-free infrastructure, harnessing
observability, and AI to enable the industry’s best data
management. As the only enterprise-grade storage service natively
embedded in the world’s biggest clouds, our data storage delivers
seamless flexibility. In addition, our data services create a data
advantage through superior cyber resilience, governance, and
application agility. Our CloudOps solutions provide continuous
optimization of performance and efficiency through observability
and AI. No matter the data type, workload, or environment, with
NetApp you can transform your data infrastructure to realize your
business possibilities. Learn more at www.netapp.com or
follow us on X, LinkedIn, Facebook, and
Instagram.
NETAPP, the NETAPP logo, and the marks listed at
www.netapp.com/TM are trademarks of NetApp, Inc. Other
company and product names may be trademarks of their respective
owners.
Additional Resources
- Scaling AI Initiatives Responsibly: The Critical Role of an
Intelligent Data Infrastructure
- Enabling AI Transformations Study with Intelligent Data
Infrastructure: Challenges, Best Practices, and Business Outcomes
for AI Leaders
- Advanced Artificial Intelligence – Explore NetApp
Perspectives
View source
version on businesswire.com: https://www.businesswire.com/news/home/20240507078248/en/
Media Contact: Kenya Hayes NetApp
kenya.hayes@netapp.com
Investor Contact: Kris Newton NetApp
kris.newton@netapp.com
NetApp (NASDAQ:NTAP)
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