Despite firm belief in AI plans, businesses’ fragmented AI
strategies and execution that overlook end-to-end lifecycles will
not deliver successful outcomes
News Summary:
- Organizations are failing to understand the compute and
networking demands across the end-to-end AI life cycle, with fewer
than half of IT leaders admitted to having a full understanding of
what the demands of the various AI workloads across training,
tuning and inferencing might be.
- While data management was labelled as one of the most critical
elements for AI success, only 7% of organizations can run real-time
data pushes/pulls and just 26% have set up data governance models
and can run advanced analytics.
- Many businesses are adopting siloed approaches, with only 57%
setting one single consolidated strategy.
- Despite the integral role of legal and compliance functions,
22% of IT leaders aren’t involving legal teams in their business’s
AI strategy conversations at all.
In a research report commissioned by Hewlett Packard Enterprise
(NYSE: HPE), nearly half (44%) of IT leaders surveyed believe their
organizations are fully set up to realize the benefits of AI. The
report reveals critical gaps in their strategies, such as lack of
alignment between processes and metrics, resulting in consequential
fragmentation in approach, which will further exacerbate delivery
issues.
The report, ‘Architect an AI Advantage’, which surveyed more
than 2,000 IT leaders from 14 countries, found that while global
commitment to AI shows growing investments, businesses are
overlooking key areas that will have a bearing on their ability to
deliver successful AI outcomes – including low data maturity
levels, possible deficiencies in their networking and compute
provisioning, and vital ethics and compliance considerations. The
report also uncovered significant disconnects in both strategy and
understanding that could adversely affect future return on
investment (ROI).
“There’s no doubt AI adoption is picking up pace, with nearly
all IT leaders planning to increase their AI spend over the next 12
months,” said Sylvia Hooks, VP, HPE Aruba Networking. “These
findings clearly demonstrate the appetite for AI, but they also
highlight very real blind spots that could see progress stagnate if
a more holistic approach is not followed. Misalignment on strategy
and department involvement – for example – can impede organizations
from leveraging critical areas of expertise, making effective and
efficient decisions, and ensuring a holistic AI roadmap benefits
all areas of the business congruently.”
Acknowledging Low Data Maturity
Strong AI performance that impacts business outcomes depends on
quality data input, but the research shows that while organizations
clearly understand this – labelling data management as one of the
most critical elements for AI success – their data maturity levels
remain low. Only a small percentage (7%) of organizations can run
real-time data pushes/pulls to enable innovation and external data
monetization, while just 26% have set up data governance models and
can run advanced analytics.
Of greater concern, fewer than 6 in 10 respondents said their
organization is completely capable of handling any of the key
stages of data preparation for use in AI models – from accessing
(59%) and storing (57%), to processing (55%) and recovering (51%).
This discrepancy not only risks slowing down the AI model creation
process, but also increases the probability the model will deliver
inaccurate insights and a negative ROI.
Provisioning for the end-to-end lifecycle
A similar gap appeared when respondents were asked about the
compute and networking requirements across the end-to-end AI
lifecycle. On the surface, confidence levels look high in this
regard: 93% of IT leaders believe their network infrastructure is
set up to support AI traffic, while 84% agree their systems have
enough flexibility in compute capacity to support the unique
demands across different stages of the AI lifecycle.
Gartner® expects “GenAI will play a role in 70% of text- and
data-heavy tasks by 2025, up from less than 10% in 2023,” * yet
less than half of IT leaders admitted to having a full
understanding of what the demands of the various AI workloads
across training, tuning and inferencing might be – calling into
serious question how accurately they can provision for them.
Ignoring cross-business connections, compliance, and
ethics
Organizations are failing to connect the dots between key areas
of business, with over a quarter (28%) of IT leaders describing
their organization’s overall AI approach as “fragmented.” As
evidence of this, over a third (35%) of organizations have chosen
to create separate AI strategies for individual functions, while
32% are creating different sets of goals altogether.
More dangerous still, it appears that ethics and compliance are
being completely overlooked, despite growing scrutiny around ethics
and compliance from both consumers and regulatory bodies. The
research shows that legal/compliance (13%) and ethics (11%) were
deemed by IT leaders to be the least critical for AI success. In
addition, the results showed that almost 1 in 4 organizations (22%)
aren’t involving legal teams in their business’s AI strategy
conversations at all.
The fear of missing out on AI and the business risk of over
confidence
As businesses move quickly to understand the hype around AI,
without proper AI ethics and compliance, businesses run the risk of
exposing their proprietary data – a cornerstone for retaining their
competitive edge and maintaining their brand reputation. Among the
issues, businesses lacking an AI ethics policy risk developing
models that lack proper compliance and diversity standards,
resulting in negative impacts to the company’s brand, loss in sales
or costly fines and legal battles.
There are additional risks as well, as the quality of the
outcomes from AI models is limited to the quality of the data they
ingest. This is reflected in the report, which shows data maturity
levels remain low. When combined with the metric that half of IT
leaders admitted to having a lack of full understanding on the IT
infrastructure demands across the AI lifecycle, there is an
increase in the overall risk of developing ineffective models,
including the impact from AI hallucinations. Also, as the power
demand to run AI models is extremely high, this can contribute to
an unnecessary increase in data center carbon emissions. These
challenges lower the ROI from a company’s capital investment in AI
and can further negatively impact the overall company brand.
“AI is the most data and power intensive workload of our time,
and to effectively deliver on the promise of GenAI, solutions must
be hybrid by design and built with a modern AI architecture,” said
Dr. Eng Lim Goh, SVP for Data & AI, HPE. “From training and
tuning models on-premises, in a colocation or in the public cloud,
to inferencing at the edge, GenAI has the potential to turn data
into insights from every device on the network. However, businesses
must carefully weigh the balance of being a first mover, and the
risk of not fully understanding the gaps across the AI lifecycle,
otherwise the large capital investments can end up delivering a
negative ROI.”
ABOUT THE REPORT: In January 2024, HPE commissioned Sapio
Research to conduct a survey to examine where businesses are in
their AI journeys, and whether they are taking a holistic enough
approach to position themselves for success. The survey included
over 2,400 IT decision makers (IT leaders) across 14 markets
(Australia/New Zealand, Brazil, France, Germany, India, Italy,
Japan, Mexico, Netherlands, Singapore, South Korea, Spain,
UK/Ireland, and USA). These IT leaders work at companies of 500+
employees, and span industries from financial services to
manufacturing, retail, and healthcare.
*Press release: Gartner, Use Generative AI to Enhance APM
and Observability, By Martin Caren, 26 February 2024.
GARTNER is a registered trademark and service mark of Gartner,
Inc. and/or its affiliates in the U.S. and internationally and is
used herein with permission. All rights reserved.
About Hewlett Packard Enterprise
Hewlett Packard Enterprise (NYSE: HPE) is the global
edge-to-cloud company that helps organizations accelerate outcomes
by unlocking value from all of their data, everywhere. Built on
decades of reimagining the future and innovating to advance the way
people live and work, HPE delivers unique, open, and intelligent
technology solutions as a service. With offerings spanning Cloud
Services, Compute, High Performance Computing & AI, Intelligent
Edge, Software, and Storage, HPE provides a consistent experience
across all clouds and edges, helping customers develop new business
models, engage in new ways, and increase operational performance.
For more information, visit: www.hpe.com.
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version on businesswire.com: https://www.businesswire.com/news/home/20240430498445/en/
Ben Stricker Ben.Stricker@hpe.com
Hewlett Packard Enterprise (NYSE:HPE)
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