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Home » Notice A major blind spot hidden in overconfidence in AI is being overlooked – HPE survey results

Notice A major blind spot hidden in overconfidence in AI is being overlooked – HPE survey results

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Big blind spots hidden in AI overconfidence are being overlooked – HPE survey results
Even with a well-thought-out AI plan, a piecemeal AI strategy or initiatives that don’t consider the end-to-end lifecycle will hinder success.
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overview
IT leaders say they have a poor understanding of the compute and networking demands across the end-to-end AI lifecycle in their enterprise, and do not fully understand the training, tuning, and inference demands of various AI workloads. Less than half
Despite the recognition that data management is paramount to AI success, only 7% of companies can push and pull data in real time, and only 7% of companies can set up a data governance model and run advanced analytics. only 26%
Many companies take a siled approach; only 57% have a single, integrated strategy
Despite the critical role played by legal and compliance departments, 22% of IT leaders do not involve them at all in business AI strategy discussions.
HPE surveyed more than 2,400 IT leaders in 14 countries and regions, and found that approximately half (44%) of IT leaders said their companies are well positioned to reap the benefits of AI. Did. On the other hand, the results of this survey reveal that there are significant gaps in strategy, such as misalignment between targets and the process to achieve them, exacerbating operational problems due to fragmented efforts. It turns out it’s possible.
Additionally, while investments in AI are increasing globally, there are many issues affecting AI success, such as low data maturity in enterprises, potential for poor networking and compute allocation, and important ethics and compliance considerations. It also became clear that things that needed to be done were being overlooked. It also became clear that there was a large gap between the “strategy” and “understanding” of the requirements, which could have a negative impact on ROI (return on investment). For more information, see AI
Advantage (https://www.hpe.com/jp/ja/solutions/ai-artificial-intelligence.html?slug=architecting-ai-advantage-jpn&x=4c0V6B&utm_campaign=ai&utm_content=tech-article&utm_source =com-fav&utm_medium=or&utm_term=pressrelease).
said Sylvia Hooks, VP of HPE Aruba Networking.
“There is no doubt that AI adoption is accelerating, with nearly all IT leaders planning to increase spending on AI over the next 12 months. However, it also highlights blind spots that can lead to stagnation if a more comprehensive approach is not focused. It can hinder your ability to leverage, make effective and efficient decisions, and ensure that your overall AI roadmap delivers consistent benefits to all areas of your business.”
Recognize low data maturity
Powerful AI performance that drives business outcomes depends on high-quality data input. Research clearly understands this,
identifying data management as one of the most important factors for AI success, yet data maturity remains low. Only 7% of companies are able to perform the real-time data push and pull needed to innovate and monetize external data, and only a small percentage of companies are able to set up data governance models and run advanced analytics. It was only 26%.
Of further concern is that less than 60% of respondents say the key stages of data preparation for use in AI models are complete (59% accessed key stages of data preparation). , storage 57%, processing 55%, recovery 51%). This situation not only risks slowing down the process of creating an AI model, but also increases the likelihood that the model will yield inaccurate insights and negative ROI. Deployment (provisioning) considering the end-to-end AI lifecycle The results below reveal a gap between IT leaders’ confidence in the state of their networking and compute infrastructure and their understanding of the demands placed on AI workloads.
Confidence expressed by IT leaders: 93% are confident that their network infrastructure is in place to support AI traffic, and 84% are confident that their systems support the unique demands of different stages of the AI ​​lifecycle respondents say they have enough flexibility in compute capacity to
Understanding the demands placed on workloads: Gartner(R) says, “Generative AI will play a role in 70% of text- and data-intensive tasks by 2025, up from less than 10% in 2023.” We expect (*1) ( https://www.gartner.com/document/code/799825?ref=ddisp&refval=5251963 ). However, fewer than half of IT leaders say they fully understand the demands placed on various AI workloads across training, tuning, and inference. This calls into question the ability to properly deploy networking and compute for AI workloads.
Lack of collaboration between departments, compliance, and efforts in line with ethical standards
28% of IT leaders say their organization-wide AI efforts are “fragmented,” revealing a lack of connection between the business dots. More than a third, 35%, have chosen to create a separate AI strategy for their business, and 32% have set completely separate goals.
The danger is that ethical standards and compliance are under increasing scrutiny from both consumers and government agencies, yet are completely overlooked. IT leaders said legal/compliance was not important to AI success at 13%, ethics at 11%, and 22% said they did not include the legal department in discussions about the business’s AI strategy.
Business risks posed by fear of delay in AI implementation and overconfidence As companies increasingly seek to understand the debate surrounding AI, they must consider that without proper AI ethics and compliance initiatives, their data could be at risk. Data is the cornerstone of maintaining competitiveness and brand reputation. The lack of an AI ethics policy can lead to the development of models that lack appropriate compliance and diversity standards, risking negative brand impact, lost sales, and costly fines and legal battles.
There are also additional risks, as the quality of the results you get from AI models depends on the quality of the data you collect. When you combine the indicators that data maturity remains low with the indicator that half of IT leaders admit that they do not fully understand the demands on their IT infrastructure across the AI ​​lifecycle, the AI ​​illusion ( This increases the overall risk of developing an ineffective model, including the effects of
hallucinations. Additionally, running AI models consumes significant amounts of power, which can unnecessarily increase data center CO2 emissions. As a result, the ROI on capital investments in AI will decrease, which can further negatively impact the company’s overall brand.
said Dr. Eng Lim Goh, SVP of Data & AI at HPE.
“AI is the most data-intensive and power-intensive workload of our time, and to effectively meet the promise of generative AI, solutions must be hybrid designs and built with modern AI architectures. From training and tuning models on-premises, co-located, or in the public cloud to inference at the edge, generative AI has the ability to turn data collected from any device on your network into insights. We need to carefully balance being ahead of the curve with the risk of not fully understanding the gaps across the AI ​​lifecycle, lest large capital investments result in negative ROI. This is something I would like you to consider.”
About the report: In January 2024, HPE commissioned a study from Sapio Research (https://sapioresearch.com/) to understand where companies are in their AI journeys and how well they are positioned for success. We looked at whether they were taking a comprehensive approach. The survey surveyed over 500 financial services employees in 14 countries and territories (Australia/New Zealand, Brazil, France, Germany, India, Italy, Japan, Mexico, Netherlands, Singapore, South Korea, Spain, UK/Ireland, and the US). The survey was conducted among over 2,400 IT decision makers (IT leaders) from companies in a variety of industries, including manufacturing, retail, and healthcare. Notes
*1 Source: Press release: Gartner, Use Generative AI to Enhance APM and Observability, By Martin Caren, 26 February 2024.
Gartner is a trademark and service mark of Gartner, Inc. and/or its affiliates in the United States and other countries and is used herein with permission. All rights reserved.
*This release is a Japanese translation based on the English release issued by Hewlett Packard Enterprise (Headquarters: Houston, Texas, USA, hereinafter referred to as HPE) on April 30, 2024 (local time). Please see here for the original text (full text).
https://www.hpe.com/us/en/newsroom/press-release/2024/04/global-report-finds-organizations-overlook-huge-blind-spots-in-their-ai-overconfidence.html ■About Hewlett Packard Enterprise (HPE)
Hewlett Packard Enterprise (NYSE: HPE) is a global Edge-to-Cloud company, helping you unlock the value of all your data, everywhere, and accelerate business results. With decades of reimagining and innovating to improve the way people live and work, HPE delivers unique, open and intelligent technology solutions as a service. We deliver cloud services, compute, HPC & AI, intelligent edge, software, and storage with a consistent experience across all clouds and edges to help customers create new business models, develop new engagement, and improve operational efficiency. We help you maximize performance. For more information, please visit https://www.hpe.com.
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