This report reveals the fragmentation of organizations' AI strategies. More than a quarter (28%) of IT leaders say their organization-wide AI approach is “fragmented,” and 35% have created separate AI strategies for individual functions. , 32% have set completely different goals. This siled approach not only hinders cross-functional collaboration and synergy, but also risks duplicating efforts and misaligning priorities.

Perhaps even more alarming is the apparent disregard for ethics and compliance considerations. IT leaders deemed legal/compliance (13%) and ethics (11%) to be the least important to AI success. Surprisingly, almost a quarter (22%) of organizations do not involve their legal team in discussions about their business's AI strategy at all.

risk of overconfidence

The effects of overlooking these blind spots can be far-reaching and potentially damaging. Companies that lack a robust AI ethics policy risk developing models that lack appropriate compliance and diversity standards, resulting in negative brand reputation, lost sales, and costly fines. , a legal battle will ensue.

Additionally, the quality of an AI model's results is directly related to the quality of the data ingested. Data maturity levels remain low, with half of IT leaders admitting that they do not fully understand the demands on their IT infrastructure across the AI ​​lifecycle, leading to inefficient models that include the hallucinatory effects of AI. The risk of developing has increased significantly. Moreover, the huge power demands of AI models can contribute to an unnecessary increase in data center carbon emissions, further eroding his ROI from capital investments in AI and negatively impacting a company's brand.

As organizations navigate the world of AI, it's important to take a holistic and considered approach. From training and tuning models on-premises, co-located, or in the public cloud to inference at the edge, AI has the potential to transform data into actionable insights across all devices on your network.

However, companies must carefully balance their desire to be first movers with the risk of not fully understanding the gaps across the AI ​​lifecycle. Failure to do so could result in large capital investments resulting in a negative ROI, undermining the very benefits that AI promises.

The path to AI success lies in recognizing and addressing these blind spots. Organizations must prioritize data maturity and align data management practices with AI goals. They also need a comprehensive understanding of computing and networking demands across the AI ​​lifecycle to ensure appropriate resource provisioning and infrastructure scalability.

“AI is the most data- and power-intensive workload of our time, and to effectively deliver on the promise of Gen AI, solutions must have a hybrid design and be built on modern AI architectures. ” said Dr. Eng Lim Goh, Senior Vice President of Data and Data. HPE AI. “From training and tuning models on-premises, co-located, or in the public cloud to inference at the edge, Gen AI has the potential to turn data from every device on your network into insights. Companies must carefully weigh the balance between being a first mover and the risk of not fully understanding the gaps across the AI ​​lifecycle, otherwise large capital investments could result in negative ROI. there is.”

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