HPE Report Finds Organizations Are Overlooking Huge Blind Spots in Their AI Overconfidence 

As artificial intelligence continues to revolutionize various industries, organizations are exploring new ways to tap into the transformative potential of AI technologies. While the majority of business leaders believe that AI is essential for business sustainability, has their organization been successful in adopting AI? 

According to a study by Hewlett-Packard Enterprise Company (HPE), too many businesses have a false sense of confidence in their AI approach. The report reveals that while organizations are committed to continue investing in AI, they are overlooking key areas such as deficiencies in their networking and computer provisioning, low data maturity levels, data preparedness, and compliance considerations. 

These gaps can undermine the ability of the organization to deliver successful AI outcomes and can negatively impact the future return on investment (ROI). While short-term decisions related to AI investments may pay off, having more sustained success will require a deeper understanding of the full AI lifecycle. 

The HPE report reveals that less than half of IT leaders admit to having a full understanding of the demands of AI workloads. This casts serious doubts over their ability to accurately provision AI workloads. 

There are also concerns about low data maturity levels. Only 7% of organizations have developed the ability to push or pull real-time data to enable innovation and external data monetization. Similarly, only 26% have set up data governance models for advanced analytics.  

Even more alarming is that fewer than 6 of 10 respondents said their organization is fully capable of handling any of the key stages of data preparation for AI models, such as accessing, storing, and processing. Without the ability to adequately prepare data for AI models, the organization risks slowing down AI creation, not generating reliable insights from their AI models, and a negative 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.” 

According to Hooks, a misalignment of strategy and department involvement can be a major obstacle to AI success as it hinders the ability of a company to fully leverage its expertise and resources in implementing AI. Hooks recommends a holistic AI strategy that brings benefits to every part of the organization.  

Despite the increasing importance of ethics and the heightened scrutiny of AI compliance by regulatory authorities, these two areas are being overlooked by organizations based on the findings of the report. It is concerning that legal/compliance (13%) and ethics (11%) are considered by IT leaders to be the least critical for AI success. One in four organizations are not even involving their legal team in AI strategy at all. 

The report shared some key tips for addressing these alarming gaps and blindspots. It recommends not to rush AI adoption just because it is a trending technology. The fear of missing out should not overshadow business needs. Organizations should assess their desired business outcomes and leadership output to determine where AI would be most useful, and focus on that.  

Organizations should also take an overarching AI strategy that spans the entire business and includes all the stakeholders. In addition, the organization should ensure they foster collaboration between IT leaders and C-Suite executives, tapping into the technical expertise of the IT team and the business acumen of the leadership team. 

Lastly, the report recommends a nuanced approach to AI implementation by understanding the entire AI lifecycle. Each stage of AI implementation requires careful consideration and optimization to ensure the success of AI outcomes. These tips can help organizations transform data into actionable insights and derive greater value from their AI initiatives. 

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