Global report finds organizations overlook huge blind spots in their AI overconfidence

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.