A recent study commissioned by Hewlett Packard Enterprise (HPE) has uncovered significant gaps in the AI strategies of organizations, with 44% of IT leaders asserting that their setups are fully prepared to harness AI benefits. However, the report titled ‘Architect an AI Advantage’ suggests a different reality. Surveying over 2,000 IT leaders across 14 countries, the findings indicate that despite the increasing investments in AI, many organizations might not be ready to realize successful AI outcomes due to several overlooked critical areas.

“Enterprises in APAC are understandably eager to embrace AI to reap its many transformative benefits,” said Joseph Yang, General Manager, HPC and AI, APAC and India at HPE.

“Yet, as the findings from our survey clearly indicate, many organisations are not yet ready for effective and safe AI deployments. We are seeing critical blind spots in their AI strategies, including overlooking ethics and compliance, that could lead to serious consequences on the business. As AI investments continue to soar, it’s important that organisations devise a holistic AI roadmap that addresses these blind spots to ensure AI success and optimise their ROI.”

The study highlights a concerning low level of data maturity among organizations. Only 7% of respondents can manage real-time data transactions that enable innovation and external data monetization, and merely 26% have established robust data governance models. This lack of data preparedness not only hampers AI model accuracy but also increases the likelihood of poor return on investment.

Moreover, the report reveals overconfidence among IT leaders regarding their infrastructure’s readiness for AI demands. While 93% believe their network infrastructure is equipped to support AI traffic, and 84% feel their systems have adequate compute flexibility, less than half fully understand the specific demands of various AI workloads. This disconnect could lead to ineffective provisioning for AI projects, escalating the risks associated with AI implementations, including increased carbon emissions from data centers due to the high power demands of AI models.

“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.”

The report serves as a crucial wake-up call for businesses diving into AI investments. It stresses the need for a holistic approach that addresses data management, infrastructure readiness, and ethical and compliance issues to fully exploit the transformative power of AI and ensure sustainable success in the digital era.