Organizations feel pressure to accelerate their adoption of artificial intelligence (AI) but have yet to see the anticipated gains. This is coupled with the lack of necessary infrastructure to scale their deployments.
According to Cisco’s 2024 AI Readiness Index, just 23% say they have the necessary GPUs to meet current and future AI demands. The index surveyed 3,600 senior business leaders across 14 Asia-Pacific markets, including Japan and China.
Another 30% of respondents have the capabilities to safeguard data in their AI models with end–to–end encryption, security audits, continuous monitoring, and instant threat response.
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Moreover, only 15% of respondents are fully prepared to deploy and tap AI-powered technologies, compared to 17% last year, Cisco said. The index assesses six areas to measure AI readiness, including strategy, data, governance, and culture. “This decline underscores the challenges companies face in adopting, deploying, and fully leveraging AI,” the US networking vendor said. “Given the rapid market evolution and the significant impact AI is anticipated to have on business operations, this readiness gap is especially critical.”
Across the Asia-Pacific, 98% of respondents expressed an increased urgency to deploy AI over the past year, with 49% pointing to their CEO and leadership team as the main source of pressure. Another 40% said their middle management was feeling pushed to adopt AI, while 36% cited their board of directors.
These organizations are setting aside resources for AI, with 50% parking between 10% and 30% of their IT budget to AI deployments. Some 30% plan to spend at least 40% of their IT budget on AI initiatives over the next four to five years, 5% more than those spending the same proportion currently.
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Half of the respondents highlight the need to improve the scalability and manageability of their IT infrastructure as a top priority
Some 42% are spending their AI funds on cybersecurity, 40% are focusing on IT infrastructure and 34% are investing in data analysis and management.
Most respondents hope their AI investments will improve the efficiency of systems, processes, operations, profitability, ability to innovate, remain competitive, and increase revenue.
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However, more than 40% have not seen any returns on their investment or described the gains as short of their expectations to augment, assist, or automate current processes, the study revealed.
“As companies accelerate their AI journeys, it’s critical they adopt a comprehensive approach to implementation and connect the dots to link AI ambition with readiness,” said Dave West, Cisco’s president for Asia-Pacific, Japan, and Greater China. “This year’s AI Readiness Index reveals that to fully leverage the potential of AI, companies need a modern digital infrastructure capable of meeting evolving power needs and network latency requirements from growing AI workloads. This must be supported with the right visibility to achieve their business objectives.”
Growing need to find AI success
A separate report from IDC released Friday projected that Asia-Pacific business leaders will demand an 80% success rate on their generative AI (gen AI) initiatives by 2027, up from the current rate of 62%.
These statistics signal a move away from the “scramble” to deepen the integration of gen AI within the organization, towards improving efficiency and growing revenue, the IDC stated.
“This shift urges businesses to move beyond pilot projects and embed AI into core operations, aiming for measurable success and strategic outcomes by 2027,” the research firm said.
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It also anticipates AI to be a significant economic growth driver in the Asia-Pacific region, where spending on AI will grow 1.7 times faster than total digital technology investments over the next three years.
The increase is expected to fuel a $1.6 trillion economic impact across the region by the end of 2027.
IDC further predicts that, by 2025, 70% of organizations will formalize policies and oversight to address AI risks, including ethical and personally identifiable information.
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With high-quality data critical to AI success, the use of data-as-a-product architectures will help break down data silos in 50% of large Asia-Pacific Japan organizations by 2027.
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However, more than one-third of organizations by 2026 will remain “stuck” in the experimental, point-solution phase of AI experimentation.
“2025 will be the year of ‘the AI Pivot,'” said Sandra Ng, IDC’s group vice president and general manager. “It marks the shift from seemingly endless AI experimentation to executing AI at scale. Organizations must integrate AI into their business strategies to stay ahead of the competition, moving beyond isolated pilot projects to achieve real, measurable business outcomes through structured approaches, governance, quality data, and scalable fit-for-purpose infrastructure.”
