Asia’s AI “Jitters” Reveal Mismatch Between Valuations And Real‑World Adoption

The Reuters global‑markets piece on 17 February highlighted “AI jitters” as one of the main forces behind equity gyrations, hinting at a growing mismatch between market valuations and on‑the‑ground adoption in many sectors.

In the US, mega‑cap tech firms with credible AI roadmaps continued to deliver strong results, but smaller names riding the hype saw more volatile trading as investors questioned whether their revenue trajectories justified premium multiples. In Asia, similar questions were being asked about chipmakers and platform companies whose valuations had run far ahead of current earnings.

CIO forums across ASEAN and North Asia, including the CIO Leadership Live ASEAN series, paint a more nuanced picture of AI adoption. Manufacturing and supply‑chain executives report that while computer‑vision quality control, predictive maintenance and AI‑assisted documentation are gaining traction, many generative‑AI pilots have struggled due to poor data quality and integration issues. IDC analysts expect AI deployment in heavy industry to lag services and finance through 2026 for precisely these reasons.

In finance and fintech, by contrast, AI is moving faster into production. ASEAN‑focused analyses describe rapid rollout of machine‑learning models for credit scoring, fraud detection and personal financial management across digital banks and embedded‑finance platforms. However, regulators are increasing scrutiny of model risk, explainability and bias, which may slow some deployments even as valuations assume seamless scaling.

This divergence between financial‑market expectations and operational realities helps explain the “jitters” Reuters referenced. Investors find themselves trying to discount not only AI’s eventual economic impact but also the timing and distribution of gains across sectors and regions—a notoriously difficult task.

For Gulf and Asian corporates, the lesson from 17 February’s cross‑currents is that AI strategy must now be managed as both a technological and a capital‑markets issue. Over‑promising on AI can fuel short‑term rallies but invite harsh corrections if delivery lags; under‑investing risks erosion of competitiveness and valuation multiples relative to peers who do execute.