AI-savvy consumers hold US$46.5bil in spending power, Entermind whitepaper reveals
- ID
- 39828
- Status
- summarized
- Published
- 27 Oct 2026, 10:06 AM
- Fetched
- 27 Oct 2026, 12:17 PM
- Provider
- Digital News Asia
- Category
- malaysia-tech
- Original URL
- https://www.digitalnewsasia.com/business/ai-savvy-consumers-hold-us465bil-spending-power-entermind-whitepaper-reveals
- Source URL
- https://www.digitalnewsasia.com/rss.xml
Summary
- Score
- 5.5
- Created
- 27 Oct 2026, 12:17 PM
- Tags
- Audience
- developersvibe_coderssaas_startup_foundersai_agent_users
What happened
Entermind, an AI-native consultancy, published a whitepaper claiming RM190 billion (US$46.5bil) in annual Malaysian consumer spending sits with adults it classifies as 'Savvy' or 'Vanguard' on its proprietary Customer AI Savviness Index. The Sept 2026 survey of 1,300 adult AI users in Malaysia found 43% consult an AI assistant first for important decisions versus 31% for search engines, 41% have made a purchase or financial decision primarily on AI-provided information, and 78% wish company apps and websites made decisions as easily as AI tools do. Inside apps, 42% prefer a search box versus 17% who prefer menus; daily AI users were twice as likely as weekly-or-less users to act on AI advice (52% vs 26%).
Why it matters
It is a single vendor's survey with a proprietary index and no published sampling detail, so treat the RM190 billion figure as directional, not a market size you'd put in a deck. The usable signal is the interface preference split — 42% want a search box, 17% want menus — which is a concrete argument for spending your next sprint on answer-first search over nav/menu restructuring, and for checking whether your product content is quotable when a customer asks an assistant 'which bank/school/holiday should I pick' instead of browsing your site.
Discussion angle
Would you actually rebuild your app's navigation around a search/answer box on the strength of a 1,300-person vendor survey — and what would you want to see in the methodology (sampling frame, question wording, how 'Savvy' vs 'Vanguard' is scored) before committing engineering time to it?