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Proof over Promise Insights on Real World AI Adoption from 2025 MINDS Organizations
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- 2026.01.19 · 발행처 페이지에서 확인
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In collaboration
with Accenture
Proof over Promise:
Insights on Real-World AI Adoption
from 2025 MINDS Organizations
W H I T E P A P E R
J A N U A R Y 2 0 2 6
Images: Adobe Stock
Contents
Foreword 3
Executive summary 4
1 Advancing meaningful AI adoption through the MINDS programme 5
1.1 Overview of the MINDS programme 5
1.2 A global landscape of AI adoption: diversity across regions, 6
industries and scales
1.3 Selected cohort of organizations for the 2025 MINDS programme 7
2 Key insights from MINDS organizations 12
2.1 Insight 1: Harnessing AI as a core part of how organizations work 12
2.2 Insight 2: Amplifying strengths when humans
and AI work together within a changing workforce 14
2.3 Insight 3: Strengthening data foundations and augmented 17
data sources to advance impact and scale
2.4 Insight 4: Modernizing the technology stack for advanced 20
AI capabilities
2.5 Insight 5: Scaling AI with confidence through responsible 23
AI practices
2.6 Amplifying impact through the interplay of key insights 24
Conclusion 25
Contributors 26
Endnotes 28
Disclaimer
This document is published by the
World Economic Forum as a contribution
to a project, insight area or interaction.
The findings, interpretations and
conclusions expressed herein are a result
of a collaborative process facilitated and
endorsed by the World Economic Forum
but whose results do not necessarily
represent the views of the World Economic
Forum, nor the entirety of its Members,
Partners or other stakeholders.
© 2026 World Economic Forum. All rights
reserved. No part of this publication may
be reproduced or transmitted in any form
or by any means, including photocopying
and recording, or by any information
storage and retrieval system.
Proof over Promise: Insights on Real-World AI Adoption from 2025 MINDS Organizations 2
January 2026 Proof over Promise:
Insights on Real-World AI Adoption
from 2025 MINDS Organizations
Foreword
Muqsit Ashraf Maria Basso 문서 식별번호 497d19dfba4bfb1a81734527 · 페이지별 원문 API
