2026年职场AI应用从生产力增效到组织转型研究报告

C O M M U N I T YP A P E RJ A N U A R Y2 0 2 6 Contents Forewords3 Executive summary5 Introduction6 1The promise of AI7 2The reality of AI11 2.1 The promise of AI is conditional112.2 Trust and governance122.3 Reshaping job tiers132.4 Uneven adoption14 3The future of AI and work163.1 Four imperatives to support workers in adopting AI163.2 Three macro questions19 Conclusion20 Appendix: Questions sent21 Contributors23 Endnotes25 Disclaimer This document is published by theWorld Economic Forum as a contributionto a project, insight area or interaction.The findings, interpretations andconclusions expressed herein are a resultof a collaborative process facilitated andendorsed by the World Economic Forumbut whose results do not necessarilyrepresent the views of the World EconomicForum, nor the entirety of its Members,Partners or other stakeholders. ©2026 World Economic Forum. All rightsreserved. No part of this publication maybe reproduced or transmitted in any formor by any means, including photocopyingand recording, or by any informationstorage and retrieval system. Foreword Erik Brynjolfsson Jerry Yang and Akiko Yamazaki Professor,Institute for Human-Centered Artificial Intelligence,Director of the Digital Economy Lab, Stanford University;Co-Chair, World Economic Forum Future of Jobs Initiative First, the risk. If you remove the first rung of thecareer ladder, you are not just changing coststructure. You are potentially damaging your firm’stalent pipeline. You are making it harder for the nextgeneration to acquire tacit knowledge, mentorshipand judgement. Over time, that erodes managerialdepth and strategic capacity. You cannot promotepeople who were never hired. AI at work: What the earliest signals are tellingus – and what leaders need to do now We are living through one of the most importanttransitions in the history of work. For decades,digital technologies have reshaped production,logistics, customer service and even strategy. Butartificial intelligence – especially modern generativeAI – is different in both its speed and the scopeof tasks it can take on. It is no longer limited toroutine, codified work. It now reaches deeply intoanalytical, creative and communication tasks thatwere historically considered the foundation ofearly-career “knowledge work”. Second, the opportunity. The same tools thatallow AI to handle first-draft work can, if deployedthoughtfully, accelerate human development insteadof replacing it. We are already seeing cases wherejunior employees are brought into higher-valueconversations earlier, supported by AI co-pilots andinternal knowledge assistants. Rather than spendingtheir first year formatting decks and responding totier-one tickets, they are sitting in on client meetings,synthesizing options and exercising judgement –years ahead of the old schedule. This model canproduce not fewer skilled workers, but stronger ones. This community paper, produced in collaborationwith leading technology and services firms thatare both building and deploying AI at scale, offersa rare view inside that transition. It does notspeculate about what AI might accomplish someday. It documents what is already happeninginside organizations today: how work is beingredesigned, which skills are being repricedand how leadership, not just technology, isdetermining who benefits. Which outcome you get is not determined by thetechnology. It is determined by leadership. That is, ultimately, the core message for businessleaders: the economic gains from AI will not comesimply from “installing” a model. They will come fromredesigning workflows, incentives, managementpractices, governance and upskilling pathways sothat humans and AI together create more valuethan either could alone. The same model, droppedinto two different firms, can be either a demo or atransformation. The difference is organizational. Leaders are discovering that AI can now performmany of the traditional “first-rung” tasks thathistorically justified hiring a large class of junioranalysts, assistants, researchers and associates:draft the briefing; prepare the summary; generatethe first pass at a marketing concept; triage thecustomer ticket; build the slides; do the firstcompliance check. Those tasks are no longer theexclusive domain of entry-level hires. This is why I have argued for years that thereal bottleneck in the digital economy is notinvention – it is diffusion. We are quite good, This has two profound implications. as a society, at producing powerful technologies.We are much less good at translating thosetechnologies into broad-based productivitygrowth, higher wages and shared prosperity. Thattranslation challenge is one of the reasons I helpedstart Workhelix. Our focus is to help companiesidentify specific workflows where AI can augmentworkers, boost measurable performance andcreate durable competitive advantage – notjust cut short-term labour costs. The goal isempowerment, not hollowing out. Finally, we cannot n