---
id: "lib-2026-09-30-global-workforce-hopes-and-fears-survey-2026"
type: "library"
title: "Global Workforce Hopes and Fears Survey 2026"
lang: "EN"
org: "PwC"
source_type: "Survey"
date: "2026-09-30"
week: "2026-W41"
url: "https://nosov.ai/library/lib-2026-09-30-global-workforce-hopes-and-fears-survey-2026/"
original_url: "https://www.pwc.com/gx/en/issues/workforce/hopes-and-fears.html"
tier: "A"
tier_name: "Strong evidence"
tier_reason: "PwC's annual worker survey with a large weighted sample (49,364 workers in 48 countries and regions), but based on self-reported perceptions and framed to support PwC's advisory services."
summary_basis: "full-text"
original_minutes: "29"
tags: ["future of work", "enablement", "ai adoption", "l&d access", "trust"]
---

# Global Workforce Hopes and Fears Survey 2026

Tier A (Strong evidence): PwC's annual worker survey with a large weighted sample (49,364 workers in 48 countries and regions), but based on self-reported perceptions and framed to support PwC's advisory services.

Summary based on the full text.

## In short

PwC's Global Workforce Hopes and Fears Survey 2026, published 29 September 2026, polled 49,364 workers in May and June 2026. It finds AI use spreading fast but unevenly. A minority of "front-runners" (14%) with strong AI benefit and in-demand skills are moving ahead, while the "engine room" majority (56%) has less AI access, falling learning access and low trust in leaders. PwC argues that adoption alone does not deliver returns and that companies must redesign roles, workflows and incentives.

## Key numbers

- 64%: workers who used AI at work in the past 12 months, up ten points year-on-year
- 14% to 22%: rise in the share of workers using GenAI daily over one year
- 56%: workers in the 'engine room' cohort with less access to AI, learning and opportunity
- 7 points: year-on-year fall in trust in management

## What it reports

- 64% of workers used AI at work in the past 12 months, up ten points; daily GenAI use rose from 14% to 22%; 59% expect their AI use to increase.
- Front-runners: 51% use GenAI daily versus 11% of the engine room; 75% trust top management and 82% their manager; 29% are very or extremely likely to change employer within a year.
- Engine room: 33% trust top management and 40% their manager; most say they have never used AI; under 40% have access to learning resources versus nearly 80% of front-runners.
- Overall trust in management fell seven points; fewer than half of workers trust top management.
- Only 34% can pay bills with money left over, down eight points; 62% say cost of living affected them at work.
- Access to learning and development fell from 59% to 51%.
- More workers cite economic volatility (57%) than AI taking on tasks (44%) as a job-security risk.
- 31% of senior executives and 24% of managers find it difficult to manage change.

## How they got it

An online survey of 49,364 workers across 48 countries and regions and 29 sectors, May-June 2026, weighted to each country's working population by gender and age. Cohorts come from two indexes: labour-market pressure (demand for the role, difficulty replacing skills) and self-reported AI advantage (quality, creativity, skills, value).

## Limits

- All measures are self-reported perceptions, not observed productivity or pay.
- Cited figures such as the 62% AI wage premium and "20% of companies capture 74% of AI-driven returns" come from other PwC studies not described here.
- The recommendations align with PwC's workforce consulting offer.

## Why it matters for companies

The survey shows AI access tracking existing advantage: the workers who already get training and trust also get the tools. For a traditional company, the large operational workforce is the group most likely to be left out and least trusting. Rolling out tools without protected learning time and changed incentives is likely to widen that gap. The figures on falling learning access and manager strain point to where internal programmes should start.

Original: https://www.pwc.com/gx/en/issues/workforce/hopes-and-fears.html