The Future of Work
B · Useful, limited Flagship Accenture Research report with large surveys and job-posting analytics, but vendor research whose figures are self-reported and whose method is summarised rather than shown.
Summary based on the full text·Original: about 45 minutes to read
In short
Accenture's "Future of Work Trends 2026" (fiscal 2027 Q1), led by Mamta Kapur, Gastón Carrión and Karalee Close. It argues that firms are not yet capturing value from AI: saved time disappears back into the business, few can price AI against outcomes, and process redesign lags executive expectations. It names five trends: new economics of work, "work intelligence" for talent strategy, employee experience, decisions as the operating model's advantage, and leadership under uncertainty.
Key numbers
- 35%organisations that can precisely calculate cost per business outcome for their highest-volume AI use case
- 15% vs 62%executives redesigning end-to-end processes with AI at the core vs expecting AI to transform their business model within three years
- 119%rise in skill-depth requirements for entry-level roles, 2022 to 2025
- 0 of 202interviewed workers and managers who described an explicit policy on where AI-saved time should go
- 5%organisations that would bank the savings if token prices fell 25% or more
As reported by the source.
English·PDF·opens accenture.com
What it reports
- One in five organisations with heavy AI adoption say nothing fundamentally new has been created with saved time; none of 202 interviewed workers and managers in seven countries described a policy on where saved time should go.
- Only 35% can precisely calculate cost per business outcome for their highest-volume AI use case. 60% describe token spend as workflow cost or AI capacity; 19% compare it to labour cost.
- Token prices are expected to fall about 19% over 24 months while consumption rises about 78%. If prices fell 25% or more, 5% would bank the savings, 42% would extend AI to more people and 35% would launch use cases previously judged too costly.
- 15% of executives are redesigning end-to-end processes with AI at the core; 62% expect AI to substantially or completely transform their core business model within three years.
- Entry-level skill-depth requirements rose 119% between 2022 and 2025; expertise requirements rose 35% (entry), 32% (early career) and 37% (mid-career); the entry-to-mid expertise gap widened 42%.
- Mid-career roles requiring strong decision-making carry a salary premium of more than 50%; at entry level, 12% in 2025.
How they got it
Interviews with 45 senior executives and academics across 11 industries and 12 geographies; AI-moderated interviews with 202 workers and managers in seven countries; a Pulse of Change survey of 3,000 staff and 3,000 executives in 20 markets; a cognitive-intensity analysis of 830+ occupations; and expertise modelling on more than one million Lightcast job postings. Accenture client experience is also drawn on.
Limits
- Vendor research that supports Accenture's HR and talent reinvention services.
- Survey questions, samples per figure and modelling methods are not reproduced; most numbers are footnoted to Accenture's own studies.
- Interviews were AI-moderated, and "no one described a policy" is an absence in interview accounts, not an audit.
Why it matters for companies
Two numbers are worth checking internally: whether you can price your main AI use case against a business outcome, and whether anyone owns the decision on what saved time is used for. Without both, productivity claims cannot be tested. The entry-level skill data supports the concern that AI removes the tasks juniors learned on while raising the bar to get hired. Treat the percentages as Accenture's survey view and test them against your own data.
Summary based on the full text. Read the original for the full argument and data.Open PDF on accenture.com