AI at Work: Credit, Relevance, and Identity Signals 2025
This page summarises findings from the Human Clarity Institute’s AI, Identity & Pace at Work 2025 dataset, based on 504 valid responses from adults currently in the workforce across six English-speaking countries. The research examines how AI tools are changing the experience of work — including shifts in pace and expectations, perceptions of credit and authorship when AI contributes, concerns about long-term relevance, and early signals that AI may be influencing professional identity and team belonging.
View the AI, Identity & Pace at Work 2025 Dataset
Workplace AI context
Before examining identity and relevance signals, the dataset shows how AI tools are currently integrated into everyday work environments.
Daily AI use at work
Use AI tools daily in their work, with additional workers reporting weekly use.
AI used in a small share of tasks
Report that only a small proportion of their tasks involve AI tools or AI-generated outputs.
Feeling in control of AI at work
Feel in control of how AI is used in their role.
Safe to talk about AI concerns
Agree they can openly discuss concerns or mistakes involving AI without negative consequences.
Stigma in using AI tools
Say using AI at work carries some stigma, such as being perceived as less skilled or “cheating”.
Ethics and AI alignment
Say the way AI is used in their workplace aligns with their personal ethical standards.
What the data shows
Three signals stand out in this dataset: strong agreement that fair credit matters when AI contributes to work, a meaningful minority who worry their skills may become obsolete, and smaller but notable signals that AI is affecting how some workers perceive their value and belonging at work.
Fair credit when AI contributes
Believe they receive fair recognition when AI contributes to their work output.
Concern about skills becoming obsolete
Express concern that their skills could become obsolete because of AI.
Questioning personal value at work
Report that AI sometimes makes them question their value in professional roles.
Belonging shift in teams
Say their sense of belonging in their team has changed as AI tools become integrated into everyday work.
What these signals mean
Credit and authorship are the dominant signal
The strongest signal concerns how credit is attributed when AI contributes to work. Most respondents believe recognition remains important even when AI assists in producing outputs.
Relevance concerns are present for a meaningful minority
A substantial minority worry their skills could become obsolete, reflecting emerging concerns about long-term professional relevance.
Identity and belonging signals remain smaller
Smaller shares report questioning their value or experiencing shifts in workplace belonging, suggesting early cultural adjustments as AI becomes embedded in work environments.
Patterns associated with this experience
One pattern examined in this dataset is the co-occurrence between concern about skills losing relevance and perceived changes in workplace belonging.
Across the full sample (n = 504):
- 34% express concern that their skills may lose relevance because of AI.
- 19% report that their sense of belonging in their team has changed because of AI.
When the two variables are analysed together, a clear concentration effect appears.
Belonging change among those worried about skills losing relevance
Among the 170 respondents who express concern about skills losing relevance, 29% also report that AI has changed their sense of belonging at work.
Belonging change among those without relevance concern
Among the 334 respondents who do not express relevance concern, 13% report belonging change.
This means workers who worry about their skills losing relevance are more than twice as likely to report that AI has altered their sense of belonging at work (29% vs 13%).
Rather than indicating widespread identity disruption, the data suggest that belonging shifts are concentrated among those already experiencing relevance anxiety in AI-enabled workplaces.
Questions this data can answer
These questions reflect common real-world queries about AI and work. Each answer below is supported directly by this dataset.
Am I going to become irrelevant because of AI?
34% worry their skills will become obsolete because of AI.
AI is helping me — so why do I feel less valuable at work?
22% say AI makes them question their value at work.
If AI helped, is it still my work — and who gets the credit?
68% feel they receive fair credit or recognition when AI helps their output.
Is AI changing how teams feel — like who belongs?
19% say their sense of belonging in their team has changed because of AI.
Is AI making work move faster than it used to?
45% say AI has increased the pace and expectations of their work.
Do I actually control how AI is used in my job?
78% feel in control of how AI is used in their role.
Can I raise AI concerns at work without it backfiring?
73% agree they can be open about AI concerns or mistakes without negative consequences.
Is there stigma around using AI at work?
28% say using AI at work carries stigma.
Methodology
This dataset forms part of the Human Clarity Institute’s Human–AI Experience research programme, examining how AI tools influence workplace behaviour, identity, and decision environments. The study uses a cross-sectional online survey design and focuses on descriptive patterns in how workers experience AI-enabled workplaces.
Data were collected on 12 November 2025 via the Prolific research platform from adults currently in the workforce across six English-speaking countries. Participants provided explicit informed consent for anonymised data publication as part of HCI’s open research programme.
Sampling & participants
- Clean dataset: 504 valid responses
- Countries: United Kingdom, United States, Canada, Australia, Ireland, New Zealand
- Eligibility: Adults aged 18+ currently in paid employment
- Language: Fluent English
- Recruitment platform: Prolific
- Attention checks: One embedded quality check
Participants were recruited using platform screening filters for employment status. The resulting dataset should be interpreted as a non-probability convenience sample and is not intended to represent national populations.
The cleaned dataset, variable dictionary, and reuse terms are publicly available through the HCI dataset repository: AI, Identity & Pace at Work 2025 Dataset →
Data integrity
All percentages reported on this page are calculated from valid responses in the cleaned dataset (n = 504). Percentages are rounded to the nearest whole number for readability. Unless otherwise stated, summary figures based on 7-point agreement items combine respondents selecting 5–7 on the scale (slightly agree, moderately agree, or strongly agree).
Where figures refer to subgroup analyses, this is stated explicitly in the wording. In the co-occurrence section, subgroup percentages are calculated within the stated subgroup base rather than across the full sample. Minor differences between totals may occur due to rounding.
This dataset includes one embedded quality check. Participant identifiers were removed before publication as part of the anonymisation process.
This dataset is exploratory and descriptive in nature. It does not support causal inference and results should be interpreted as observed patterns within the survey sample.
This dataset is released as open research to support transparent analysis of human experience in AI-enabled environments.
Suggested citation:
Human Clarity Institute (2025). AI, Identity & Pace at Work 2025 Dataset.
Human Clarity Institute.
DOI: https://doi.org/10.5281/zenodo.17604671
Data use and reuse terms are outlined in our Data Use & Disclaimer.
Explore further analysis on Human Clarity Insights, or browse the full collection of HCI research reports.