What it is
A worker with too many cases and a free chatbot on their phone has a fast way to draft a letter. Nobody with authority over the deployment approved that chatbot. The survey cited below reports that most respondents use AI tools in their role, and that most have limited or no control over how their workplace selects them.
What it pushes on in the Lab
In the Lab, this pressure raises the failures people write into records. It raises record contamination pressure. It opens a pathway where people paste client data into an unsanctioned tool. It also lowers the engagement of deployment authority, because nobody with that authority approved the tools.
Who feels it
The worker feels it as help, which is why the practice grows. The clients carry the risk twice: their details leave the organization, and an unchecked answer enters their record.
What answers it
An answer has to narrow or close the pathway out of the organization, gate what workers write back into the record, or put someone with authority over the tools in use. Each lever below does at least one of these.
Levers in the Lab that push the other way on something this pressure pushes on:
- Gate record entriesPattern:Human-in-the-loop write gating
- Vet connectionsPattern:Connection authorization
- Store less dataPattern:Data minimization
- Train the staff
- Review on scheduleat its strong tierPattern:Oversight cadence & retrospectives
The list leaves out levers the Lab has retired, levers it keeps as counter-examples, and any lever no network offers.
Where it starts switched on
Networks in the Lab that start with this pressure switched on:1
The evidence behind its effects
The Lab cites these claims from the evidence registry for this pressure's effects.
In a national survey of 1,179 U.S.-based social workers conducted from October 2025 to February 2026 by the University of Texas at Austin in collaboration with NASW, 63.5% of respondents reported using AI tools or technologies in their current role.[2]
Borah, E., Meyerhoff, J., Al-Turk, A., Gower, K., & Mastryukova, A. (2026, June). Use of artificial intelligence in social work practice: Findings and recommendations from a national survey. Moritz Center for Societal Impact, Steve Hicks School of Social Work, The University of Texas at Austin. https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
Appears in: Evidence correction (2026)
Topics: human-ai-interaction, social-work
Isbanner, S., O'Shaughnessy, P., Steel, D., Wilcock, S., & Carter, S. (2022). The Adoption of Artificial Intelligence in Health Care and Social Services in Australia: Findings From a Methodologically Innovative National Survey of Values and Attitudes (the AVA-AI Study). Journal of Medical Internet Research, 24(8), e37611. https://doi.org/10.2196/37611
Appears in: Evidence reverification (2026)
Topics: human-ai-interaction, public-benefits
In the 2025–2026 University of Texas at Austin / NASW national survey of U.S. social workers, 42.1% of respondents reported having no role in decision-making about AI adoption in their workplace; the report concludes most respondents have limited or no control over how AI technologies are selected or implemented within their organizations.[†]
Borah, E., Meyerhoff, J., Al-Turk, A., Gower, K., & Mastryukova, A. (2026, June). Use of artificial intelligence in social work practice: Findings and recommendations from a national survey. Moritz Center for Societal Impact, Steve Hicks School of Social Work, The University of Texas at Austin. https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
https://moritzcenter.utexas.edu/wp-content/uploads/2026/06/Moritz-Center-AI-SW-Survey-Report.pdf
Appears in: Evidence correction (2026)
Topics: human-ai-interaction, social-work