At a glance
- Source
- Rinta-Kahila, Penttinen, Salovaara, Soliman and Ruissalo (2023). The Vicious Circles of Skill Erosion: A Case Study of Cognitive Automation. Journal of the Association for Information Systems, 24(5), 1378–1412. doi:10.17705/1jais.00829
- How it was done
- The researchers studied one firm in depth and mapped how reliance on automation, complacency and loss of skill fed each other. Case study. A close look at one or a few organizations, based on interviews and observation.
- Who took part
- One accounting firm that had relied on its software’s automated functions for a number of years.
- Where
- An accounting firm; the research team was based in Finland and Australia.
“The erosion of essential human expertise is problematic if workers remain accountable for tasks for which they lack sufficient understanding, rendering them incapable of responding if the automation fails.”
What it found
- The more staff relied on automation, the more complacent they and the firm became.
- Complacency weakened three habits: knowing what the software is doing, keeping one’s own skill up, and checking the output.
- With those habits gone, skills eroded further, which increased reliance. The authors call these loops vicious circles.
- The erosion can stay hidden. Neither workers nor managers may see it until the automation fails.
The authors add that losing obsolete skills is a normal cost of new technology. The trouble starts when the lost skill is one that people still need in order to answer for the work.
When the result holds
The firm’s staff stayed accountable for tasks the software carried out. That combination, responsibility without practice, is what made the erosion harmful.
A later study measured the same kind of loss directly, and much faster. See endoscopists’ skill after working with AI.
Limits
- One firm. A case study shows how something can happen. It does not show how often it happens.
- The automation was accounting software, not today’s AI chat tools.
Practices that use this study
This study supports:
- Check now and then what staff can do without the AI On a sample of cases, have people do the work without the AI and compare the result with their usual work. Start with the people who check AI output and the people still learning the job, and set the tools that learners use to give hints, not answers. Do later · Effort: medium (weeks, a few people)
- Keep the entry-level tasks that newcomers learn on Before you automate routine work, check whether it is how juniors learn the job. If it is, keep some of it for them, or replace the learning another way. Do later · Effort: medium (weeks, a few people)