Average realistic automation risk across all Research Infrastructure Specialist profiles in the dataset.
Raw potential
73%
Realistic risk
38%
Raw potential = I/O automation ceiling. Realistic risk = adjusted for informal knowledge and social context.
Score spread
Distribution across 10 profiles.
Middle half of Research Infrastructure Specialists score between 34% and 40%.
0%
50%
100%
p10 · 33%
42% · p90
Task breakdown by work type
On-screen work66%
Done entirely on a computer. High AI exposure — these tasks are already in the automation zone.
In-person + screen27%
Physical sensing, digital output — e.g. interviewing someone then writing a report. Partially protected.
Computer + action0%
Computer input, real-world output — needs someone to act on it, not just software.
Fully in-person7%
No computer required. Furthest from automation — the strongest human advantage.
Typical tasks
3 synthetic profiles for a Research Infrastructure Specialist, ordered by automation exposure.
Tab between them to see how task mix drives the score difference.
TaskTimeTypeExposure
Collaborate with researchers to optimize workflows for high-performance computing (HPC) or data-intensive tasks, including scripting, parallelization, or containerization (e.g., Docker, Singularity)
deep expertisesocial core
18%AD
10%
Evaluate and recommend new tools or technologies (e.g., AI/ML frameworks, data visualization software, or cloud services) to improve research efficiency and scalability
deep expertisesocial element
18%DD
36%
Troubleshoot and resolve technical issues reported by researchers, such as software installation problems, data transfer failures, or performance bottlenecks in computing clusters
deep expertisesocial core
17%AD
5%
Design and maintain cloud-based research computing environments (e.g., setting up virtual machines, configuring storage solutions, and managing access controls for research teams)
16%DD
48%
Develop and document best practices, tutorials, or training materials for researchers to effectively use infrastructure tools (e.g., Jupyter notebooks, SLURM job schedulers, or data management platforms)
16%DD
49%
Monitor system performance, security, and compliance (e.g., tracking resource usage, patching vulnerabilities, or ensuring adherence to data governance policies)
8%DD
68%
Coordinate with IT teams, vendors, or external partners to procure, deploy, or upgrade hardware/software (e.g., negotiating contracts, overseeing installations, or testing new systems)
deep expertisesocial core
6%AA
0%
TaskTimeTypeExposure
Design and maintain cloud-based research computing environments (e.g., setting up virtual machines, configuring storage solutions, and managing access controls for research teams)
25%DD
50%
Develop and document best practices, tutorials, or training materials for researchers to effectively use infrastructure tools (e.g., Jupyter notebooks, SLURM job schedulers, or data management platforms)
23%DD
48%
Troubleshoot and resolve technical issues reported by researchers, such as software installation problems, data transfer failures, or performance bottlenecks in computing clusters
deep expertisesocial element
16%AD
14%
Collaborate with researchers to optimize workflows for high-performance computing (HPC) or data-intensive tasks, including scripting, parallelization, or containerization (e.g., Docker, Singularity)
deep expertisesocial core
13%AD
14%
Monitor system performance, security, and compliance (e.g., tracking resource usage, patching vulnerabilities, or ensuring adherence to data governance policies)
11%DD
93%
Coordinate with IT teams, vendors, or external partners to procure, deploy, or upgrade hardware/software (e.g., negotiating contracts, overseeing installations, or testing new systems)
some context neededsocial core
9%AA
0%
Evaluate and recommend new tools or technologies (e.g., AI/ML frameworks, data visualization software, or cloud services) to improve research efficiency and scalability
deep expertisesocial element
1%DD
33%
TaskTimeTypeExposure
Develop and document best practices, tutorials, or training materials for researchers to effectively use infrastructure tools (e.g., Jupyter notebooks, SLURM job schedulers, or data management platforms)
25%DD
49%
Design and maintain cloud-based research computing environments (e.g., setting up virtual machines, configuring storage solutions, and managing access controls for research teams)
22%DD
44%
Monitor system performance, security, and compliance (e.g., tracking resource usage, patching vulnerabilities, or ensuring adherence to data governance policies)
19%DD
92%
Troubleshoot and resolve technical issues reported by researchers, such as software installation problems, data transfer failures, or performance bottlenecks in computing clusters
deep expertisesocial core
18%AD
16%
Evaluate and recommend new tools or technologies (e.g., AI/ML frameworks, data visualization software, or cloud services) to improve research efficiency and scalability
deep expertisesocial element
8%DD
31%
Collaborate with researchers to optimize workflows for high-performance computing (HPC) or data-intensive tasks, including scripting, parallelization, or containerization (e.g., Docker, Singularity)
deep expertisesocial core
5%AD
11%
Coordinate with IT teams, vendors, or external partners to procure, deploy, or upgrade hardware/software (e.g., negotiating contracts, overseeing installations, or testing new systems)
deep expertisesocial core
1%AA
0%
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AI tools for this role
Tools relevant to the most automatable tasks in this profession.
