Industry Report
22 professions analyzed in the computer & mathematical sector. Average automation risk: 42%.
22
Professions
42%
Avg Automation Risk
0
High Risk
63
Avg Fossil Score
AI is automating actuarial modelling and data work rapidly. The regulatory requirement for human sign-off protects the credential — but the work behind it is changing fast.
AI can write code and run experiments, but formulating genuinely novel research questions, designing studies, and advancing the field's understanding still require a trained researcher.
Automated provisioning and network monitoring have absorbed the routine work, but designing networks for complex security requirements, hybrid cloud environments, and resilience under failure is still an expert job.
Automated monitoring tools handle most routine diagnostics, but troubleshooting real-world failures that don't match known patterns still requires someone with hands-on network experience.
AI coding assistants can generate working code from descriptions, but debugging complex systems, designing software architecture, and making security-aware implementation decisions still need an experienced programmer.
Documentation generation and requirements gathering are being streamlined, but translating ambiguous business needs into coherent systems design and navigating the organisational complexity of enterprise IT still requires experienced judgment.
Chatbots handle most password resets and known-issue FAQs, but diagnosing problems that span hardware, software, and user error — and supporting the non-technical users who struggle most — still needs a patient human with real diagnostic skill.
Standard reporting and dashboards are being automated by AI tools that let business stakeholders query data in plain English. The analyst who defines the right questions, interprets results in business context, and drives decisions rather than reports is in a stronger position.
AutoML and AI coding assistants are lowering the barrier for building models, but defining the right problem, understanding the business context, and translating statistical findings into decisions that organisations actually act on is still distinctly human work.
Cloud-managed database services have automated a large part of routine DBA work — backups, patching, scaling, and performance tuning assistance are now platform features. DBAs who understand the platform deeply, manage complex environments, and handle security and architecture decisions are in a much more durable position than those doing only routine maintenance.
AI-assisted schema generation and platform recommendations can suggest database designs, but the architectural decisions that shape how data flows across a system — choosing between relational, document, and analytical models; designing for scale, compliance, and integration — still require a human who understands the whole technical and business context.
AI is changing how information security analysts work day to day. Learning to use these tools isn't a nice-to-have anymore — it's becoming part of the job.
AI handles routine computation, literature search, and standard modelling. Mathematical scientists who do novel theoretical work or complex problem formulation are well positioned — those doing repetitive applied analysis face real pressure.
AI helps mathematicians do their jobs better and faster, but it can't replace the human skills at the heart of this work.
Network and Computer Systems Administrators are in a strong position. The core of this job — working with people, making judgment calls, solving unique problems — is hard for AI to touch.
AI helps operations research analysts do their jobs better and faster, but it can't replace the human skills at the heart of this work.
Software Developers are in a strong position. The core of this job — working with people, making judgment calls, solving unique problems — is hard for AI to touch.
Junior coding tasks are under pressure. Systems architecture and complex problem-solving are not.
AI helps software quality assurance analysts and testers do their jobs better and faster, but it can't replace the human skills at the heart of this work.
AI helps statisticians do their jobs better and faster, but it can't replace the human skills at the heart of this work.
AI helps web and digital interface designers do their jobs better and faster, but it can't replace the human skills at the heart of this work.
AI is changing how web developers work day to day. Learning to use these tools isn't a nice-to-have anymore — it's becoming part of the job.
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