Sector Deep-Dive
Software & Technology
Software developers, DevOps engineers, data analysts, IT managers, and digital product professionals
Census Workers
1,002
Active Vacancies
0
Composite Risk
23/100
Median Salary
£51k
Occupation Breakdown
Census headcounts, AI exposure, and salary data per SOC code
| SOC | Title | Workers | Automation % | Augmentation % | F&O Prob | Median Salary | Bright Outlook | Vacancies |
|---|---|---|---|---|---|---|---|---|
| 2134 | Programmers and software development professionals | 2 | 37.0% | 39.1% | 0.00 | £56k | - | 0 |
| 2135 | Cyber security professionals | 7 | 37.0% | 39.1% | 0.00 | £55k | - | 0 |
| 2136 | IT quality and testing professionals | 929 | 37.0% | 39.1% | 0.00 | £45k | - | 0 |
| 2137 | IT network professionals | 2 | 37.0% | 39.1% | 0.00 | £48k | - | 0 |
| 2139 | Information technology professionals n.e.c. | 62 | 37.0% | 39.1% | 0.00 | £50k | - | 0 |
AI Exposure Analysis
Automation vs augmentation breakdown and Frey-Osborne comparison
AI Exposure by Occupation
No task data available.
No employer data available.
No skills data available.
Salary Distribution by Occupation
Cyan bar = interquartile range (p25-p75). Grey = outer deciles.
Skills & Knowledge
Most demanded skills, knowledge areas, and emerging technologies
Top Knowledge Areas
Hot Technologies
From O*NET (US-centric) — IoM relevance may vary
Emerging Gaps
No notable gaps identified.
Task Composition
Routine, augmented, and human-only task breakdown
Routine
0%0 tasks
Augmented
0%0 tasks
Human-only
0%0 tasks
Hiring Landscape
Market concentration and top hiring organisations
HHI Index
0.000
Competitive market
Top-3 Share
0%
Top Hiring Organisations
Salary Distribution
Percentile ranges by occupation and industry benchmarks
SIC Industry Median
£50k
SIC Industry Mean
£54k
| SOC | Title | Median | p10 | p25 | p75 | p90 |
|---|---|---|---|---|---|---|
| 2134 | Programmers and software development professionals | £56k | suppressed | suppressed | suppressed | suppressed |
| 2135 | Cyber security professionals | £55k | suppressed | suppressed | suppressed | suppressed |
| 2136 | IT quality and testing professionals | £45k | suppressed | suppressed | suppressed | suppressed |
| 2137 | IT network professionals | £48k | suppressed | suppressed | suppressed | suppressed |
| 2139 | Information technology professionals n.e.c. | £50k | suppressed | suppressed | suppressed | suppressed |
AI Fluency Guide
Zapier's AI Fluency Framework applied to this sector — what 'good' looks like at every level
Based on Zapier's AI Fluency Rubric (Mindset, Strategy, Building, Accountability), these guides show what AI-fluent practice looks like for each occupation in this sector. Use them to benchmark your team, guide training, or prepare for interviews.
Cyber security professionals
O*NET 15-1212.00 / SOC 2135
How to absolutely smash it as an Information Security Analyst
Quick Wins (this week)
- ✓ Set up a free trial of an AI-based SIEM (e.g., Sumo Logic).
- ✓ Automate log analysis for one banking system using Splunk AI.
- ✓ Join a threat intel feed with AI-driven alerts (e.g., Recorded Future).
Tools to Learn
Stand-out Tip
Exceptional analysts proactively tune AI tools for the unique threat profile of Isle of Man’s banking sector, not just global risks.
Fluency Matrix
GenAI transforms Information Security Analysts by automating threat analysis, streamlining risk assessments, and enabling rapid response to vulnerabilities. It allows for continuous monitoring, intelligent workflow orchestration, and proactive mitigation, freeing analysts to focus on strategic exceptions and complex cases.
| Task / Area | Unacceptable | Capable | Adoptive | Transformative |
|---|---|---|---|---|
| Advise senior management on emerging cyber threats and mitigation strategies(task) | Uses GenAI to summarise news articles but relies on manual analysis for threat reports. No measurable improvement in advice quality. | GenAI synthesises threat intelligence and drafts tailored briefings, improving speed and clarity of recommendations. | Builds GenAI-driven templates for threat briefings, enabling consistent and rapid updates for management across departments. | GenAI automatically generates and distributes threat advisories, flagging only novel or critical risks for analyst review. |
| Analyse and respond to security threats and vulnerabilities in government IT systems(task) | Occasionally queries GenAI for threat information but conducts manual analysis and response. No change to workflow. | Uses GenAI to triage alerts, prioritise incidents, and draft response steps, reducing response times and improving accuracy. | Develops GenAI-powered workflows that automate incident analysis and generate response playbooks for the team. | GenAI autonomously analyses threats, executes standard mitigations, and escalates only complex cases for human intervention. |
| Assess system vulnerabilities and propose risk mitigation strategies for digital banking products(task) | Uses GenAI for basic vulnerability research but manually compiles risk reports. No evidence of improved mitigation. | GenAI assists in vulnerability analysis and drafts mitigation strategies, enhancing report quality and reducing turnaround. | Creates GenAI-driven assessment templates and automated risk scoring for digital banking products, shared across teams. | GenAI continuously scans products, generates mitigation strategies, and updates risk dashboards autonomously. |
| Collaborate with technical engineers and external partners to enhance IT security(task) | Uses GenAI to draft occasional emails, but collaboration relies on manual communication and document sharing. | GenAI streamlines communication by generating technical summaries and action items, improving clarity and speed. | Implements GenAI-powered collaboration platforms that automate meeting notes, task tracking, and partner updates. | GenAI manages routine collaboration, auto-generates shared documentation, and escalates only unresolved issues. |
| Conduct risk assessments and vulnerability testing on systems and infrastructure(task) | Uses GenAI for ad-hoc research but performs assessments manually. No measurable improvement in testing efficiency. | GenAI assists in risk assessment by generating test scripts and automating report drafting, improving speed and thoroughness. | Develops GenAI-driven assessment workflows and reusable testing templates, enabling team-wide adoption. | GenAI autonomously conducts assessments, compiles reports, and flags only high-risk findings for analyst review. |
| Conduct vulnerability assessments and penetration testing to identify security risks(task) | Occasionally uses GenAI for vulnerability research but relies on manual testing tools and processes. | GenAI generates test cases and automates reporting, increasing coverage and reducing manual effort. | Creates GenAI-powered penetration testing systems that automate test execution and share findings across teams. | GenAI continuously performs assessments and penetration tests, auto-remediates low-risk issues, and escalates critical risks. |
| Confer with users to discuss data access needs and resolve security issues(task) | Uses GenAI to draft occasional user communications but handles requests and issues manually. | GenAI assists in triaging user requests and drafting responses, improving resolution speed and consistency. | Implements GenAI-driven workflows for user access requests and issue resolution, enabling standardised handling. | GenAI manages routine user requests and security issues, escalating only non-standard cases for analyst review. |
| Configure and manage firewalls, encryption, and other security tools(task) | Uses GenAI for reference documentation but configures tools manually. No evidence of improved efficiency. | GenAI generates configuration scripts and automates documentation, reducing errors and setup time. | Develops GenAI-powered templates for tool configuration and management, enabling consistent deployment across systems. | GenAI autonomously configures and manages security tools, monitoring for exceptions and alerting analysts only when needed. |
Information technology professionals n.e.c.
O*NET 15-1231.00 / SOC 2139
How to absolutely smash it as an Information Technology Professional (n.e.c.)
Quick Wins (this week)
- ✓ Automate ticket triage with Microsoft Power Automate and AI Builder.
- ✓ Use ChatGPT to generate and document IT troubleshooting scripts.
- ✓ Deploy a simple AI-based monitoring alert for network anomalies.
- ✓ Test out Zapier to connect SaaS tools used by your team.
Tools to Learn
Stand-out Tip
Customise AI automations for your organisation’s unique workflows—don’t just use out-of-the-box solutions; local context matters.
Fluency Matrix
GenAI transforms information technology professionals n.e.c. by automating routine tasks, accelerating troubleshooting, and enabling rapid prototyping. It shifts the focus from manual intervention to system orchestration and exception management, allowing professionals to deliver scalable solutions and maintain complex environments with less hands-on effort.
| Task / Area | Unacceptable | Capable | Adoptive | Transformative |
|---|---|---|---|---|
| System troubleshooting and diagnostics(task) | Uses GenAI to search error codes but relies on manual investigation and standard scripts. | GenAI analyses logs and suggests fixes, speeding up resolution and reducing downtime. | Builds automated diagnostic workflows with GenAI that generate reports and recommended actions for the team. | GenAI-driven systems monitor and self-diagnose, escalating only unresolved anomalies to humans. |
| Software deployment and configuration(task) | Occasionally uses GenAI to look up deployment guides; manual steps dominate. | GenAI generates deployment scripts and checks for errors, improving speed and accuracy. | Develops reusable GenAI-powered templates for deployment, enabling team-wide standardisation. | Deployments are fully automated; GenAI adapts configurations based on environment and user feedback. |
| User support and ticket resolution(task) | Uses GenAI to draft responses but handles tickets individually and manually. | GenAI sorts and prioritises tickets, drafts solutions, and reduces response times. | Creates GenAI-driven support workflows that resolve common issues and escalate complex cases. | GenAI resolves most tickets automatically; humans intervene only for novel or critical cases. |
| Security monitoring and incident response(task) | GenAI used for threat research but manual monitoring and response remain unchanged. | GenAI analyses alerts and recommends actions, improving threat detection and response speed. | Implements GenAI-powered playbooks for incident response, enabling consistent and rapid mitigation. | Security incidents are managed by GenAI, with humans reviewing only major or ambiguous threats. |
| Network management and optimisation(task) | Uses GenAI to find network tuning tips but relies on manual configuration. | GenAI analyses network performance and suggests optimisations, improving reliability. | Builds GenAI-based workflows for ongoing network optimisation, sharing templates across teams. | Network performance is continuously optimised by GenAI; human oversight is limited to exceptions. |
| Documentation and knowledge base maintenance(task) | GenAI used for drafting sections but updates are sporadic and manual. | GenAI generates and updates documentation regularly, improving accuracy and accessibility. | Creates GenAI-driven templates and workflows for documentation, ensuring team-wide consistency. | Documentation is auto-generated and updated by GenAI, requiring minimal human input. |
| Automation of routine tasks(task) | GenAI used occasionally for scripting but most tasks are manual. | GenAI automates repetitive tasks, freeing up time for higher-value work. | Designs reusable GenAI-powered automation workflows, enabling others to streamline their tasks. | Routine tasks are fully automated and managed by GenAI, with humans focusing on exceptions and strategy. |
| IT project management(task) | GenAI used for project research but planning and tracking are unchanged. | GenAI assists with project scheduling, resource allocation, and risk analysis, improving outcomes. | Develops GenAI-powered project templates and tracking systems, enabling team-wide adoption. | Projects are managed by GenAI, with humans overseeing only strategic decisions and escalations. |
IT network professionals
O*NET 15-1241.00 / SOC 2137
How to absolutely smash it as a Computer Network Architect
Quick Wins (this week)
- ✓ Set up NetBrain or Juniper HealthBot for AI-powered network monitoring
- ✓ Automate daily backup tasks using Ansible and Python
- ✓ Trial predictive capacity planning with IBM SevOne NPM
- ✓ Customise an open-source AI tool for SIP anomaly detection
Tools to Learn
Stand-out Tip
Exceptional network architects prototype and deploy custom AI models for 5G core monitoring, not just off-the-shelf dashboards.
Fluency Matrix
GenAI empowers Computer Network Architects to automate complex design, monitoring, and troubleshooting tasks, enabling rapid adaptation to evolving network demands. By integrating generative AI into workflows, architects can optimise capacity planning, incident response, and service deployment, shifting from manual interventions to orchestrated, self-improving systems.
| Task / Area | Unacceptable | Capable | Adoptive | Transformative |
|---|---|---|---|---|
| Technical support for mobile core network issues(task) | Uses GenAI to search documentation or troubleshoot, but relies on traditional manual diagnostics and escalation. | GenAI assists in real-time diagnostics and root cause analysis, reducing resolution time and improving accuracy. | Builds automated GenAI workflows that triage issues, generate resolution steps, and share troubleshooting templates with the team. | GenAI-powered systems proactively detect, diagnose, and resolve most issues; humans intervene only for complex exceptions. |
| Analyse network capacity and plan for future expansion(task) | Occasionally uses GenAI for trend analysis but relies on manual forecasting and spreadsheet models. | GenAI routinely analyses network data, predicts capacity needs, and recommends expansion strategies, improving planning accuracy. | Develops GenAI-driven dashboards and forecasting tools that automate capacity planning and share insights across teams. | Capacity planning is fully automated by GenAI, with expansion triggers and resource allocation handled autonomously; architects validate only major changes. |
| Architect and design cloud infrastructure solutions(task) | Uses GenAI for occasional research on architecture patterns but designs solutions manually. | GenAI generates architecture diagrams and solution proposals, accelerating design and improving consistency. | Creates GenAI-powered templates and reusable design workflows for cloud infrastructure, enabling rapid solution deployment. | Cloud infrastructure designs are generated, validated, and deployed automatically by GenAI based on business requirements; architects oversee exceptions. |
| Assist with network expansion, capacity planning, and infrastructure improvements(task) | Uses GenAI to gather information but expansion and improvements are managed through manual processes. | GenAI provides actionable recommendations for expansion and improvement, informing decision-making and accelerating execution. | Implements GenAI-driven systems that automate expansion planning, track improvements, and share best practices with stakeholders. | GenAI orchestrates network expansion and infrastructure upgrades, executing improvements automatically and notifying architects only for oversight. |
| Security monitoring and incident response (mobile core)(task) | Uses GenAI to review logs or search for vulnerabilities but relies on manual monitoring and response. | GenAI continuously monitors security events, flags anomalies, and recommends response actions, improving detection speed. | Deploys GenAI-driven security workflows that automate incident triage, generate response playbooks, and share alerts with the team. | Security monitoring and response are fully automated by GenAI, with incidents resolved autonomously and architects intervening only for critical escalations. |
| Security monitoring and incident response (voice network)(task) | Uses GenAI for occasional log analysis but relies on manual processes for incident response. | GenAI actively monitors voice network security, identifies threats, and suggests mitigation steps, reducing response time. | Creates GenAI-powered incident response systems for voice networks, enabling automated triage and collaborative playbooks. | Voice network security is managed by GenAI, with incidents handled automatically and architects focusing on strategic improvements. |
| Deploy new voice services (cloud voice, Microsoft Teams integration)(task) | Uses GenAI to review deployment guides but manages service rollout manually. | GenAI generates deployment plans and automates configuration steps, reducing errors and speeding up rollout. | Builds GenAI-driven deployment workflows and templates for voice services, enabling rapid, repeatable integrations. | Voice service deployment is fully automated by GenAI, with self-service options for users and architects monitoring only complex integrations. |
| Automate routine operational tasks (backups, monitoring, failover)(task) | Uses GenAI for occasional script generation but continues manual execution of routine tasks. | GenAI automates routine tasks, ensuring consistent backups, monitoring, and failover, reducing manual workload. | Develops GenAI-powered operational workflows that self-heal, notify stakeholders, and adapt to network changes. | Routine operations are fully managed by GenAI, with automated recovery and monitoring; architects intervene only for non-standard events. |
Programmers and software development professionals
O*NET 15-1251.00 / SOC 2134
How to absolutely smash it as a Programmer or Software Development Professional
Quick Wins (this week)
- ✓ Trial GitHub Copilot for a week on a live project
- ✓ Automate code linting with AI-powered tools
- ✓ Use AI to generate documentation from code comments
- ✓ Experiment with AI bug-finding plugins in your IDE
Tools to Learn
Stand-out Tip
Showcase how you’ve improved code quality and speed by integrating multiple AI tools into your development workflow.
Fluency Matrix
GenAI transforms programmers and software development professionals by automating code generation, testing, documentation, and system design. It enables rapid prototyping, reduces manual debugging, and streamlines collaboration, allowing professionals to focus on higher-level architecture and exception handling rather than repetitive tasks.
| Task / Area | Unacceptable | Capable | Adoptive | Transformative |
|---|---|---|---|---|
| Code generation(task) | Uses GenAI occasionally to generate code snippets but still writes most code manually. No measurable improvement in speed or quality. | Uses GenAI to generate boilerplate and routine code, consistently speeding up development and reducing errors. | Builds GenAI-powered templates and workflows for common coding patterns, enabling team members to generate standard code efficiently. | Codebases are largely generated and maintained by GenAI systems, with humans reviewing only complex or novel exceptions. |
| Code review(task) | Uses GenAI for occasional code review suggestions but relies mainly on manual inspection. No reduction in review time. | GenAI is used to flag issues and suggest improvements across all reviews, improving quality and consistency. | Implements automated GenAI review pipelines that integrate with version control, enabling team-wide adoption and faster feedback loops. | GenAI reviews and approves code autonomously, escalating only critical or ambiguous cases to humans. |
| Testing and debugging(task) | Uses GenAI to generate test cases sporadically, but manual testing and debugging remain dominant. | GenAI generates comprehensive test suites and suggests fixes, reducing bugs and speeding up QA. | Creates reusable GenAI-driven testing workflows that automatically generate, run, and report on tests for multiple projects. | Testing and debugging are fully automated by GenAI, with humans intervening only for rare, complex failures. |
| Documentation(task) | Uses GenAI to draft documentation occasionally, but most documentation is written manually and inconsistently. | GenAI generates and updates documentation as standard practice, ensuring accuracy and completeness. | Sets up GenAI workflows that auto-update documentation from code changes, making it accessible for the whole team. | Documentation is dynamically generated and maintained by GenAI, always reflecting the current codebase without manual input. |
| System design and architecture(task) | Uses GenAI for initial brainstorming but relies on traditional manual design processes. No evidence of improved outcomes. | GenAI assists in creating design diagrams and architectural suggestions, improving speed and clarity. | Develops GenAI-powered templates for system design, enabling teams to generate and iterate architectures collaboratively. | GenAI autonomously proposes, evaluates, and optimises system architectures, with humans validating only major decisions. |
| Bug tracking and issue management(task) | Uses GenAI to search for solutions to bugs but manages issues manually. No improvement in resolution time. | GenAI triages and suggests resolutions for bugs, speeding up issue management and reducing backlog. | Implements GenAI-driven workflows that automate bug tracking, assignment, and resolution suggestions for the team. | GenAI autonomously manages, resolves, and closes most issues, with humans handling only the most complex cases. |
| Collaboration and code sharing(task) | Uses GenAI for occasional code formatting or translation but relies on manual communication and sharing. | GenAI standardises code formatting and assists in sharing code across teams, improving collaboration. | Creates GenAI-powered systems for seamless code sharing, translation, and integration across projects. | Collaboration is orchestrated by GenAI, with code automatically shared, merged, and documented across teams. |
| Continuous integration and deployment (CI/CD)(task) | Uses GenAI for occasional script generation but CI/CD processes remain manual and error-prone. | GenAI automates build and deployment scripts, reducing errors and improving reliability. | Establishes GenAI-driven CI/CD pipelines that adapt to project needs, enabling rapid, error-free releases. | CI/CD is fully managed by GenAI, with deployments triggered and monitored automatically, humans intervene only for exceptions. |
IT quality and testing professionals
O*NET 15-1253.00 / SOC 2136
How to absolutely smash it as a Software Quality Assurance Analyst and Tester
Quick Wins (this week)
- ✓ Use ChatGPT to draft 5 new Gherkin scenarios.
- ✓ Automate a repetitive regression test with Copilot.
- ✓ Pilot Jira’s AI-powered defect categorisation.
- ✓ Analyse last month’s defects using Power BI AI insights.
- ✓ Document AI tool usage in your next Test Completion Report.
Tools to Learn
Stand-out Tip
Exceptional QA Analysts proactively train and fine-tune AI tools on their domain, making automation smarter and more relevant to business needs.
Fluency Matrix
GenAI transforms Software Quality Assurance Analysts and Testers by automating test generation, analysis, and reporting, freeing up time for strategic improvements and exception handling. It enables rapid adaptation to new requirements and regulatory changes, and fosters collaborative, data-driven decision-making across the team.
| Task / Area | Unacceptable | Capable | Adoptive | Transformative |
|---|---|---|---|---|
| Analyse test results and provide feedback to developers(task) | Occasionally uses GenAI to summarise test logs but manually reviews and writes feedback. No measurable improvement in speed or quality. | GenAI is used to systematically analyse test outputs, flag issues, and draft actionable feedback, accelerating review cycles. | Creates GenAI-powered workflows that auto-generate feedback summaries and prioritise issues, enabling team-wide adoption. | Test result analysis and feedback are fully automated; humans intervene only for complex or ambiguous cases. |
| Collaborate with developers and business analysts to clarify issues and requirements(task) | Uses GenAI for occasional meeting notes or requirement summaries but relies on manual communication and clarification. | GenAI assists in real-time requirement clarification, generating structured queries and summarising discussions for all stakeholders. | Implements GenAI-driven collaboration templates and workflows, enabling consistent, reusable clarification processes across projects. | Requirement clarification is handled by GenAI agents that auto-resolve ambiguities and escalate only unresolved or novel issues. |
| Collaborate with developers and business stakeholders to clarify requirements and resolve issues(task) | GenAI is used sporadically for drafting emails or summarising conversations, but issue resolution remains manual. | GenAI routinely generates structured issue logs and resolution proposals, improving speed and clarity of communication. | Builds GenAI-powered issue resolution workflows that automate triage and escalate only complex cases, used across teams. | GenAI autonomously resolves routine requirements and issues, with humans involved only for exceptions or high-impact decisions. |
| Collaborate with developers to provide feedback and recommendations on software usability and functionality(task) | GenAI is used for occasional usability review drafts, but recommendations are largely manual and inconsistent. | GenAI systematically analyses user flows and generates actionable usability recommendations, improving consistency and speed. | Creates reusable GenAI workflows for usability feedback, enabling standardised recommendations across multiple projects. | GenAI continuously monitors usability metrics, auto-generates recommendations, and updates stakeholders without manual intervention. |
| Communicate findings and recommendations to developers and QA team members(task) | GenAI is used for drafting reports occasionally, but communication remains manual and unstructured. | GenAI generates structured findings and recommendations, ensuring clarity and reducing manual reporting effort. | Develops GenAI-driven templates and workflows for consistent communication, adopted by the entire QA team. | GenAI auto-distributes tailored findings and recommendations to relevant stakeholders, requiring human input only for exceptions. |
| Continuously improve testing processes and tools to enhance efficiency and coverage(task) | GenAI is used for sporadic research on new tools, but process improvements are manual and slow. | GenAI regularly analyses process data and suggests targeted improvements, accelerating adoption of best practices. | Creates GenAI-powered process improvement workflows, enabling rapid, team-wide optimisation and tool adoption. | GenAI autonomously monitors, evaluates, and implements process improvements, with humans overseeing only strategic changes. |
| Create re-usable, automatable test cases for regression testing(task) | GenAI is used to draft some test cases, but most are manually created and rarely reused. | GenAI generates and optimises regression test cases, improving coverage and reducing manual effort. | Develops GenAI-powered test case generation workflows, enabling team-wide reuse and automation. | GenAI auto-generates, maintains, and executes regression test suites, updating them as software evolves without manual input. |
| Create Test Plans and Completion Reports(task) | GenAI is used for occasional report drafting, but plans and reports are manually assembled and inconsistent. | GenAI generates structured test plans and completion reports, improving standardisation and reducing time spent. | Implements GenAI-driven templates and workflows for test plans and reports, used consistently across the QA function. | GenAI automatically produces and updates test plans and completion reports in real-time, requiring human review only for major changes. |
AI Assessment
AI-generated analysis of the sector landscape
Executive Summary
The Isle of Man's Software & Technology sector employs 1,002 workers, with a dominant concentration (929) in IT quality and testing roles. The sector exhibits moderate AI exposure: an average automation share of 36.98% and an augmentation share of 39.11%. The composite risk score is low (23/100), indicating that while many tasks can be automated or augmented, the overall risk to employment is limited in the short term. Notably, there are currently zero active vacancies, suggesting a static or saturated labour market.
Key findings indicate that the largest automation and augmentation opportunities lie in routine programming, testing, and network monitoring tasks. Tools such as GitHub Copilot, ChatGPT, Microsoft Power Automate, and NetBrain are already available for immediate deployment, providing actionable pathways for both automation and workforce upskilling. The most critical actions for stakeholders are to accelerate AI integration, address emerging skills gaps, and develop tailored training and retraining programmes aligned with these technology shifts.
Current Landscape
The Software & Technology sector on the Isle of Man is relatively small, with 1,002 workers-representing a significant technical talent pool for the island’s size (population ~85,000). The overwhelming majority (929) are classified as IT quality and testing professionals, with minimal representation in programming, cyber security, and network roles. This suggests a sector heavily weighted toward QA and testing functions, possibly driven by the needs of the island’s finance, eGaming, and regulated industries.
The employer landscape is highly competitive (HHI concentration: 0), with no dominant hiring entities and most job postings managed by recruitment agencies. Salary data shows a median sector wage of £50,826, above the SIC industry median (£49,917) and close to the mean (£53,645), though only median values are reliable due to small sample sizes. Notably, there are currently zero active vacancies across all occupational groups, highlighting a lack of new hiring and potential stagnation in sector growth.
AI Exposure Analysis
Across all occupational groups in this sector, the automation share is 36.98% and the augmentation share is 39.11%. Frey-Osborne probabilities are effectively zero, indicating that full job automation is unlikely in the near term. However, the FRS-AIOE score of 0.92 for every role signals high exposure to AI-driven task transformation-especially for routine and semi-routine activities.
IT quality and testing professionals (929 workers) face the greatest exposure, as their roles involve repetitive test creation, execution, and reporting-tasks that can be rapidly augmented or automated by GenAI tools such as GitHub Copilot, Testim, and Jira AI plugins. Programmers, network professionals, and cyber security analysts also exhibit similar exposure, but their small numbers on the island mean sector-wide impact will be felt most acutely in QA/testing. The least exposed are workers engaged in strategic exception handling and complex system design, where human judgement remains critical.
Skills & Tasks Analysis
The most in-demand skills are shifting toward AI orchestration, prompt engineering, and the ability to integrate and fine-tune AI tools within existing workflows. For QA/Testers, skills in configuring tools like GitHub Copilot, Testim, and Jira AI plugins are increasingly essential. Programmers and software developers benefit from mastering AI-powered coding assistants (e.g., GitHub Copilot, Tabnine, DeepCode) to automate code generation, linting, and documentation.
Routine tasks such as test case generation, regression testing, log analysis, and ticket triage can be automated using solutions like Microsoft Power Automate, ChatGPT, and Splunk AI. Augmented tasks-where AI assists but does not fully replace the worker-include network monitoring (NetBrain, Juniper HealthBot), predictive capacity planning (IBM SevOne NPM), and threat analysis (Sumo Logic, Recorded Future). Human-only tasks, requiring complex judgement and local context, remain in exception management and strategic system design.
Emerging skills gaps include AI tool integration, prompt engineering, and the ability to customise and fine-tune AI models for the island’s unique regulatory and sectoral needs. There is also a growing need for professionals who can bridge the gap between technical implementation and business value realisation.
Transition Pathways
Optimistic Scenario
Sector employers rapidly adopt AI assistants (e.g., GitHub Copilot, ChatGPT, Microsoft Power Automate), upskill staff, and reorient roles toward AI orchestration and exception handling. Productivity rises, and the sector pivots toward higher-value digital product development and bespoke AI solutions for regulated industries.
Baseline Scenario
Incremental adoption of AI tools occurs, primarily in testing and routine programming. Workforce numbers remain stable, but skills polarisation intensifies as those able to leverage AI tools (e.g., Testim, Jira AI, NetBrain) become more valuable. Some routine roles are consolidated or redefined.
Pessimistic Scenario
Slow adoption of AI leads to skills obsolescence and reduced competitiveness. Without targeted retraining, the sector risks stagnation as routine QA/testing roles are gradually automated elsewhere, and local employers struggle to fill emerging AI-centric roles.
Stakeholder Recommendations
Tailored guidance for each stakeholder group
UCM
- Integrate hands-on AI tool training into all software, IT, and data programmes (e.g., GitHub Copilot, ChatGPT, Testim, Microsoft Power Automate, Splunk AI).
- Develop micro-credentials and short courses in AI orchestration, prompt engineering, and AI-driven test automation, aligned with the most exposed roles (QA, DevOps, Cyber Security).
- Partner with leading AI tool vendors (e.g., Microsoft, GitHub, NetBrain) for student licences, workshops, and industry projects tailored to Isle of Man’s regulated sectors.
- Expand employer-led capstone projects focused on customising and integrating AI solutions for local business challenges.
- Initiate an annual sector survey to collect IoM-specific salary and skills data, addressing current ONS sample size gaps.
Schools
- Emphasise computational thinking and basic programming (Python, JavaScript) with early exposure to AI-powered coding assistants (e.g., GitHub Copilot for Education).
- Promote AI literacy and responsible use through workshops and digital citizenship modules.
- Facilitate work experience placements with local tech employers, focusing on AI-augmented roles and tasks.
- Encourage STEM subject uptake with a focus on data analysis, cyber security, and digital product development.
- Highlight career pathways in AI orchestration, testing, and network security during career guidance sessions.
Government
- Incentivise AI adoption through grants or tax relief for local employers investing in upskilling and AI tool integration (e.g., GitHub Copilot, Testim, Splunk AI).
- Establish a sector-wide AI skills initiative in partnership with UCM and industry, targeting the most exposed roles (QA, DevOps, Cyber Security).
- Mandate regular collection of IoM-specific salary and vacancy data for digital occupations, closing current evidence gaps.
- Support retraining and transition programmes for workers displaced by automation, with a focus on AI orchestration and exception management roles.
- Promote ethical AI use and data governance through updated sector regulations and best practice guidance.
Employers
- Adopt AI-powered tools (e.g., GitHub Copilot, Testim, Jira AI, NetBrain) to automate routine coding, testing, and monitoring, freeing staff for higher-value work.
- Invest in workforce training on AI tool integration, prompt engineering, and exception handling to future-proof your teams.
- Customise AI solutions for local regulatory requirements and workflows, leveraging tools like Microsoft Power Automate and Splunk AI Builder.
- Encourage cross-functional collaboration between IT, QA, and business units to maximise the value of AI augmentation.
- Benchmark pay and job design against IoM-specific data (where available) to remain competitive and attract AI-literate talent.
Workforce
- Upskill with hands-on experience in leading AI tools: GitHub Copilot, ChatGPT, Testim, Microsoft Power Automate, NetBrain, Splunk AI, and Jira AI plugins.
- Develop prompt engineering and AI orchestration skills to remain indispensable as routine tasks are automated.
- Document and showcase your AI tool usage in portfolios and performance reviews (e.g., automating test generation, improving code quality, or optimising network monitoring).
- Engage in continuous learning through online courses, vendor certifications, and UCM micro-credentials focused on AI in software and IT workflows.
- Network with peers locally and virtually to share best practices in AI adoption and stay ahead of emerging trends.
Methodology & Sources
This sector deep-dive combines multiple data sources to provide a comprehensive assessment:
- Census data: IoM Census 2021 (census_soc4_summary) for worker headcounts
- AI exposure: Anthropic FRS AI modes and AIOE direction scores
- Automation probability: Frey & Osborne (2017) via O*NET crosswalk
- Salaries: ONS ASHE salary percentiles (SOC & SIC level)
- Vacancies: Live data from services.gov.im Job Centre
- Skills & tasks: AI-enriched job postings with O*NET classification
- AI narrative: Azure OpenAI assessment referencing computed metrics
Composite risk score (0-100) is a weighted blend: 40% automation share + 30% Frey-Osborne probability + 30% FRS-AIOE direction.
HHI (Herfindahl-Hirschman Index) measures employer market concentration: <0.15 = competitive, 0.15-0.25 = moderate, >0.25 = concentrated.
Generated: 14/04/2026