Top 5 Jobs in Government That Are Most at Risk from AI in Bermuda - And How to Adapt

Bermuda's public sector can no longer treat AI as a distant threat - generative AI already creates text, images and summaries, powers chat agents, and automates routine work, which places receptionists, data‑entry and records clerks, call‑centre staff, communications officers, financial analysts and policy researchers squarely at risk.

These models can synthesize long reports, auto‑draft FAQs and speed forecasting, so a small office could see repetitive tasks vanish “like a virtual clerk summarizing a week of paperwork in seconds.” Learn how generative systems work and common public‑sector use cases in the Google Cloud generative AI use cases primer (Google Cloud generative AI use cases primer), and consider practical reskilling: Nucamp's Nucamp AI Essentials for Work bootcamp registration teaches workplace prompt skills, verification, and safe pilots to help Bermudian civil servants adapt.

"VAEs opened the floodgates to deep generative modeling by making models easier to scale," said Akash Srivastava, an expert on generative AI at the MIT-IBM Watson AI Lab.

Methodology blended the ILO's task‑level index with local relevance checks: starting from the refined Global Index - built on nearly 30,000 real‑world tasks, thousands of worker assessments, expert validation and AI‑assisted scoring - we filtered roles most common in Bermuda's public sector and cross‑checked patterns reported for high‑income economies like Bermuda by The Royal Gazette and UN coverage.

Priority went to occupations with consistently high task exposure (clerical work, call‑centre functions, communications, finance and policy research) and to risks that reflect Bermuda's workforce profile, including the gendered exposure the ILO flagged; practical feasibility and local digital access were also weighed so the list highlights where routine paperwork or FAQs could be handled by a “virtual clerk” in seconds.

For readers planning pilots or reskilling, see the ILO/UN findings on exposure and The Royal Gazette's local analysis, plus our Practical AI steps for Bermuda beginners to move from insight to safe experiments.

“Transformation of jobs is the most likely impact of GenAI.”

Administrative and clerical roles - receptionists, data‑entry and records clerks - sit squarely in the front line for Bermuda's public sector as GenAI spreads: the ILO's task-level analysis finds clerical work has 24% of tasks highly exposed and another 58% at medium exposure, meaning roughly four in five routine clerical tasks could be reshaped by automation or augmentation, from auto‑populating records to drafting standardized responses; the UN flags that women and clerical workers face the highest risk of radical transformation, so Bermudian offices with large proportions of female staff should prioritise inclusive reskilling and participatory rollout plans.

Practical measures include running low‑risk pilots, upskilling toward oversight and data‑verification, and redesigning roles to emphasise judgement, relationship management and local knowledge - see the ILO global analysis for the task breakdown and the UN News coverage for policy implications, and consult our Practical AI steps for Bermuda beginners to start affordable, worker‑centred pilots.

“Transformation of jobs is the most likely impact of GenAI.”

Customer service officers - from call‑centre agents to citizen‑services staff and ticketing clerks - face a double‑edged opportunity in Bermuda's public sector: LLMs and RAG‑augmented assistants can cut routine work, speed responses and scale 24/7 services, yet they must be grounded, monitored and privacy‑aware to avoid hallucinations that erode trust; practical pilots should prioritise Retrieval‑Augmented Generation for accurate, up‑to‑date answers (see deepsense.ai on why RAG is the “last mile”), domain‑specific evaluation to measure faithfulness, completeness and coherence (the Observe.ai framework), and private or tightly governed models where citizen data is sensitive.

Applied well, automation can free agents for relationship work - one striking case saw appointment scheduling improve by 130% - but rollouts must pair AI with human escalation, QA and clear data governance so a virtual assistant helps rather than confuses a caller; start small with worker‑centred pilots and follow a local checklist like the Practical AI steps for Bermuda beginners to design accountable, measurable deployments.

"They have the best data engineering expertise we have seen on the market in recent years" - Elias Nichupienko, CEO, Advascale

Communications and media officers in Bermuda - press officers, editors, technical writers and proofreaders - face a unique squeeze as generative AI both eases production and chips away at trust: UNRIC warns that AI brings “powerful tools and significant threats to press freedom, integrity, and public trust,” citing cases like a 2024 deepfake that manipulated a journalist's voice and headline, and DCN documents how AI is quietly reshaping editorial authority and gatekeeping by nudging editors with performance metrics and automation.

Yet the same technologies that threaten authenticity can speed transcription, summarisation and multilingual access, tasks many newsrooms already use (see surveys summarising journalist adoption rates), so Bermudian communications teams should prioritise human‑in‑the‑loop workflows, transparent disclosure, and local editorial standards.

Start small with worker‑centred pilots, tie any automation to clear QA and provenance rules, and consult practical frameworks such as our Practical AI steps for Bermuda beginners to design accountable experiments that protect local voice while capturing efficiency gains.

Treat AI like a smart assistant who tends to be a bullshitter

Financial and analytical staff in Bermuda - from financial analysts and auditing assistants to brokerage and actuarial clerks - are squarely in the sights of AI that can reconcile hundreds of feeds, flag anomalies and refresh forecasts in real time; platforms that promise “close‑ready books on Day 2, not Day 10” show how continuous, AI‑native reconciliation can turn month‑end from a weekend of spreadsheet triage into an almost routine check, freeing humans for judgment, exceptions and policy advice.

AI models also turbocharge forecasting and scenario work, spotting patterns across market data and client transactions that would take teams days to assemble, while anomaly detection and predictive risk tools cut fraud and compliance exposure - see Safebooks' guide to AI‑native reconciliation and Coherent Solutions' overview of AI in financial modeling for how these systems scale.

That said, sensitive public finances demand tight governance: start with pilot scopes, closed LLMs for confidential data, clear audit trails, and role redesign so analysts move from number‑chasing to model oversight and stakeholder interpretation; for practical FP&A adoption tips see Vena's guide to using AI for forecasting.

Imagine a Bermudian treasury that spends less time matching ledgers and more time stress‑testing shocks - that's the strategic “so what” of prudent, worker‑centred adoption.

“When we think about why people are implementing AI‑based solutions, it's about trying to free time up with automation to be able to do more value‑added, strategic‑thinking tasks.” - Rob Drover, Vena (quoted in Vena Solutions)

Policy and research assistants in Bermuda - policy researchers, management analysts and statistical assistants - are especially exposed because generative AI is already helping with the core activities these roles perform: gathering information, drafting and summarising reports, and routine monitoring and data pulls, as shown in the Microsoft Research analysis of generative AI workplace impact (Microsoft Research analysis of generative AI workplace impact).

At the same time, a Stanford study of worker preferences stresses that employees want automation that cuts repetitive work but preserves agency, oversight and partnership with AI (Stanford study on worker preferences for AI in the workplace), which matters for Bermuda's small, tightly staffed ministries: pilots should split tasks into “automatable” research and drafting versus human-led judgment, and invest in verification, clear escalation paths and communication skills so statistical assistants spend less time number‑wrangling and more time explaining trade‑offs to policymakers - the very skills the Stanford team expects to gain value as AI reshapes work.

“Our research shows that AI supports many tasks, particularly those involving research, writing, and communication, but does not indicate it can fully perform any single occupation.” - Kiran Tomlinson, Microsoft Research

Conclude with practical, Bermuda‑specific steps: open social dialogue now (workers know their jobs best) to map which tasks in each ministry are automatable and which require human judgement, run small worker‑centred pilots that pair human oversight with closed models for sensitive data, and prioritise reskilling for clerical roles where women are disproportionately exposed - a point underscored by the ILO coverage in The Royal Gazette (Royal Gazette: Bermuda among high‑income countries at risk from AI).

Align pilots and procurement with emerging local governance expectations: adopt board accountability, proportional risk assessments and model validation consistent with the BMA discussion paper framing (see the Grant Thornton summary of the BMA consultation Grant Thornton summary of BMA AI governance consultation).

For immediate workforce impact, invest in practical prompt and verification skills for non‑technical staff - for example, Nucamp's AI Essentials for Work bootcamp trains workplace promptcraft, verification and safe pilots (Register for Nucamp AI Essentials for Work bootcamp) - so government teams can turn disruption into opportunity while keeping privacy (PIPA), audit trails and human‑in‑the‑loop review front and centre.

“As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI.”