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

Billings government workers should pay attention: global research shows generative AI is already reshaping white‑collar roles, with the ILO 2025 update on generative AI and jobs finding

“one in four workers” face some degree of exposure

and most jobs likely to be transformed rather than simply eliminated; other analyses warn that routine entry‑level office tasks are especially vulnerable and that municipalities are adopting AI tools like chatbots and no‑code automation to cut costs.

For Billings this means scheduling, form processing, basic reporting and first‑line inquiries could be automated unless staff adapt - a practical local example is how no‑code automation for small local agencies in Billings scales services without big IT budgets.

One concrete step: the AI Essentials for Work bootcamp registration (15 weeks) teaches prompt writing and on‑the‑job AI skills to keep roles relevant and productive.

Methodology: selection prioritized roles most exposed to routine, high‑volume tasks and public‑facing processes by combining sector signals and local use cases - industry sessions on “The Art of the Possible with AI” and AI's role in faster knowledge work (Oliver Wyman) were paired with Billings‑focused examples of no‑code automation, fraud‑detection prompts, and data‑stewardship needs from Nucamp research to build five clear criteria: routineness, transaction volume, documentation intensity, citizen contact, and inventory/logistics exposure.

The result: jobs that handle scheduling, intake forms, repeatable reporting or first‑line inquiries rose to the top because those functions are already the target of chatbot and no‑code workflows in Billings, meaning upskilling in prompt design and automation is the practical defense.

Sources that shaped the approach include Oliver Wyman's AI programming and the Nucamp guides to local government AI use cases and prompts.

“Go All In”

Office clerks and administrative assistants in Billings are especially exposed because their day‑to‑day - scheduling, processing intake forms, routing mail and routine correspondence, compiling meeting minutes and basic reports - matches exactly what chatbots, RPA and no‑code workflows automate; the O*NET profile for Executive Secretaries and Executive Administrative Assistants documents these tasks and shows nearly universal daily phone (99%) and e‑mail (97%) activity, heavy repetition, and frequent document handling, making many functions automatable (O*NET Executive Secretaries and Executive Administrative Assistants (43-6011.00) profile).

That matters: median wages for these roles ($74,260 annual, BLS 2024 data) mean changes will affect municipal budgets and experienced workers, and local agencies are already adopting scaled, low‑cost automation in Billings to speed service delivery - so staff who learn to oversee, prompt and audit these tools preserve both job value and public trust (no-code automation for small local agencies in Billings).

Accounting, auditing clerks and bookkeepers in Billings face clear exposure because the core of their work - high‑volume transaction matching, routine reconciliations, coding recurring expenses and generating standard reports - maps directly to the same chatbot, rule‑based and no‑code automation tools already used by local agencies; small Montana offices can scale these automations without big IT teams, so many hours of line‑by‑line processing are at risk unless staff shift to oversight and exception handling (no-code automation tools for small local government agencies in Billings).

That matters for public finance: automated anomaly flagging and fraud‑detection prompts help protect taxpayer dollars but require human audit trails and governance to work responsibly (welfare fraud detection and anomaly flagging systems for Billings government), and Montana's clear data stewardship requirements for local governments in Montana mean clerks who learn prompt auditing and policy compliance will preserve roles and strengthen municipal controls.

Customer service representatives who staff Billings' front desks and phone lines should prepare for rapid change: contact‑center AI is already automating routine FAQs, call routing, and multilingual web chat so agencies can handle surges without proportional staff increases - federal pilots at the Department of Labor and Veterans Affairs show AI routing and agent‑assist tools that triage simple inquiries and surface complex cases for humans (Department of Labor and Veterans Affairs federal contact center AI pilots), while state and local projects demonstrate that a small team can be overwhelmed by volume (Minnesota's Driver and Vehicle Services' 35‑person call center fields roughly 30,000 calls a week before AI augmentation).

That matters in Billings because routine intake and status checks - exactly the interactions AI excels at - are common across municipal services; without upskilling into oversight, exception handling and data stewardship, roles that now answer basic inquiries risk being reduced as agencies deploy low‑cost automation (no-code automation for small local agencies in Billings).

Policymakers are noticing too: proposed legislation aims to shield workers and consumers from poorly governed AI in call centers (Keep Call Centers in America Act to protect workers from AI-driven outsourcing), so the practical step for Billings staff is to learn prompt auditing and escalate only the cases that need human judgment - skills that keep careers relevant while improving citizen service.

"We're not looking to let go of any agents... we are going to utilize natural attrition because we do know that once we implement the AI, we're not going to need as many agents as we have, but natural attrition should be able to take care of that so that we're not having to actually sever anyone from their jobs."

Technical writers, records managers and reporting specialists in Billings face a twofold risk: generative AI can both produce polished drafts and silently leak confidential material if staff use public tools with sensitive records.

NSF warns about public exposure of uploaded content:

“any information uploaded into generative AI tools not behind NSF's firewall is considered to be entering the public domain,”

meaning a single pasted incident can become effectively public and harm privacy and legal compliance.

At the same time, federal practice shows AI is already replacing parts of documentation workflows - examples in the Department of State AI Inventory demonstrate FOIA indexing, NLP summarization, and automated metadata generation - so routine drafting, tagging and standard reporting steps in Billings are prime targets for automation.

The practical takeaway: keep sensitive text out of public LLMs, adopt approved, audit‑capable tools, and follow local data stewardship requirements so writers retain oversight, ownership and legal accountability while using AI to speed non‑sensitive drafting.

See the Department of State AI Inventory for documented municipal use cases and Nucamp's AI Essentials for Work syllabus for practical guidance on safe AI adoption:

Department of State AI Inventory (FOIA indexing, summarization, and metadata automation use cases)

Nucamp AI Essentials for Work syllabus (practical AI skills and Montana data stewardship guidance)

Material movers, laborers and inventory clerks - Billings' mailroom, stores and delivery teams - do tightly routine, high‑volume work (counting shipments, labeling, routing and simple data entry) that can be replicated by warehouse management systems, barcode/voice‑picking and inventory automation; O*NET's Shipping, Receiving, and Inventory Clerks profile details the exact tasks and in‑demand tech skills that make these functions automatable (O*NET profile for Shipping, Receiving, and Inventory Clerks).

The scale raises the stakes: CareerOneStop lists roughly 3,004,800 laborers and material movers nationally, so when local agencies in Billings adopt low‑cost no‑code automation to scale services without big IT budgets, a small municipal mailroom can see much of its daily work shifted to software rather than people (CareerOneStop largest‑employment occupations data).

The so‑what: these roles pay modestly (median wages under ~$21/hr for many shipping/receiving jobs) and often anchor entry‑level municipal hiring - so upskilling into inventory system oversight, exception handling and prompt/audit skills is a practical defense for workers and a way for Billings to retain institutional knowledge while adopting efficient tools (no‑code automation for small local agencies in Billings).

Practical adaptation in Billings means three immediate, concrete moves: train on job‑focused AI skills (prompt design, agent‑assist oversight and prompt/audit workflows) using local guides and courses, insist any pilot include algorithmic impact assessments and clear vendor transparency before production, and codify human‑in‑the‑loop review and audit trails for eligibility or finance workflows so staff - not opaque models - remain accountable; this matters because real deployments without those guardrails have harmed people (Indiana's Medicaid/SNAP modernization saw application denials rise ~50%), so demand staged pilots with human review, public reporting and Montana‑specific data controls to protect constituents.

Use the Roosevelt Institute's scan of public‑sector AI use cases to argue for worker protections and failure‑mode testing, adopt the governance checklist in the GovLoop framework for local oversight, and follow Montana data‑stewardship steps from Nucamp's guide so tools speed routine work without shifting risk onto residents or frontline staff.

“Failures in AI systems, such as wrongful benefit denials, aren't just inconveniences but can be life‑and‑death situations for people who rely upon government programs.”

Conclusion: Billings government workers can protect careers by treating AI as a force that reconfigures tasks, not a fate - industry research urges rapid reskilling (

92% of technology roles evolve

), and academic work shows firms invest in higher‑skilled roles unless workers upgrade, so the practical move for Montana staff is to learn hands‑on AI oversight, prompt design and audit skills that keep humans in the loop.

Prioritize short, job‑focused training (for example, the Nucamp AI Essentials for Work bootcamp registration: Nucamp AI Essentials for Work bootcamp registration), require algorithmic impact assessments from vendors (as the Brookings analysis of AI's effects on firms and workers highlights the redistribution toward skilled tasks), and adopt Montana‑specific data‑stewardship rules so automation speeds routine work without exposing sensitive records or eroding public trust; one specific, memorable metric to track locally: measure error/appeal rates after each pilot so any tool that raises denials or complaints is paused and audited immediately.