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

Artificial intelligence is already influencing federal guidance and private-sector support that reach state agencies, so South Carolina's public workforce should treat AI as an urgent operational and human‑rights issue: the U.S. Department of State's Risk Management Profile for AI and Human Rights warns that poorly governed AI can harm immigration, benefits, criminal‑justice and other public services, while the Global AI Research Agenda calls for research into AI's labor‑market impacts and reskilling - meaning licensing clerks, municipal call centers, translators, and data teams in South Carolina may face rapid automation and new oversight duties.

Federal‑industry programs have already mobilized significant funding (the Partnership for Global Inclusivity on AI pledged more than $100 million) to expand tools and training, so the practical response is twofold: adopt risk‑management practices and build workplace AI skills - for example, a focused 15‑week course like Nucamp's AI Essentials for Work bootcamp teaches prompt writing and job‑based AI applications for non‑technical staff.

“We're harnessing technology for the betterment not just of our people and our friends, but of all humanity.”

The methodology combined three evidence streams to identify the South Carolina government jobs most exposed to AI: analysis of vendor and policy signals (

AI will displace some jobs

noted in Microsoft's Annual Report 2024 and 2023 describing new AI skilling initiatives), a review of local Nucamp use‑cases and guidance for Columbia agencies (including predictive analytics pilots and recommended partnerships with USC, Clemson, and SCRA), and a hands‑on look at productivity AI such as Copilot in Outlook, which already drafts messages and summarizes long threads - clear indicators that routine text drafting, standardized licensing rules, and high‑volume correspondence are vulnerable.

Roles were scored by task automability (repetitive rule‑based work, volume of text/audio processing, routine data entry), proximity to existing tool capabilities, and available local reskilling pipelines; that scoring led to prioritizing translators, municipal call‑center staff, writers/editors, data teams, and licensing/permit clerks for immediate adaptation efforts.

Translators and interpreters in South Carolina's government - staff who handle immigration interviews, municipal call‑center escalation, and press‑office briefings - face fast, targeted pressure from automation: CSA Research's large “Perceptions on Automated Interpreting” study (a 352‑page report with ~9,400 datapoints) shows automated interpreting is attractive for 24/7, low‑risk needs but repeatedly fails in complex, high‑risk settings such as healthcare, legal, and emergency services unless a human can rapidly escalate; that means county courts and health‑agency encounters in Columbia should keep human‑in‑the‑loop protocols while piloting AI for routine notifications and scheduling (where cost and access gains are real).

Evidence from labor studies also signals tangible worker impact - research finds areas with higher machine‑translation uptake saw falling translator employment - so adaptation is not optional.

Practical steps for South Carolina agencies: adopt clear decision criteria (required accuracy, risk of harm, language complexity), mandate disclosure and escalation channels, and invest in interpreter up‑skilling so professionals operate AI as an assistive tool rather than a hidden replacement (see CSA's findings and policy guidance and the CEPR analysis for labor effects, and align pilots with local workforce partnerships listed in Nucamp's Columbia AI guide).

South Carolina municipal call centers and transport agency phone lines face high exposure to conversational AI because routine, high‑volume tasks - intake questions, appointment scheduling, status lookups - are already automatable with chatbots, IVR and cloud platforms; the South Carolina Department of Social Services' move to Amazon Connect showed how a small pilot (five agents, one supervisor) scaled into mission‑critical operations that generate transcripts, summarize intake, offer callbacks and processed more than 100,000 Disaster SNAP calls during Hurricane Helene, proving automation can boost reliability without eliminating the need for human judgment.

To adapt, city and county phone teams should train for human‑in‑the‑loop workflows (verifying AI transcripts, handling escalations, and managing sensitive cases), partner with IT on secure cloud deployments, and work with HR to shift job descriptions toward case management and AI oversight - approaches documented in South Carolina's municipal AI discussions and broader guidance on conversational AI for local governments.

Agencies should also monitor emerging state AI rules that affect procurement and contracting while piloting tools that preserve empathy and reduce hold times.

Practical takeaway: learn to operate and audit the bot - those who validate AI outputs will be the most indispensable agents.

“Don't be afraid of innovation.” - Jose Encarnacion, IT director and CIO, DSS

Writers, technical writers, editors, and news analysts in South Carolina's government communications face a double-edged reality: generative AI can draft routine briefings, social posts, and summaries - Pennsylvania's state pilot reported average time savings of about 95 minutes per day for staff using ChatGPT - but those speed gains come with real risks (fabricated facts, “hallucinated” quotes, and loss of source provenance) that can quickly erode public trust and create legal exposure.

Adopted editorial safeguards should mirror best practice: require human‑in‑the‑loop signoff and provenance checks, disclose when content is AI‑assisted and watermark outputs, secure consent before replicating a public official's likeness, and train communicators in prompt crafting and AI verification workflows; see the UK Government Communications Service's principles for responsible generative AI use for practical rules on oversight and disclosure.

Ground these changes in the worker‑centred concerns documented by the Roosevelt Institute - where informal AI adoption increased worker burden and risk - so that AI becomes an assistant for accuracy and reach, not a hidden author that undermines accountability.

“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.”

Data analysts and web developers in South Carolina's government - management analysts, public‑finance analysts, and the teams that maintain agency dashboards - face immediate pressure from tools that clean, classify, and forecast at scale: the GSA's AI inventory lists live pilots for Key KPI forecasts, document classification, ServiceNow ticket routing, and even contract‑evaluation automation that directly overlap with routine analyst work, while generative assistants (Gemini‑style features) can now help draft queries, debug code, and surface data insights for non‑specialists.

The practical risk is not only job loss but task‑shift: instead of compiling monthly reports, analysts will increasingly validate model outputs, check provenance, and manage human‑in‑the‑loop workflows before forecasts feed budget or procurement decisions.

Adaptation is concrete - build AI‑validation checklists, require provenance and human signoff for any model output used in fiscal decisions, and tap local training and research pipelines (forge partnerships with USC, Clemson, and SCRA) to run realistic pilots and reskill teams.

Use cases and governance lessons in the GSA inventory make one thing plain: agencies that train staff to audit and operationalize AI will convert a liability into an oversight advantage.

“AI will likely affect your work in both research and education in ways that are both enabling and challenging.”

Administrative clerks and licensing/permits staff in South Carolina should treat Robotic Process Automation (RPA) as an immediate operational pressure point because routine, rule‑based tasks - form intake, document validation, fee calculation, and status updates - map directly to automation; industry guides note that

RPA for public sector can boost the system by handling applications in a much faster

and that RPA is a low‑cost, near‑zero‑risk gateway for agencies with constrained IT budgets or legacy back‑end systems.

To adapt, shift job descriptions from repetitive data entry toward exception handling, quality assurance, and bot oversight: pilot RPA on predictable workflows while keeping human review for discretionary licensing decisions, document interpretation, and appeals.

Practical next steps for South Carolina agencies include selecting a narrow licensing use case to pilot, documenting rules for automation, and investing in short reskilling pathways that teach clerks to test and monitor bots - leveraging local training and partnership resources to turn backlog pressure into a skills advantage.

See government RPA use cases and workshop guidance for getting started.

Start with a focused, practical playbook: (1) audit roles and tasks to flag high‑risk workflows (routine drafting, intake, licensing rules, and high‑volume call handling - note DSS processed >100,000 Disaster SNAP calls in a cloud rollout), (2) pilot narrow automations with human‑in‑the‑loop checkpoints and provenance requirements aligned to South Carolina's Three Ps (Promote, Protect, Pursue) as described in the state AI strategy, (3) embed governance and security controls before scaling, and (4) partner with local universities and federal communities for training and shared best practices - join the GSA Artificial Intelligence Community of Practice to tap cross‑agency playbooks and training cohorts and consider targeted reskilling like a 15‑week AI Essentials pathway for non‑technical staff to learn promptcraft, AI oversight, and job‑based use cases (see the state AI strategy and enroll in a practical course).

HR units should rewrite job descriptions toward exception handling and AI auditing, fund short bootcamps through local partnerships, and run quarterly audits of deployed tools so employees shift from “replaced” to “oversight and value creation” roles.

Program: AI Essentials for Work (Nucamp)
Length: 15 Weeks
Why it helps: Teaches prompt writing, job‑based AI skills, and non‑technical oversight workflows
Registration: Nucamp AI Essentials for Work registration and course details

“Don't be afraid of innovation.” - Jose Encarnacion, IT director and CIO, DSS