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

Rochester's public workforce is at a crossroads: an April 2025 audit by New York State Comptroller Thomas DiNapoli found agencies “largely on their own” when governing AI, with patchwork oversight, unclear rules on confidential data, and little bias monitoring - gaps that can turn efficiency tools into legal and ethical hazards; local guidance from the University of Rochester likewise warns that non‑public or sensitive information must never be uploaded into external AI tools and points staff to a secure campus chatbot for higher‑risk data (New York State Comptroller DiNapoli audit on AI governance, University of Rochester responsible use of AI tools guidance).

With regulators flagging problems like

“AI washing” and hallucinations

, targeted reskilling matters now - practical programs such as the Nucamp AI Essentials for Work bootcamp registration teach safe prompt writing and on-the-job AI skills that help municipal employees adapt before automation reshapes services.

Selection began with task-level triage: inventory public‑sector roles and break them into discrete duties, then score each duty for data sensitivity, repetitiveness and downstream legal or reputational impact - a practical risk/benefit lens borrowed from Deloitte's generative‑AI risk framework Deloitte generative AI risk framework – Managing gen AI risks.

Threat modelling followed, using STRIDE to enumerate attack types and DREAD to prioritise fixes so privacy‑sensitive work (legal briefs, procurement records, child‑welfare case notes) gets higher scrutiny, as laid out in technical guides to LLM security Zendata threat modelling and risk analysis for LLM security.

Finally, tests and pilots - mirroring GOV.UK experiments that reached roughly 80% answer accuracy in targeted use cases - helped flag roles where a model already handles the bulk of routine outputs, signalling high exposure and a clear window for targeted reskilling rather than blunt layoffs; the UK AI Playbook informed the governance checkpoints used to score readiness and required human‑in‑the‑loop controls UK Government Artificial Intelligence Playbook and governance guidance.

The result: a ranked list that weights impact, likelihood, legal/privacy exposure and realistic adaptation pathways for Rochester's municipal workforce.

Administrative and clerical staff - customer service representatives, new‑accounts clerks and telephone operators - are among the most exposed in Rochester's public sector because their day is stacked with repetitive text, form‑filling and routine record lookups that AI already automates elsewhere; when those duties touch sensitive records (think child‑welfare case notes or procurement files) the upside of speed quickly becomes a compliance risk unless controls are tight.

Smart adoption means starting small, keeping humans in the loop, and validating models regularly - best practices that local governance, risk and compliance experts warn are essential to preserve privacy and auditability (GRC insights: AI opportunities and challenges for governance, risk & compliance).

Practical adaptation pathways in New York include retraining clerical staff toward digital roles, leveraging state training and career ladders at the NYS Office of Information Technology Services, and aligning pilots with institutional governance from Rochester's AI programs so automation augments rather than replaces experienced public‑servants (NYS Office of Information Technology Services employment and training opportunities, University of Rochester artificial intelligence programs and governance).

The diagnostic rule: automate the routine, protect the sensitive, and invest in human‑centered validation so a single erroneous reply never becomes a citywide data breach.

Legal support roles - paralegals, legal assistants and policy research analysts - sit at an awkward sweet spot: demand remains steady in New York (paralegal pay ranks among the top states) even as routine research, document review and e‑discovery are being absorbed by legal tech, so the task mix is shifting from volume work to higher‑value analysis and compliance; the BLS‑based outlook still shows modest job openings and a median paralegal salary near $59,200, but firms are already recalibrating how work is billed and staffed (BLS paralegal outlook and salary trends).

Practice surveys and reports highlight the consequences: many legal managers expect AI efficiencies to change the billable‑hour model, and investment in embedded generative AI and knowledge‑management tools means legal support staff who learn e‑discovery, AI oversight and secure prompt governance will be the most resilient (Wolters Kluwer Future Ready Lawyer Report on staffing and tech trends, ABA Journal legal tech trends that defined 2024).

Practical adaptation in Rochester means upskilling paralegals to validate AI outputs, manage secure workflows, and translate AI‑produced drafts into strategy - so that instead of losing jobs, local legal teams redeploy expertise where human judgement matters most.

“It's not just about filling a position anymore. We're looking for paralegals who are not only highly skilled but adaptable to remote work and eager to grow in new areas like legal tech. The competition for talent is fierce, and retention is an even bigger challenge.”

Accounting, finance and procurement staff in Rochester face a fast‑shifting landscape: routine reconciliations, fraud screening and vendor‑due‑diligence tasks that once ate whole days are now prime targets for generative AI, but New York's experience shows the payoff comes with sharp guardrails - New York State's April 2025 New York State AI Governance audit (April 2025) warned that agencies lack an effective AI governance framework and often do not test outputs for accuracy or bias, creating audit and privacy exposure if controls are weak.

Financial regulators and industry groups push the same point: the Treasury/City Bar dialogue highlights model complexity, hallucinations and third‑party risk, and New York's regulators already expect robust oversight, documentation and vendor audit rights for AI systems used in financial decisions (see the NYC Bar report on US Treasury AI in financial services and the NYDFS AI guidance summary).

For municipal accountants and procurement officers the practical takeaway is concrete: require model‑validation and annual drift testing in contracts, build human‑in‑the‑loop checks for budgeting and audit outputs, and upskill staff to spot a plausible‑sounding AI hallucination before it becomes a misreported ledger line or a costly procurement error - because a single unchecked AI prompt can ripple into a compliance review or public complaint faster than manual spreadsheets ever did.

Communications and content roles - public information officers, grant writers and report authors - are squarely in the crosshairs because generative tools can crank out first drafts, media lists and real‑time monitoring but also amplify errors, bias and deepfakes that swiftly damage public trust; experts warn authenticity and disclosure matter because

trust can take years to build but be lost overnight

and auditors found AI outputs contain notable error rates (NewsGuard flagged ~18% unreliable responses, with many left uncorrected) so human oversight is non‑negotiable (Cision: AI risks and ethical use in public relations, PRNEWS: NewsGuard audit on AI misinformation in PR).

Security and reputational safeguards must sit beside speed: encrypt pipelines, verify sources, adopt content‑authentication practices and keep humans in the loop for quotes and legal claims - advice echoed in analyses of AI's nexus with cybersecurity and deepfakes (USC Annenberg report on AI and cybersecurity risk).

The practical play for Rochester's comms teams is clear - use AI to boost monitoring and draft routine copy, but mandate disclosure, rigorous fact‑checking and tailored training so a single plausible‑sounding hallucination never becomes a citywide crisis.

Data‑entry, records and library science roles - archivists, records clerks and librarians - are squarely in AI's early path because modern Optical Character Recognition (OCR) and cataloging models can extract, index and classify whole batches of scanned records that once required repetitive transcription; OCR

automates the conversion of scanned documents into editable text,

making formerly image‑only files searchable and shifting routine workloads toward verification and rights/metadata curation (Optical Character Recognition in document scanning, How OCR data entry works).

That technical upside collides with real‑world constraints in Rochester: repositories like the University of Rochester Archives flag access restrictions and the need to remediate harmful or sensitive language, which means automation must be paired with human review, provenance controls and governance so a single misapplied tag doesn't turn a historic file into a privacy breach.

Practical adaptation is concrete - reskill for AI‑assisted metadata, validation and secure pipelines - so staff move from typing entries to becoming guardians of context and rights, not just data processors; imagine a dusty box of scanned invoices instantly searchable, but still beholden to careful human judgement before public release.

Rochester's city managers need a cross‑cutting playbook that blends relationship‑building, hard verification and workforce clarity so AI improves services without triggering governance failures: start by embedding auditors into project teams and quarterly learning roundtables (yes, the “audit pizza” that Diligent highlights) so controls and stakeholders move in step rather than in silos Diligent best practices for public sector auditors; pair that with rigorous, mixed‑method testing - inspection, observation, inquiry and re‑performance - to validate model outputs before they touch budgets or case files IS Partners audit testing methods; and run fast, transparent staff and desk audits to map who does what, capture critical institutional knowledge, and target reskilling where it preserves service continuity rather than cutting roles Washington OFM desk audit guidance.

The practical action plan is simple: convene stakeholders, require human‑in‑the‑loop checks and annual drift tests, and invest in focused training so a single hallucination never becomes a public crisis - think of it as swapping a dusty file cabinet for a searchable archive that still needs a human guardian at the keyhole.

Practical next steps for Rochester's public sector focus on three things: stronger governance, funded reskilling, and human‑centered validation. At the policy level, New York's proposed Workforce Stabilization Act (S9401) shows how mandatory impact assessments and a retraining fund could help municipalities manage displacement and pay for transitions (Workforce Stabilization Act (S9401) summary); locally, Rochester partners and grants - MPower, RochesterWorks' IWT grants and ARPA‑funded programs - already subsidize employer training and incumbent upskilling to keep staff on the payroll (Rochester local upskilling programs and grants (MPower, RochesterWorks)).

For immediate capacity building, short, job‑focused courses such as the 15‑week Nucamp AI Essentials for Work bootcamp teach safe prompt writing, validation and on‑the‑job AI skills that preserve human judgment while boosting productivity (Nucamp AI Essentials for Work registration).

Treat AI like a supervised assistant - require audits, human‑in‑the‑loop checks and documented vendor controls - and Rochester can harness efficiency without sacrificing trust or jobs.

“We're living in an era of lightning-fast advancement in Artificial Intelligence that has equal potential to help society as it does to deepen economic inequality.”