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

Surprise, Arizona sits at the frontline of a nationwide shift: AI is set to automate repetitive municipal work, reshape skills, and push local governments toward more vendor-driven, efficiency-focused solutions - trends covered in Route Fifty's analysis of public-sector change and the Roosevelt Institute's detailed scan of AI use cases for public administrators.

City staff who process permits, handle benefits, or run customer-service desks may see routine tasks handled by chatbots and intelligent document processing, while new roles for oversight, data governance, and human–AI collaboration emerge; but those gains come with risks - longer waits for help, vendor lock-in, and the potential for biased or incorrect decisions.

Practical reskilling can help: Nucamp's AI Essentials for Work bootcamp teaches nontechnical employees how to use AI tools, craft effective prompts, and apply AI across business functions in 15 weeks (early-bird $3,582), making it a concrete option for Surprise workers and leaders preparing for change.

Learn more and register for the AI Essentials for Work bootcamp at the Nucamp AI Essentials for Work registration page or review the full AI Essentials for Work syllabus.

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.

Methodology leaned on occupation-level automation scores linked to census microdata: researchers mapped Frey & Osborne-style risk scores to American Community Survey data, flagging the occupations with risk scores in the 0.70–0.99 band (the top 20 high‑risk jobs that together accounted for 4.5 million California workers in the UCLA analysis) and then replicated that task‑level lens for Arizona using the 2023 pooled five‑year ACS in the UCLA Latino Data Hub brief.

The approach disaggregates outcomes by ethnicity, sex, age, citizenship, English proficiency, education and home internet/device access so hotspots and vulnerable subgroups become visible - for example, younger workers and noncitizens show up more frequently in high‑risk roles.

Results reported here draw directly from UCLA's state and national analyses and the Hub's new automation indicator, which makes it possible to filter and compare local risk patterns across communities (UCLA Latino Data Hub: Automation Risks for California Latinos report, UCLA Latino Data Hub Arizona automation risks brief, Latino Data Hub expansion and automation indicator overview).

Caveats were noted: occupation‑level scores can miss task variation, and local policy choices shape how quickly automation materializes - yet the method reliably surfaces where disruption is most likely to land.

Entry-level administrative and clerical staff - like City of Surprise administrative assistants who handle permit queries, benefits paperwork, and daily data entry - are among the roles most exposed to automation: a recent government estimate that 62% of administrative assistant work could be automated, and municipalities are already piloting chatbots, transcription and intelligent document processing to speed routine interactions.

These tools can speed up repeatable tasks and offer 24/7 responses (examples include local AI-driven service portals such as Phoenix's bilingual myPHX311), but the Roosevelt Institute cautions that generative chatbots and automation often shift burdens onto human staff - increasing review work, creating new supervisory responsibilities, and, in worst cases, producing harmful errors like wrongful benefit denials that staff must resolve (Roosevelt Institute analysis of AI in public administration).

The takeaway for Surprise: while automation can clear the inbox of rote paperwork, it can also turn an assistant's day into a stream of error‑corrections and disgruntled callers - a vivid reminder that planning, human oversight, and targeted reskilling will determine whether AI becomes a relief or a new source of stress for municipal workers.

For Surprise's budget analysts and performance technicians, the automation threat runs alongside a parallel danger: fragile IT and app security can turn routine analytics work into a liability.

Skilled analysts who clean, reconcile, and flag exceptions may see much of that repeatable work automated - but the underlying systems these tools run on are often riddled with trouble: research finds roughly 80% of government agencies have software vulnerabilities left unaddressed for at least a year, and application flaws now account for a growing share of breaches (the percentage tied to application vulnerabilities has doubled to about 43% in recent analyses).

That combination matters in Surprise because automated pipelines and third‑party forecasting tools can amplify errors or expose sensitive budget and payment data if vendors or legacy apps aren't secured; practical responses include embedding automated security checks into data workflows and using targeted revenue‑recovery and forecasting tools that come with built‑in validation and vendor controls.

Local leaders who pair automation pilots with stronger DevSecOps practices and vendor oversight stand a better chance of converting routine data work into faster, safer financial decisioning for the city rather than a new source of outages or fraud (report on unaddressed software vulnerabilities in government agencies, analysis of application-security risks in federal agencies, overview of revenue-recovery and forecasting tools for local governments).

“Through this AI RFI, Treasury continues to engage with stakeholders to deepen its understanding of current uses, opportunities, and associated risks of AI in the financial sector,” said Under Secretary for Domestic Finance Nellie Liang.

For Surprise customer service center representatives - those answering permit questions, billing calls, and 311‑style inquiries - the near horizon looks like a hybrid desk: chatbots and virtual assistants will pick off routine FAQs and simple transactions, while humans handle escalations, nuanced cases, and oversight.

Research finds government lags private sectors on contact‑center AI (only about 45% of public‑sector centers are automated), yet agencies that do it well use chatbots to free agents for higher‑value work rather than simply cut headcount, and clear use cases plus strong security and data links are essential (Route Fifty report on AI in contact centers).

Practical guidance from Digital.gov stresses starting with narrow question‑and‑answer bots, rigorous usability testing, and safe handoffs to humans and legacy systems - lessons that can help Surprise pilot small, measurable bots that improve response times without sacrificing accuracy (Digital.gov's chatbot guidance).

The “so what?” is simple: a well‑designed bot can shrink routine wait times dramatically, but poorly scoped automation can create new workloads and public trust problems unless leaders pair it with testing, transparency, and workforce retraining.

“When we first started ... we had a 1.5 hour wait time just to answer a question for someone standing in line at the airport. By the time we would answer that question most people were already on the plane and flying and it was too late. Now our time to reply and answer a question is less than two minutes.”

Surprise HR recruiters and benefits administrators face a near-term shift where smart tools take over the repetitive heavy lifting - resume parsing, interview scheduling, routine follow‑ups, and even onboarding steps like completing Form I‑9 and tax paperwork - so staff can spend more time on candidate engagement and benefits counseling; vendors and guides show this in practice (the American Heart Association saw a 200% jump in sourcing and a 50% boost in recruiter time spent engaging after automation) and analysts note roughly 73% of recruiters' time is tied to automatable tasks, making the efficiency case hard to ignore.

Thoughtful pilots should start small: use applicant tracking and screening to flag likely matches, deploy multilingual texting and outreach where local outreach is critical, and connect screening tools to HRIS for secure onboarding - examples and implementation steps are detailed in iCIMS' recruitment process automation guide and Ribbon's step‑by‑step screening playbook, while mobile‑first vendors like Team Engine show how AI screening can reach multilingual, hard‑to‑reach applicants.

But the “so what?” is this: automation can turn a week of paperwork into hours only if Surprise pairs tool choice with audits, bias checks, clear privacy controls, and retraining so HR shifts from data entry to human judgment and employee support.

"AI can transform the recruitment process by automating tedious tasks and improving candidate matching, but it's crucial to ensure these tools are used ethically and transparently."

Permit technicians and code‑compliance monitors in Surprise face an immediate mix of threat and opportunity as end‑to‑end permitting platforms and automated review engines move routine approvals - and even some inspections - online: modern systems let residents file 24/7, track status in real time, and route applications across departments to cut manual handoffs (GovPilot permitting software overview: how permitting software works), while cloud solutions with GIS, mobile inspection apps, and automated workflows promise faster turnarounds and fewer repetitive checks (Granicus SmartGov cloud permitting solution).

In Arizona, the NREL SolarAPP pilot showed the flip side - Tucson and Pima County used automated solar reviews to process thousands of residential solar applications more quickly (the pilot helped streamline a pipeline of nearly 3,600 permits), demonstrating that rule‑based automation can instantly approve compliant designs and return actionable corrections for noncompliant ones (NREL SolarAPP automated residential solar permitting pilot).

The practical “so what” for Surprise: automation can clear backlog and let staff focus on complex inspections and enforcement, but success requires one‑stop digital design, clear workflows, and human oversight so technicians shift from data entry to higher‑value code review rather than becoming a constant backstop for automated errors.

“We now have a lower volume of calls and a lower volume of emails, because everyone is using the portal and things are getting done quicker and more efficiently.”

Expect Surprise's city hall to look more like a hybrid operations center over the next five years: routine permit checks, form processing, and basic 311 inquiries are likely to move into automated pipelines while human staff shift toward oversight, exception-handling, and strategic projects - a shift Route Fifty calls a near-term reshaping of public-sector jobs rather than wholesale elimination (Route Fifty analysis of AI's impact on public-sector jobs).

That move can speed service and cut overtime costs when modernization is done right - CentralSquare documents clear ROI from cloud-based automation and analytics - yet it also pushes smaller cities toward vendor solutions and creates dependency on third‑party platforms.

Local leaders in Surprise will therefore need practical pilots, strengthened data governance, and training pipelines so staff can run AI copilots and audit outputs; otherwise, faster case processing could come with new risks to privacy, security, and public trust.

The payoff can be dramatic - real-world pilots show manual review tasks collapsing from over an hour to minutes - so the essential question for Surprise is not whether to adopt AI, but how to buy, govern, and train for it responsibly (App Maisters guide to local government use of AI).

“Government is ripe for automation,” Everson says.

Surprise workers facing AI-driven change can practically shift risk into opportunity by building data literacy and stackable credentials tied to everyday municipal tasks: start with a simple skills baseline, then use targeted cohorts and short badges so clerks, permit technicians, HR staff and customer‑service reps learn to read, validate, and communicate data rather than just file it.

StateTech's playbook shows the payoff - Indiana awarded 1,800 data‑literacy badges after launching a structured program - and warns that most states lack formal efforts, creating a clear opening for Arizona to act.

Read the StateTech article on nurturing data literacy in government for program examples and outcomes: StateTech article: Nurturing Data Literacy in Government Workforces.

Practical program design follows a proven four‑step framework - plan, curate, engage, measure - so agencies can pick curated courses, cohort work, hands‑on projects and clear metrics rather than one‑off trainings; see the AWS blog on launching a successful public sector data literacy program for details: AWS blog: Launch a Successful Data Literacy Program for Public Sector Employees (AWS/Coursera framework).

For leaders and aspiring supervisors, short virtual leadership offerings give practical guidance on data strategy and ethics, making it easier for Phoenix‑area and Surprise teams to supervise AI outputs and spot errors before they reach residents; explore the Partnership for Public Service's free Insightful Governance cohort: Insightful Governance: Data Literacy for Leaders (Partnership for Public Service).

The immediate goal is simple: swap paper panic for steady dashboards, human review checkpoints, and career paths that reward data skill‑building.

“If someone doesn't understand how their actions affect others down the pipeline, then it's going to be difficult to institute change and improvement.” - Derek Werthmuller

As Surprise modernizes, new city jobs will grow up around the machines: AI system administrators who run and monitor agent fleets (think the bank example that uses AI to scan millions of logs for anomalies), data‑governance officers who set provenance, privacy, and bias controls, and human–AI collaboration specialists who design safe handoffs, train staff on model limits, and translate policy into day-to-day workflows.

Federal trends show AI is already being used to assist administrative and IT functions - see the Federal CIO AI in Action report for examples - and expanded procurement access to major models makes vendor‑facing roles and acquisition‑savvy IT leads especially important with the GSA Multiple Award Schedule AI procurement announcement.

Practical guides and industry examples underline that success won't come from buying models alone but from building internal capabilities - technical, managerial, and policy - so Surprise can hire or train people who ensure AI augments staff rather than offloads accountability; for an industry perspective, read about AI agents transforming systems administration and government operations.

The payoff is tangible: faster services and clearer audit trails when human expertise is woven into every AI deployment, not sidelined by it.

“GSA is proud to leverage our procurement expertise to advance the President's AI Action Plan. Through GSA's marketplace, agencies will be able to explore a wide range of AI solutions, from simple research assistants powered by large language models to highly tailored, mission-specific applications,” said Federal Acquisition Service Commissioner Josh Gruenbaum.

Key Arizona datapoints for leaders and workers: Latinos make up roughly 31% of Arizona's population and account for about 1.3 million eligible voters - nearly a quarter of the state electorate - so changes to municipal services and job training will have outsized effects on a younger, often bilingual workforce; in Maricopa County alone Latinos are one-third of residents (about 1.4 million), with high labor-force participation but lower median hourly wages and higher uninsured rates, highlighting both the urgency and opportunity for targeted reskilling and outreach (see UCLA's Key Facts About Latinos in Maricopa County and the UCLA LPPI state briefs).

Local capacity-building is already underway: the UCLA LPPI and ASU LDH Action Lab in Arizona trains community leaders to use Census data for policy advocacy, a practical model for designing equitable AI pilots and workforce programs.

“Our analysis highlights the significant growth and contributions of the Latinos in Maricopa County, who now make up nearly a third of the population. Despite high labor participation, Latinos continue to experience disparities in wages, educational attainment, homeownership, and healthcare access. Taking into account that Latinos in Maricopa are very young and many are U.S. citizens, these findings provide a clearer picture of how Latinos' wellbeing is critical to the county's continued growth.” - Jie Zong, senior research analyst at UCLA LPPI and manager of the Latino Data Hub

Takeaway for Surprise: the path forward is practical and local - pair pilots with workforce pipelines, fund short credentials and apprenticeships, and make use of existing Arizona systems so displaced clerks, permit technicians, HR staff and contact‑center workers can move into higher‑value roles instead of being left behind.

City leaders should lean on the Arizona Reskilling & Recovery Network to scale community‑college partnerships and industry‑aligned micro‑credentials (Arizona Reskilling & Recovery Network reskilling programs), connect residents to on‑the‑ground help at the Surprise Resource Center and ARIZONA@WORK employment services (Surprise Resource Center employment services and ARIZONA@WORK), and adopt skills‑first hiring practices shown to widen local talent pools.

Workers who want hands‑on, job‑ready AI skills can enroll in short, applied programs such as Nucamp's 15‑week AI Essentials for Work (early‑bird $3,582) to learn tools, prompts, and workflows that turn “a week of paperwork into hours” and make municipal roles more resilient (Nucamp AI Essentials for Work 15-week bootcamp registration).

With coordinated reskilling, transparent pilots, and clear governance, Surprise can convert automation risk into a faster, fairer local labor market.

“This is about creating better pathways for those workers whose skills align best to critical public-sector roles - while also creating new pipelines into fulfilling state government careers that can help us build a stronger and more resilient workforce.”