A collaboration between Lewis McLain & AI

Cities are about to experience an administrative shift that will look nothing like a “tech revolution” and nothing like a classic workforce reduction. It will arrive as a workquake: a sudden drop in the labor required to complete routine tasks across multiple departments, driven by AI systems that can ingest documents, apply rules, assemble outputs, and draft narratives at scale.

The danger is not that cities will replace everyone with software. The danger is more subtle and far more likely: cities will allow AI to hollow out core functions unintentionally, through non-replacement hiring, scattered tool adoption, and informal workflow shortcuts—until the organization’s accountability structure no longer matches the work being done.

In 2026, the right posture is not fascination or fear. It is proactive redesign.

---

I. The Real Change: Task Takeover, Not Job Replacement

Municipal roles often look “human” because they involve public trust, compliance, and service. But much of the day-to-day work inside those roles is structured:

• collecting inputs
• applying policy checklists
• preparing standardized packets
• producing routine reports
• tracking deadlines
• drafting summaries
• reconciling variances
• adding narrative to numbers

Those tasks are precisely what modern AI systems now handle with speed and consistency. What remains human is still vital—but it is narrower: judgment, discretion, ethics, and accountability.

That creates the same pattern across departments:

• the production layer shrinks rapidly
• the review and exception layer becomes the job

Cities that don’t define this shift early will experience it late—as a staffing and governance crisis.

---

II. Example- City Secretary: Where Governance Work Becomes Automated

The city secretary function sits at the center of formal governance: agendas, minutes, public notices, records, ordinances, and elections. Much of the labor in this area is procedural and document-heavy.

Tasks likely to be absorbed quickly

• Agenda assembly from departmental submissions
• Packet compilation and formatting
• Deadline tracking for posting and notices
• Records indexing and retrieval
• Draft minutes from audio/video with time stamps
• Ordinance/resolution histories and cross-references

What shrinks

• clerical assembly roles
• manual transcription
• routine records handling

What becomes more important

• legal compliance judgment (Open Meetings, Public Information)
• defensibility of the record
• election integrity protocols
• final human review of public-facing outputs

In other words: the city secretary role does not disappear. It becomes governance QA—with higher stakes and fewer support layers.

---

III. Example – Purchasing & Procurement: Where Process Becomes Automated Screening

Purchasing has always been a mix of routine compliance and high-risk discretion. AI hits the routine side first, fast.

Tasks likely to be absorbed quickly

• quote comparisons and bid tabulations
• price benchmarking against history and peers
• contract template population
• insurance/required-doc compliance checks
• renewal tracking and vendor performance summaries
• anomaly detection (odd pricing, split purchases, policy exceptions)

What shrinks

• bid tabulators
• quote chasers
• contract formatting staff
• clerical procurement roles

What becomes more important

• vendor disputes and negotiations
• integrity controls (conflicts, favoritism risk)
• exception approvals with documented reasoning
• strategic sourcing decisions

Procurement shifts from “processing” to risk-managed decisioning.

---

IV. Example – Budget Analysts: Where “Analysis” Separates from “Assembly”

Budget offices are often mistaken as purely analytical. In reality, a large share of work is assembly: gathering departmental submissions, normalizing formats, building tables, writing routine narratives, and explaining variances.

Tasks likely to be absorbed quickly

• ingestion and normalization of department requests
• enforcement of submission rules and formatting
• auto-generated variance explanations
• draft budget narratives (department summaries, highlights)
• scenario tables (base, constrained, growth cases)
• continuous budget-to-actual reconciliation

What shrinks

• entry-level budget analysts
• table builders and narrative drafters
• budget book production labor

What becomes more important

• setting assumptions and policy levers
• framing tradeoffs for leadership and council
• long-range fiscal forecasting judgment
• telling the truth clearly under political pressure

Budget staff shift from spreadsheet production to decision support and persuasion with integrity.

---

V. Example – Police & Fire Data Analysts: Where Reporting Becomes Real-Time Patterning

Public safety analytics is one of the most automatable municipal domains because it is data-rich, structured, and continuous. The “report builder” role is especially vulnerable.

Tasks likely to be absorbed quickly

• automated monthly/quarterly performance reporting
• response-time distribution analysis
• hotspot mapping and geospatial summaries
• staffing demand pattern detection
• anomaly flagging (unusual patterns in calls, activity, response)
• draft CompStat-style narratives and slide-ready briefings

What shrinks

• manual report builders
• map producers
• dashboard-only roles
• grant-report drafters relying on routine metrics

What becomes more important

• human interpretation (what the pattern means operationally)
• explaining limitations and avoiding false certainty
• bias and fairness oversight
• defensible analytics for court, public inquiry, or media scrutiny

Public safety analytics becomes less about producing charts and more about protecting truth and trust.

---

VI. Example – More Roles Next in Line

Permitting & Development Review

AI can quickly absorb:

• completeness checks
• code cross-referencing
• workflow routing and status updates
• templated staff reports

Humans remain essential for:

• discretionary judgments
• negotiation with applicants
• interpreting ambiguous code situations
• public-facing case management

HR Analysts

AI absorbs:

• classification comparisons
• market surveys and comp modeling
• policy drafting and FAQ support

Humans remain for:

• discipline, negotiations, sensitive cases
• equity judgments and culture
• leadership counsel and conflict resolution

Grants Management

AI absorbs:

• opportunity scanning and matching
• compliance calendars
• draft narrative sections and attachments lists

Humans remain for:

• strategy (which grants matter)
• partnerships and commitments
• risk management and audit defense

---

VII. The Practical Reality in Cities: Attrition Is the Mechanism

This won’t arrive as dramatic layoffs. It will arrive as:

• hiring freezes
• “we won’t backfill that position”
• consolidation of roles
• sudden expectations that one person can do what three used to do

If cities do nothing, AI will still be adopted—piecemeal, unevenly, and without governance redesign. That produces an organization with:

• fewer people
• unclear accountability
• heavier compliance risk
• fragile institutional memory

---

VIII. What “Proactive” Looks Like in 2026

Cities need to act immediately in four practical ways:

1. Define what must remain human
   • elections integrity
   • public record defensibility
   • procurement exceptions and ethics
   • budget assumption-setting and council framing
   • public safety interpretation and bias oversight
2. Separate production from review
   • let AI assemble
   • require humans to verify, approve, and own
3. Rewrite job descriptions now
   • stop hiring for assembly work
   • hire for judgment, auditing, communication, and governance
4. Build the governance layer
   • standards for AI outputs
   • audit trails
   • transparency policies
   • escalation rules
   • periodic review of AI-driven decisions

This is not an IT upgrade. It’s a redesign of how public authority is exercised.

---

Conclusion: The Choice Cities Face

Cities will adopt AI regardless—because the savings and speed will be undeniable. The only choice is whether the city adopts AI intentionally or accidentally.

If adopted intentionally, AI becomes:

• a productivity tool
• a compliance enhancer
• a service accelerator

If adopted accidentally, AI becomes:

• a quiet hollowing of institutional capacity
• a transfer of control from policy to tool
• and eventually a governance failure that will be blamed on people who never had the chance to redesign the system

2026 is early enough to steer the transition.
Waiting will not preserve the old model. It will only ensure the new one arrives without a plan.

End note: I usually spend a couple of days (minimum) completing the compilation of all my bank and credit card records, assigning a classification, summarizing and giving my CPA a complete set of documents. I uploaded the documents to AI, gave it instructions to prepare the package, answering a list of questions regarding reconciliation and classification issues. Two hours later, I had the full package with comparisons to past years from the returns I also uploaded. I was 100% ready on New Year’s Eve just waiting for the 1099’s to be sent to me by the end of January. Meanwhile, I have been having AI enhance and create a comprehensive accounting system with beautiful schedules like cash flow, taxation notes, checklists with new IRS rules and general help – more than I was getting from CPA. I’ll be able to actually take over the CPA duties. It’s just the start of the things I can turn over to AI while I become the editor and reviewer instead of the dreaded grunt work. LFM