Will AI Replace Finance Jobs in Chicago? Here’s What to Do in 2025

For Illinois finance workers, “Will AI replace finance jobs in Chicago?” is already a local planning question: a Brookings-based analysis highlighted by Crain's shows Chicago-area white‑collar roles - including many finance positions - rank among the most exposed to generative AI (see Crain's article on the Brookings analysis at Chicago Business: https://www.chicagobusiness.com/technology/ai-chatgpt-will-hit-jobs-white-collar-areas-chicago).

States and workforce groups are moving to beef up tech and AI literacy to keep workers competitive (read about Illinois and state upskilling efforts at the Chicago Tribune: https://www.chicagotribune.com/2024/06/29/as-ai-gains-a-workplace-foothold-states-trying-to-make-sure-workers-dont-get-left-behind/), and employers who embed early adopters and clear AI use cases reduce displacement risk and capture value.

Practical takeaway: Illinois finance professionals should prioritize AI‑at‑work skills - prompting, tool workflows, and governance - available in focused programs such as the Nucamp AI Essentials for Work registration (Nucamp AI Essentials for Work registration: https://url.nucamp.co/aw).

“What we need is to lean into things that complement AI as opposed to learning to be really bad imitators of AI.” - Gregory LaBlanc.

AI is already reshaping how Chicago finance teams work: firms are shrinking fixed footprints in favor of collaborative, high‑quality space and more frequent space‑planning cycles, and hybrid work remains entrenched (fully remote rose to ~11.1% while hybrid use also climbed), so treasury and FP&A groups must support distributed teams and remote workflows (Bisnow report on AI-driven office sector transformation; Crain's analysis of RTO and hybrid work trends in Chicago).

At the same time, McKinsey projections cited by Bisnow flag steep exposure for processing and administrative roles - about 710,000 administrative assistants globally could be displaced by 2030 - while corporate playbooks decouple headcount from revenue as AI automates routine reconciliations and reporting; the immediate, local “so what?” is concrete: average usable office density fell to roughly 132 sq ft per person in 2025 (a 20% drop from 2024), which forces leaner back‑office footprints and accelerates demand for finance pros who can pair domain knowledge with AI tool workflows and governance.

Practical next steps for Chicago finance teams include adopting vetted tool chains and upskilling on prompts, automation recipes, and compliance checks - see curated tool recommendations for city finance pros in Nucamp's AI Essentials for Work syllabus and Top 10 AI tools guide (Nucamp AI Essentials for Work syllabus and Top 10 AI Tools guide).

“Corporate real estate portfolios need to be agile, fluid and liquid, because the changes are continuing.” - Peter Miscovich (JLL)

In Chicago the most exposed finance roles are the high‑volume, rule‑based jobs that list repetitive tasks - accounting clerks, accounts payable/receivable processors, payment coordinators and documentation specialists - because their day‑to‑day (invoice entry, PO matching, ACH runs and basic reconciliations) maps cleanly to automation: Zippia reports ~767 accounting clerk openings in Chicago with a median near $40,000, a large local pool that employers can streamline with tools (Zippia Chicago accounting clerk job listings and median salary).

By contrast, roles that require judgment, technical accounting, cross‑functional negotiation or complex GAAP work - senior staff accountants, technical accounting consultants and accounting managers - remain harder to fully automate and command materially higher pay and protection; Robert Half's listings show the same market split across routine and senior roles (Robert Half accounting and finance job market listings).

So what: if current duties are “process + data entry,” upskilling to validate AI outputs, design automation checks and own exceptions is the fastest way to stay indispensable in Chicago's evolving finance teams.

Chicago finance professionals should build a tightly focused skill stack in 2025: core data tooling (Python, SQL, Excel) and visualization (Tableau, Power BI with Power Query and DAX) to own data pipelines and automate reporting, plus applied ML and generative‑AI literacy to evaluate model outputs and embed safe automation into close and forecasting workflows - skills taught in local programs like the UIC Data Analytics Bootcamp (Chicago data analytics bootcamp).

Add strategic data storytelling and governance so AI outputs can be audited and explained to controllers and auditors; for deeper finance applications, the University of Chicago's online Machine Learning for Finance course is an eight‑week program (starts Oct 6, 2025; $2,800) that trains Python/Pandas and simulation for portfolio and risk work (UChicago Machine Learning for Finance - course details).

Rapid, modular Power BI training - modules from single‑day Power Query workshops to multi‑day DAX classes - lets accountants convert repeating reports into governed dashboards and reclaim hours each close cycle (RADACAD Power BI training - workshop details).

The so‑what: a focused 10–26 week upskill path can move a processor into an analytics role that supervises automation rather than competes with it.

“By directly involving students in the challenge of securing the right data and exploring potential solutions, we're equipping them with the foundational skills required to deliver tangible results to clients.” - Greg Green, PhD

A practical

AI + Me

routine for Chicago finance workers starts with automated reconciliations first thing in the close: run a Numeral month-end reconciliation automation to surface mismatches and reduce manual journal entries and errors, then load AR aging into a prompt template so forecasts reflect collector notes and aging buckets (Numeral month‑end reconciliation automation for Chicago finance teams; AR aging prompt templates for smarter forecasts in Chicago).

Next, run an AI‑enabled anomaly and compliance scan tied to local rules to flag potential fraud or regulatory issues, exporting exceptions into a single workbook for human triage (AI-driven fraud detection and compliance scanning for Chicago finance).

Finish with a short validation checklist - verify top five variances, document prompts and model versions, and assign exception owners - so outputs are auditable and the team shifts from firefighting late-close corrections to governed automation that prevents recurring errors.

Chicago employers should redesign finance teams by shifting routine data‑entry and reconciliation work into governed automation while creating clear human roles for exception management, AI validation, and data stewardship so judgment stays inside the organization; this worker‑centered job redesign approach - co‑developed with employees or unions - mirrors Aspen Institute recommendations to protect agency, prohibit AI discipline, require transparency in data and models, guarantee the right to override automated systems, and embed meaningful retraining (Aspen Institute report on worker power, agency, and autonomy).

Practically, that means stand up a cross‑functional AI governance group (labor, finance, IT, compliance), enforce human sign‑off on any AI decision that affects pay, audit model versions and prompts for regulatory review, and fund targeted upskilling pathways - such as cohort training and tool‑chain onboarding - so processors move into roles that oversee automation rather than compete with it; employers that pair governance with training (for example, Nucamp AI Essentials for Work syllabus and toolchain training) make transitions auditable and reduce disruption while preserving institutional knowledge.

Chicago finance firms still need humans because current AI systems create legal, security, and explainability risks that only people can manage: courts and scholars urge treating AI as “risky agents without intentions” and holding the human designers, deployers, and supervisors to objective standards of care - so liability, not the model, falls on people (UChicago Law Review article on AI as risky agents without intentions).

Regulators are pressing the same point: the CFTC's 2025 roundtable emphasized operational resilience, third‑party oversight, and post‑deployment testing (July 14, 2025) so explainable human review remains mandatory for trading, surveillance, and compliance systems (CFTC 2025 roundtable on AI and operational resilience).

Engineering research shows model integrity and confidentiality vulnerabilities - poisoning, jailbreaks, memorization - that require human-in-the-loop controls, versioned audits, and governance playbooks; practitioners in access‑to‑justice and legal aid pilots demonstrate how supervised AI copilots reduce harm when paired with trained reviewers (Stanford Justice Innovation Lab AI for Access to Justice human-in-the-loop R&D).

So what: require documented human sign‑off, prompt/version logging, and exception owners to make AI-driven finance both productive and defensible.

“the law of AI is the law of risky agents without intentions.”

Chicago finance professionals can choose practical, local routes to learn the AI and data skills firms now expect: the University of Illinois Chicago offers both a STEM‑designated, AACSB‑accredited Master of Science in Finance that pairs finance depth with market tools (UIC MSF reports 94% of 2022 grads employed within six months and an average starting salary of $82,000) - see the UIC MSF program - while UIC's Data Analytics Bootcamp teaches Python, SQL, Excel and Tableau in a 10–26 week format ideal for moving processors into analytics roles; for applied machine‑learning and model literacy, the University of Chicago's live, online Artificial Intelligence and Machine Learning short course (8 weeks, CEUs) builds practical ML and Python skills employers prize.

“By directly involving students in the challenge of securing the right data and exploring potential solutions, we're equipping them with the foundational skills required to deliver tangible results to clients.” - Greg Green, PhD

Chicago's finance jobs face a compressed timeline: a Datarails survey finds 57% of CFOs expect finance headcount to shrink by 2026, and 70% plan to invest in AI for the CFO's Office in 2025, signaling rapid adoption across mid‑market firms (Datarails 2025 CFO AI survey); complementary US CFO research shows leaders shifting AI use from pure automation toward strategic forecasting and risk work, while naming security and governance as top constraints (Kyriba US CFO AI adoption 2025).

So what: expect the next 18–24 months to be decisive - transactional roles will compress but demand will rise for controllers and analysts who can validate models, own prompt/version audit trails, and run governance; hiring will favor AI‑literate candidates as job listings increasingly call for AI skills, not just Excel.

Practical takeaway for Chicago workers and managers: treat 2025 as the transition year to move from processing to oversight, or risk obsolescence as toolchains scale.

“CFOs are spending both record time and sums on aggressively building AI capabilities which will reshape every finance team and process in the next two years. We're not just seeing technology adoption – we're witnessing a new breed of financial leader betting on AI to drive business transformation and advance their career ambitions.” - Didi Gurfinkel, CEO & Co‑founder, Datarails

For Chicago finance professionals the practical roadmap in 2025 is clear and immediate: prioritize short, applied training that pairs core data skills with AI‑at‑work discipline, then move quickly into roles that own exceptions, validation and governance.

Start with a focused analytics pathway - Python, SQL, Excel and Tableau via the UIC Data Analytics Bootcamp (10–26 weeks) or a 15‑week applied course such as Nucamp's AI Essentials for Work to master prompting, tool chains and audit-ready workflows - and add targeted model literacy with the University of Chicago's eight‑week Machine Learning for Finance offering (starts Oct 6, 2025).

These options convert routine processors into AI‑literate controllers in as little as 10–15 weeks, which matters because hiring will favor AI‑capable candidates during the decisive 18–24‑month transition window; the practical “so what” is simple: one short, employer‑aligned credential plus a governance habit (prompt/version logs and human sign‑offs) reliably shifts workers from replaceable entry tasks to oversight roles that firms still need.

Explore program details and registration below to pick the fastest route that matches your role and employer expectations.

“By directly involving students in the challenge of securing the right data and exploring potential solutions, we're equipping them with the foundational skills required to deliver tangible results to clients.” - Greg Green, PhD