Will AI Replace HR Jobs in Newark? Here’s What to Do in 2025

Newark HR leaders should care because the 2025 wave of generative and agentic AI is shifting HR from transaction-heavy work to high-value design: firms are under pressure to "hurry up" with productivity projects and many routine HR tasks can be automated, so local teams that redesign workflows, tighten governance, and quickly upskill will protect roles and influence, not just costs; see Josh Bersin's analysis on HR reinvention for context and practical urgency (Josh Bersin analysis of HR reinvention and urgency), and consider concrete training like Nucamp AI Essentials for Work 15-week syllabus for workplace AI skills to learn promptcraft, tool selection, and hands‑on prompts that move a Newark HR team from reactive to strategic.

AI, through its miraculous data integration and generation capabilities, can probably do 50 - 75% of the work we do in HR.

Generative AI is changing which HR tasks get done - not erasing the jobs themselves: Mercer's analysis finds AI can automate data entry, routine inquiries and large parts of program delivery so HRBPs, L&D professionals and total‑rewards leaders can shift time to strategy, coaching and equity work; for Newark teams this means adopting tools to offload transaction-heavy work while protecting human-led responsibilities like performance judgement, mental‑health support and pay‑equity design (Mercer analysis of generative AI in HR).

Pairing that redesign with humane governance and fairness principles can avoid productivity traps and build trust as adoption scales (Oliver Wyman Forum research on AI and the workforce).

So what: Newark HR teams that reallocate AI‑freed hours into manager coaching, targeted reskilling, and strategic workforce planning preserve roles and increase strategic influence rather than just cutting headcount.

If 2024 was the year of experimentation, 2025 has to be the year of benefit realization. As organizations continue to explore the promise of new technologies, leaders will need to pivot from a focus on cost optimization towards one centered on material returns for all stakeholders.

Mercer's research singles out three Newark-facing HR specialties most affected by generative AI - HR business partners, learning & development specialists, and total‑rewards leaders - but emphasizes task change, not wholesale replacement: HRBPs can expect talent-management work to shrink (Mercer models show talent time could fall by about one‑third), L&D design and delivery work shifts sharply (program design from ~35% to ~21% and delivery from ~34% to ~14%), and total‑rewards teams may see roughly 52% of hourly tasks affected - roughly five to six months of annual work time repurposed toward strategy and personalization rather than administration.

The so‑what for Newark: freed hours can fund manager coaching, pay‑equity reviews and localized benefits personalization that win and retain New Jersey talent, while governance and data‑quality work remain human priorities.

For practical next steps, review Mercer's role-by-role findings in

Generative AI will transform three key HR roles

and the deeper total‑rewards use cases in

AI is the future of total rewards

to map which tasks to automate, which to humanize, and where to invest in people analytics and training.

National data now shaping local risk: trackers such as TechCrunch 2025 tech layoffs tracker documents thousands of tech cuts this year (TechCrunch counted more than 22,000 known reductions with a February spike of 16,084), while investigative reporting shows automation is an increasingly explicit driver - Fortune report on AI-linked layoffs finds that over 10,000 2025 job cuts were directly linked to AI and that entry-level postings have fallen roughly 15% year‑over‑year - a direct threat to the pipelines Newark HR teams rely on.

Macro research from Goldman Sachs research on AI workforce impact tempers the panic with projection ranges (a modest short‑term unemployment uptick and a few percent of U.S. employment at risk) but underscores transition friction: so what for Newark - protect early‑career hiring and invest now in reskilling, internal mobility and manager coaching to stop short‑term cuts from hollowing long‑term talent pipelines.

“A recent pickup in AI adoption and reports of AI-related layoffs have raised concerns that AI will lead to widespread labor displacement,” - Joseph Briggs and Sarah Dong, Goldman Sachs Research.

Redesigning HR work in Newark starts with breaking roles into discrete tasks, then deciding which micro‑tasks AI should handle and which require human judgment - use decomposition's Work Breakdown Structure to map workflows, allocate resources, and set timelines so teams can safely automate repeatable steps (decomposition project management methodology for HR teams); pair that with deliberate role and job design to translate strategy into clear processes and responsibilities so AI becomes a tool for hybrid roles rather than a replacement (role design framework to break strategy into processes and tasks).

Adopt work‑design principles - align tasks with skills, promote job crafting, and build platform-supported pathways for internal mobility - to convert administrative load into measurable people work like manager coaching, pay‑equity reviews, and localized reskilling clinics, using a work‑design platform to track outcomes and employee satisfaction (work design best practices for reshaping job roles); the practical payoff for Newark HR: clearer roles, faster decisions, and preserved jobs that shift from processing to human-centered value creation.

Newark HR teams can turn disruption into advantage by pairing a focused skills-gap audit with targeted, low‑friction learning pathways: use the NJIT LDI Workforce Readiness Model to map eight priority domains (two foundational + six specialized) and prioritize AI literacy, data literacy, and change‑navigation for HRBPs, L&D leads and total‑rewards specialists (NJIT LDI Workforce Readiness Model for workforce readiness); run the five‑step program Helios recommends - identify gaps, set clear employee goals, enable self‑advocacy, offer blended microlearning and hands‑on projects, and embed learning in performance reviews - to convert freed hours into manager coaching and pay‑equity work (Helios five-step upskilling playbook for AI-powered workplaces).

Stretch budgets by combining state resources like the SkillUp NJ free training program with local bootcamps and certificates (SkillUp NJ free training and reskilling resources); one memorable payoff: AI‑fluent staff can reclaim substantial time - LDI cites AI‑fluent workers saving roughly 81% more time - so investing in rapid, role‑aligned reskilling preserves jobs and creates measurable capacity for higher‑value HR work.

"With less than 40% of the workforce operating at AI-fluent or -literate levels, organizations are desperate for professionals who can accelerate their AI adoption and effectiveness."

Newark HR teams should follow three concrete operational steps: pick pragmatic HR technology that centralizes data and automates transactions (examples include HR service-delivery platforms and identity/IAM workstreams recommended by NJIT's digital workplace strategy), pair every tool purchase with a vendor‑collaboration plan and regular bias testing, and build clear employee notice, training, and audit routines so legal risk is managed under New Jersey law; state programs and hubs can accelerate this work (the NJ AI Hub opened in 2025 with founding partners including Princeton, NJEDA, Microsoft, and a $72M+ investment commitment).

Start by running short pilots with measurable KPIs, require vendor bias-audit evidence and annual reviews (A4909-style scrutiny is moving through the Legislature), and train HR staff to interpret models and explain decisions so the employer - not the vendor - stays responsible for outcomes.

For practical guidance, review the state AI resources at Choose New Jersey AI and economic resources, the Employers Association of New Jersey guidance on bias audits and notification, and the New Jersey Attorney General's office algorithmic discrimination materials to shape governance, procurement, and staff training now.

Start the next six months with a clear, testable playbook: month 0–1 run a focused AI risk assessment and apply privacy tags to HR data (offers, salaries, applicant files) as recommended in the NJBIZ AI strategy panel, then run 2–3 short pilots (months 1–3) that measure concrete KPIs - hours saved, error rates, and data‑leak risk - while insisting on vendor bias audits and transparent usage policies; months 4–6 convert measured time savings into manager‑coaching capacity, internal‑mobility pilots, and role‑aligned microtraining so freed hours preserve jobs instead of cuts.

Use state momentum (the NJ AI Hub and task‑force planning) to secure partners and consider small bridge funding: NJIT's Rapid Response Bridge grants offer up to $25,000 for a 6‑month plan to cover pilot analytics, data collection, or closing activities, which can make a short ROI case to leadership.

Track outcomes rigorously, publish simple governance rules for tool use, and prioritize explainability so Newark HR teams protect employees while proving AI's operational value.

“Privacy tags – super important. Start doing that around your HR, your offer letters, your salaries, things like that.” - Carl Mazzanti

Common beginner questions from Newark HR teams usually ask whether AI will eliminate roles, how to prevent bias, what to do about fake applicants, and where to start safely - short, practical answers: AI is reshaping tasks more than erasing jobs (see Tulane Law's overview of AI's impact on HR processes for adoption and risk context Tulane Law: Impact of Artificial Intelligence on HR Processes); require vendor bias audits, transparent disclosures, and privacy controls before deployment (vendor compliance and data‑privacy cautions are core guidance); treat hiring fraud and deepfakes as a real security issue and partner with IT on stronger verification and detection workflows (Marsh MMA documents how generative AI can create convincing fake applicants and practical defenses Marsh MMA: AI and HR - The Rise of Fake Job Seekers); and start with short pilots, measurable KPIs, and mandatory vendor evidence of bias testing - one concrete, memorable step: insist on identity verification tools like the Greenhouse–CLEAR style integrations HR vendors now offer to reduce AI-driven hiring fraud (HR Executive: How HR Teams Can Boost AI Security and Vendor Solutions).

These steps get Newark teams from uncertainty to defensible, human‑centered adoption in months, not years.

“Fraudulent candidates can put you at risk for cyber threats. It's essential to stay alert and take proactive steps to manage these risks.”

Newark HR must choose reinvention over retrenchment: by tightening governance, running short pilots with measurable KPIs, and rapidly building AI literacy they can shift routine work to safe automation and redeploy the equivalent of months of annual effort into manager coaching, pay‑equity reviews, and internal mobility - concrete steps underscored by Josh Bersin's call to “fix the plumbing” before asking AI to boost productivity (Josh Bersin on HR reinvention and urgency) and by the BCG finding that leadership-backed training and tooling materially raise employee trust and adoption (BCG AI at Work 2025 report on adoption and trust).

Practical next moves for Newark teams: require vendor bias audits, add privacy tags, run 2–3 short pilots, and invest in role‑aligned reskilling - training options such as the Nucamp AI Essentials for Work 15-week syllabus (AI at Work) give HR staff hands‑on prompt and tool skills so human judgment, not headcount, becomes the local competitive advantage.

AI, through its miraculous data integration and generation capabilities, can probably do 50 - 75% of the work we do in HR.