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

AI is already reshaping retail work across Israel: a Taub Center study shows average worker exposure to AI rose by at least five percentage points between 2023 and 2024, and flags commerce, financial and administrative roles as especially “substitutable” by technology - a sharp warning for cashiers, in‑store sales and telephone customer service staff (Taub Center AI and Labor Market study).

National surveys back this up: about 28% of Israeli firms report using AI and commerce adoption sits between 17–31%, with many adopters automating routine tasks while a majority in commerce say AI hasn't yet replaced jobs (OECD real‑time snapshot of AI adoption in Israel).

The result is mixed risk: some firms report small staff cuts (a minority), while women - concentrated in sales and service jobs - show higher exposure, so practical upskilling matters; for frontline workers, short, work‑focused AI training like Nucamp's Nucamp AI Essentials for Work bootcamp can turn risk into opportunity.

“People are losing jobs because of AI, but at this point, it is not as much as what was estimated before.”

Methodology: the list was built by matching concrete AI use cases to the realities of Israeli retail - scoring roles by how routine their tasks are, how exposed they are to automation tools identified in industry research, and how fast local adoption can scale.

Industry playbooks like CTA's

The Impact and Use Cases of AI in Retail

guided the scoring for automation risk (from inventory‑prediction to touchless checkouts and in‑store virtual assistants: CTA AI in Retail report: impact and use cases), while Israel‑specific constraints and accelerants were layered on top: compact geography that improves last‑mile optimization and SLAs (Last‑mile routing optimization in Israel for retail), and a vibrant startup and funding scene that speeds pilot to production cycles (Israeli AI startup ecosystem driving retail innovation).

Roles that repeatedly handle predictable, high‑volume tasks - think a system that can forecast which sweater will sell out before the weekend - ranked highest on the risk scale, and informed the final top‑five list and practical adaptation recommendations for workers and employers.

Retail cashiers and point-of-sale staff in Israel are on the front line of a stubborn paradox: customers - especially younger shoppers - often prefer the speed and control of self-checkout (77% say it's faster and more convenient), yet the machines shift hidden costs back onto workers through higher shrink, extra monitoring duties and bouts of understaffing (self-checkout preference and trends report).

Global retailers are already recalibrating - some have rolled back kiosks after losses and theft concerns - so Israeli operators weighing automation must balance throughput gains against service quality and safety (NBC News report on major retailers backtracking on self-checkout).

For many young or new-immigrant workers who rely on cashier jobs for first work experience, the shift narrows entry pathways even as it creates technical and attendant roles; local firms and training programs in Israel's vibrant AI ecosystem can help staff move from scanning to supervising, troubleshooting, and customer support (analysis of the Israeli AI startup ecosystem accelerating retail AI adoption).

“It's like I'm one person working six check stands.”

Customer service reps - both on the shop floor and on the phone - are already sharing the spotlight with AI in Israel, where a tight-knit retail‑tech scene is building the very tools that threaten routine support tasks while also creating new, higher‑skill roles.

Homegrown startups and platforms are pushing omnichannel automation and hyper‑personalization, from AI recommendation engines to WhatsApp ticketing flows that cut wait times and handle order queries round‑the‑clock (Israeli retail tech innovations driving AI automation; Wavetec examples of AI impact on retail customer service).

The upside: faster responses, predictive recommendations, and fewer repetitive calls - the global market was already about USD 12.10 billion in 2024 and is set for strong growth - yet the tradeoff is real for frontline workers, which is why many specialists argue for human+AI collaboration rather than full replacement (nearly half say agents should work with AI).

In practice that means reps will increasingly escalate complex, emotional or exception cases, while AI handles routine FAQs and tracking; firms that train staff to supervise bots and interpret analytics turn potential job loss into a pathway to supervisory tech roles.

Israeli vendors like Minded AI, Onebeat and Tymely are already selling these capabilities to retailers, so workers and managers who learn to pair empathy with prompt‑engineering and platform oversight will be the ones who keep customer trust intact as automation scales.

Retail sales associates - those who both close transactions and nudge the tempting upsell - are uniquely exposed as Israeli stores layer AI across the customer journey: conversational assistants and clienteling apps can answer stock questions, surface personalized add‑ons, and even complete the sale in seconds, turning “one moment, one interaction” into either a won upsell or a lost customer (conversational AI that meets shoppers instantly).

Equipping floor staff with the same data that powers web recommendations makes them more effective: AI‑driven suggestions and real‑time inventory (the “endless aisle”) increase average order value and rescue sales that would otherwise vanish, with cross‑sell/upsell programs shown to lift revenues materially (studies cite up to ~15% gains) (how AI improves cross‑selling and upselling).

In Israel, where a fast startup ecosystem is accelerating retail pilots, the practical play is clear - train associates to use clienteling tools, supervise AI prompts, and blend human judgment with machine recommendations so the store stays both personal and profitable (Israeli AI startup ecosystem accelerating retail AI).

“You're trying to make it super convenient, making sure people aren't waiting in lines… at the same time, you're leaving yourself exposed to the fact that some people might not do as well and ultimately walk out having only paid half for everything.”

Warehouse fulfillment staff and stock pickers in Israel face a fast‑moving squeeze: order picking already drives more than half of warehouse labour costs and up to 60% of process time, so technologies that speed receiving, picking, packing and shipping - automated sortation, goods‑to‑person (GTP) shuttles, AMRs and advanced WMS - go straight at the routine work that once kept stores and dark‑fulfillment hubs running (see the NetSuite warehouse automation trends and best practices primer at NetSuite warehouse automation trends and best practices).

In Israel's compact geography, last‑mile advantages and a lively startup scene mean pilots move to scale faster, so local retailers can cut walking, errors and shrink while raising throughput (read about how the Israeli AI ecosystem accelerates retail automation at Israeli AI ecosystem accelerates retail automation case study).

The practical response for workers is concrete: train on WMS and cycle‑count protocols, learn to supervise AMRs and pick‑to‑light systems, and shift into maintenance or data‑monitoring roles so human judgment stays in the loop - imagine a GTP shuttle delivering a tray to waist height and removing the tired miles from a picker's shift, not the person.

“Don't ever underestimate the importance of accurate order picking.”

Back‑office clerks who spend their days on data entry and basic bookkeeping sit squarely in the crosshairs of automation in Israel because the technology targets routine, rules‑based and data‑heavy tasks - the very work these roles do best; the Central Bureau of Statistics / OECD snapshot finds 28% of businesses now use AI, and among adopters 42% apply it to routine tasks, while paid systems (used by 17% of firms) are much more likely to change staffing patterns than free tools (paid users report workforce effects six times more often) (OECD and Israel Central Bureau of Statistics snapshot of AI adoption in Israel).

Employment impacts so far are modest - most firms report no headcount change - but a small share have cut hires or roles, so practical adaptation matters: targeted upskilling, familiarity with accounting automation and oversight of AI outputs, and policies that support retraining can turn clerical displacement into a move up the value chain, especially given Israel's fast‑moving startup scene that helps retailers deploy tools quickly (How the Israeli AI ecosystem accelerates retail automation).

A vivid test: work that once filled an afternoon of keystrokes can now be triaged by models, leaving humans to investigate only the exceptions.

“We are in the early stages of introducing artificial intelligence into the labor market, and its impact is still limited to specific areas.”

Conclusion: Israel's retail sector needs a three‑way, fast‑moving response: workers should build practical AI literacy - learn prompt craft, supervise models, and pivot to exception management - because many displaced tasks can be condensed from an afternoon of keystrokes to a ten‑minute investigation (see reskilling playbooks and worker sentiment in Nexford's surviving‑AI research, which finds 62% view AI skills as career insurance) (Surviving AI Layoffs: Reskilling Strategies - Nexford); employers must treat AI workforce planning as HR strategy - map skill gaps, run targeted upskilling and on‑the‑job “human+AI” pilots, and partner with local vendors so tools scale safely (Aon's guidance shows three out of four organizations now create roles requiring AI skills and calls for top‑down plus employee‑led assessments) (AI and Workforce Skills - Aon); and policymakers should fund short, affordable programs, apprenticeships and employer incentives linked to measurable re‑employment outcomes so youth and women - two groups with high retail exposure - get real pathways into tech‑adjacent roles.

Israel's compact geography and startup ecosystem make rapid pilots possible, so combine fast experiments with durable supports: short bootcamps that teach tool use, prompts and workplace application (for example, Nucamp's AI Essentials for Work) help turn disruption into mobility rather than displacement (Nucamp AI Essentials for Work).

“We're the last generation to manage 100 percent human teams. As we navigate the integration of AI agents, it's clear that our approach to AI literacy, reskilling and upskilling must evolve.”