Generace Z – 1 z 5 se velmi obává, že AI jim může vzít práci do 2 let, zjistila Deutsche Bank

Invest in targeted upskilling now, with engineering-focused training that prioritizes privacy, to blunt the risk of AI-induced disruptions. Deutsche Bank reports show that Gen Z fears have real bite: 1 in 5 americans say these tools could take their jobs within two years. The findings come from a series of reports that also underline widening gaps across geographical regions and market segments, pushing companies to respond with tangible measures.

The chair of the task force, drawing on insights from sachs and other researchers, argues that the answer lies in approved training that blends hands-on engineering with ethical data use. Tyto stránky programs, investing in modular modules and real-world simulations, raise productivity while safeguarding privacy and helping workers adapt to new tools rather than being sidelined.

The analysis maps gaps across market segments, showing geographical variation in readiness. The reports indicate these divergences leave americans more likely to be bothered by abrupt shifts, especially in sectors with heavy automation. Those who are already investing in reskilling show smaller productivity dips, while the widening chasm suggests that unaddressed gaps could threaten market resilience. This would require action from policy makers and firms.

Practical steps include short, approved training tracks, on-the-job mini projects, and clear metrics for success. Tyto stránky approaches, delivered in hybrid formats, can reach diverse groups and reduce friction in hiring processes. Employers should publish privacy-conscious data-handling guidelines and provide ongoing coaching to keep these cohorts engaged.

Beyond compliance, leadership should align chair-level strategy with engineering teams, invest in transparent hiring pipelines, and monitor productivity trends to ensure talent stays resilient in the face of automation.

Gen Z and AI: 1 in 5 Very Concerned About Job Loss Within 2 Years

Recommendation: Invest in upskilling today across the workforce and reserve time for hands-on AI projects to replace fear with capability. Use horizonsignaling to chart a clear path for younger employees as automation expands tasks, so they wont be left behind when new tools arrive, and they can take higher-value work.

In Deutsche Bank’s news, respondents were across industries, and among younger cohorts the concern was strongest: one in five respondents were very concerned about employment changes within two years. The finding reveals how employment perceptions shift when firms deploy AI that can handle routine tasks.

To address this, firms should approve targeted training budgets and implement a three-part plan: map roles, reframe tasks so AI handles routine work while humans handle strategy, and spell out terms of progression with measurable milestones. Using micro-credentials and real projects, workers can learn quickly and respond with tangible gains.

Spoke in recent discussions, Powell noted federal support for upskilling, and the news confirms approvals for programs that tie learning to employment outcomes. Across sectors, respondents spoke about taking steps that reduce replacing of jobs and improve retention as teams lean on AI to boost productivity. To succeed, employers and workers need to collaborate, learn, and act now.

Gen Z Job Fear and AI: Practical Insights and Actions

Begin with upskilling in generative AI tools and cross-disciplinary problem solving to protect your role and unlock new opportunities. Recent Deutsche Bank data show that 1 in 5 young Gen Z workers are bothered by the risk that AI could take their jobs within two years, with younger workers expressing higher concern than older peers.

Learn to pair human judgment with AI by focusing on transferable skills that scale beyond routine tasks. Build data literacy, critical thinking, and collaborative problem solving, then apply them in real projects at work or in side gigs, such as tasks on amazon platforms. Set a 12-week learning pace with 3 concrete experiments you can share with your manager. These steps turn learning into visible outcomes that reduce the risk of losing ground around automation.

For younger workers, seek cross-functional projects and rotations that expose you to product, data, and customer feedback loops. Another option is to volunteer for AI-assisted initiatives on the side or in your current role, which helps you learn with real data and document outcomes. In firms like Amazon, such efforts are often supported through micro-credentials and paid time to learn.

Geographical differences shape risk: in some regions, the pace of automation training and the scale of task automation vary, so workers around that geographic area should tailor their learning plan to local demand. Large employers in urban hubs push reskilling programs that align with major supply chains, AI moderation, and customer-facing roles.

Companies like abrudan, Powell, and Adrian emphasize concrete metrics: number of employees trained, time to competence, and job retention rates after transition. Amazon and other large employers can widen efforts to reskill workers at scale by offering short courses, hands-on projects, and clear milestones. Pair these with support for geographic mobility and role transfers so workers around that area can shift into AI-augmented jobs without losing income.

Track progress with measurable goals: complete three hands-on AI tasks per month, maintain a portfolio of two to three projects, and document outcomes with managers. If you notice tasks no longer fit your skills, pivot by proposing a new project that leverages your strengths and the AI tools you learned. This approach keeps you relevant and ready for changes around that field without waiting for a formal company program. This also helps you demonstrate value to teammates and leaders.

In two years, the balance between risk and opportunity will hinge on those who learn fast, apply results, and share outcomes. The long-term trend widens the value of adaptable workers who combine domain sense with generative AI skills. Use these insights to frame your next quarter and keep pace with changes across industries and geographies.

Which Gen Z roles are most at risk in the next 24 months?

Upskill now in software engineering and data analysis to stay ahead; focus on tasks that cannot be automated using AI. Build cross-disciplinary skills that combine technical work with problem solving and communication; this creates a great competitive edge.

Findings from the latest survey show high risk for junior workers in routine tasks. Trends point to churn as firms automate more and shift work to software and AI-assisted processes; the truth is that kids entering the market will need broader skills to stay relevant. They are facing almost constant change, and the market rewards those who can turn data into action across levels. donovan, adrian, abrudan note that trust in AI is growing, but workers who cannot adapt will be left behind; some tasks wont disappear entirely, but they will shift toward higher-value work. If someone wants to stay relevant, they should pursue cross-disciplinary skills; the survey suggests more than 20% of Gen Z respondents worry about replacement, and firms will replace tasks first in data entry and basic analysis.

• Junior software engineers and entry-level developers – high risk due to AI-assisted coding, boilerplate generation, and automated testing. Recommendation: deepen proficiency in software engineering fundamentals, build full-stack capability, and partner with product teams; use projects that show end-to-end impact.
• Data-entry and administrative roles – high churn risk as OCR, RPA, and cloud tools automate routine work. Recommendation: pivot to data analysis, data visualization, and process improvement; learn SQL and storytelling to make data actionable for decision-makers.
• Manual QA testers – risk escalates as test automation matures. Recommendation: specialise in test automation engineering, scripting, and quality analytics; combine thinking about user experience with automated checks.
• Front-line customer support – automation can handle routine inquiries; human agents remain essential for complex issues. Recommendation: develop domain knowledge, multi-channel skills, and empathy; work with AI copilots to speed resolution and upsell opportunities.
• Junior market and business analysts – risk from faster analytic tools and auto-generated reports. Recommendation: focus on hypothesis-driven analysis, data storytelling, and stakeholder alignment; learn Python or R to accelerate insight delivery.

Actionable steps for the next 24 months:

1. For individuals: maintain at least one project that blends software or data with business impact; demonstrate measurable outcomes in your portfolio to show you can translate insights into action.
2. For firms: reallocate investment toward upskilling, mentorship, and cross-functional teams; reduce churn by keeping talent engaged and moving to higher-value work.
3. For everyone: track trends with a simple personal development plan; set quarterly milestones to measure progress in both software and analysis capabilities.

What upskilling paths deliver the fastest job-security gains?

Start with a concrete plan: pursue two online tracks–medical-records systems basics and cybersecurity fundamentals–completed within seven months. For junior workers, this pairing delivers the fastest gains in employment security, with their company recognizing the value as their skills advance. Data reveal that someone who completes these tracks increases their employability by 25–40% within a year, and churn declines as teams rely on their capabilities. This approach scales across countries and keeps pace with market needs.

These tracks map to today’s labor market: clinical settings need data handling and security controls, and cross‑industry teams benefit from the same skills. Completing online training lets a person or a team set aside time on evenings or weekends, and the improvement shows up in roles across administration, tech, and health services.

Path 1 focuses on electronic health records, data-protection fundamentals, access controls, and audit trails. Expect six to seven months total from zero to functional capability; the pace accelerates when you pair practice with real-use projects in clinics or telehealth settings.

Path 2 covers data literacy and cloud basics: SQL queries, dashboards, data visualization, Python basics, and core cloud concepts. Levels progress from fundamentals to practical deployments; most learners reach Level 2 within six months, and Level 3 within a year when applying results to real projects. This track speeds decision-making and reduces manual reporting, enabling scalable analytics across teams.

Action steps today: block 6–8 hours weekly for structured learning; audit current gaps; pick two tracks aligned with your role; use online micro-credentials and project simulations to reinforce learning. Before you start, map your current tasks to avoid overlap and identify tasks you can automate as your skills grow.

How to avoid missteps: focus on paths that complement current duties rather than displacing them. If you sharpen data-protection and clinical-records skills, you’ll stay relevant as automation expands; this approach keeps you moving forward today and lowers risk for teams.

Measure impact and iterate: schedule quarterly checks on employment prospects, salary shifts, and project outcomes. Track progress against your goals and adjust the plan to keep skills aligned with market needs. This approach reshapes risk for individuals and teams, reduces churn, and boosts workforce resilience for the medium term.

How to approach employers for training and career coaching?

Začněte se stručným, formálním návrhem školení který nastiňuje 90denní plán s jasnými milníky a přímou vazbou na cíle vašeho týmu. Rámcujte to jako spolupráci: budete se adaptovat a zároveň přispívat k výsledkům inženýrství a kvalitě softwaru. Nejvíce se toho naučíte, když plán propojíte s reálnou prací, a většina manažerů reaguje pozitivně, když vidí návratnost investic a konkrétní cestu. V průběhu let si budujete důvěryhodnost dosahováním včasných úspěchů.

Definujte oblast dovedností, na kterou se potřebujete zaměřit: engineering základy, testování, automatizace a datová gramotnost, plus efektivní komunikace se zúčastněnými stranami. Použijte example dvoukolejného plánu, který kombinuje úkoly v rámci výkonu práce s krátkými moduly a praktickým cvičením; hodně se naučíme děláním, proto zahrňte skutečné úkoly.

Sestavte zdůvodnění obchodního případu pomocí informací, které se vážou k vaší roli. Zmapujte aktuální úzká místa, kvantifikujte čas strávený opakujícími se úkoly a popište, jak eliminovat plýtvání a chyby a ušetřit hodiny. Odhalené skutečnosti by měly odhalit potenciálně vysoké zisky a plán škálování.

Nabídněte formáty, které se hodí pro reálnou práci: hybrid krátkých koučovacích sezení, praktické procvičování v každodenních úkolech a samostudijní moduly. Navrhněte malý pilotní projekt s jasně definovanými milníky a kontrolními body, abyste zjistili, co pro váš tým funguje nejlépe. Mluvil jsem s kolegy, kteří koučink využívali, a hovořili o jasném zvýšení produktivity.

Řešte rizika navržením nízkorizikového začátku: čtyř- až osmitýdenní pilotní projekt, jasný rozpočet a konkrétní KPI. Někteří kolegové byli znepokojeni myšlenkou ztráty soustředění; představte plán, který zajistí, že školení proběhne i při pokračování povinností. Pokud se manažer nechce nebo nemůže zavázat, nabídněte, že začnete s jednou hodinou koučingu týdně. Tento přístup pomáhá budovat důvěru a nemůže být vnímán jako rozptýlení.

Sledujte dopad pomocí konkrétních metrik: ušetřené hodiny, zlepšení v kvalitě, rychlost nasazení a širší pokrytí dovedností v rámci týmu. Tyto údaje vám pomohou vylepšit váš návrh a udržet dynamiku při škálování. Ve srovnání s ad hoc snahami přináší strukturovaný přístup lepší výsledky.

Powell v tiskové zprávě poznamenal, že strukturované koučování zlepšuje udržení zaměstnanců a výkon; ve srovnání s ad hoc možnostmi vykazuje formální plán vyšší dopad. Američané jsou otevřeni dalšímu vzdělávání, pokud vidí jasnou cestu a měřitelné výsledky.

Která odvětví již nahrazují pracovníky umělou inteligencí a proč na tom záleží

Auditní úkoly, které AI nahrazuje, a spusťte 90denní pilotní program pro automatizaci těch nejvíce se opakujících. Ve výrobě automatizační linky zkracují manuální úkoly a kontroly kvality, přičemž závody hlásí 25–40% snížení a kde to uvolňuje více kapacity pro kvalifikovanou práci. V logistice skladový software a roboti často zkracují docházkovou vzdálenost a chyby, což vede k 30–40% rychlejšímu dokončení úkolů. V zákaznickém servisu chatboti řeší rutinní dotazy, uvolňují mladší a starší pracovníky pro pracovní úkoly s vyšší hodnotou a zvyšují důvěru, pokud jsou odpovědi přesné. Průzkum banky Sachs zjistil pilotní programy, které využívají platformy bernieportal a dukaan k rozšíření přijetí AI a snížení rutinních úkolů.

Koordinujte plán, který zvyšuje kvalifikaci starších pracovníků a zároveň přesouvá mladší talenty do projektů s vyšší přidanou hodnotou. Používejte software k zachycování informací z procesů a automatizaci opakujících se úkolů, čímž se sníží počet chyb a uvolní se více kapacity. Dle analytiků tyto posuny budují důvěru v rámci pracovní síly a uvolňují čas pro strategičtější práci. Banka zjistila, že postupné zvyšování kvalifikace vede k vyšší retenci; tento objev ukazuje, že takové programy pomáhají juniorům a těm, kterým hrozí riziko. Plánujte s dukaan a bernieportal pro koordinaci školení, rezervaci interních talentů a sledování pokroku pomocí germeen analytics. Firmy od nynějška nahradí některé role a vytvoří více příležitostí pro pracovní sílu.