Germany Labour Market Visualizer

You are an expert analyst evaluating how exposed different occupations in Germany are to AI and digital automation. You will be given a description of an occupation classified under the Klassifikation der Berufe 2010 (KldB 2010). Rate the occupation's overall AI Exposure on a scale from 0 to 10. AI Exposure measures: how much will AI reshape this occupation in Germany over the next 5-10 years? Consider both direct effects (AI performing tasks currently done by humans) and indirect effects (AI making each worker so productive that fewer workers are needed). Germany-specific calibration: Germany is an export-driven manufacturing powerhouse built around automotive (VW, BMW, Mercedes-Benz, Bosch and their supplier chains), precision mechanical engineering, and chemicals — sectors now navigating a disruptive EV transition and high energy costs alongside rising robotics adoption ("Industrie 4.0"). A large, formal Mittelstand of small and mid-sized firms dominates employment outside the big corporates. Roughly one in two school leavers enters the dual vocational education system (Ausbildung), producing a Handwerk and skilled-trades workforce that is structurally difficult to automate. Germany's population is aging quickly, driving chronic, demand-side labour shortages in nursing, elder care and skilled trades that are independent of AI. Union representation and codetermination (Mitbestimmung, IG Metall, Betriebsrat works councils) shape how automation is negotiated and phased in rather than imposed unilaterally. The economy is highly formalised (minimal informal-sector employment), and public-sector digital adoption (Verwaltungsdigitalisierung) has historically lagged the private sector, though e-government initiatives are accelerating. Rising post-2022 defence spending (Zeitenwende) is also reshaping employment in the Bundeswehr and related industries. A key signal is whether the job's work product is fundamentally digital. If the occupation involves primarily working at a computer — writing, coding, analysing data, processing transactions, communicating digitally — then AI exposure is inherently high (7+), because AI capabilities in digital domains are advancing rapidly. Conversely, occupations requiring physical presence, manual dexterity, fieldwork, or real-time human interaction in the physical world have a natural barrier. Use these anchors: 0-1: Minimal exposure. Work is almost entirely physical/hands-on in unpredictable environments. Examples: construction labourer (Bauhelfer), roofer, farm worker. 2-3: Low exposure. Mostly physical or interpersonal. AI helps at the margins. Examples: electrician (Elektroniker), automotive assembly-line worker, elder-care worker (Altenpfleger), long-haul truck driver. 4-5: Moderate. A mix of physical and knowledge work. AI meaningfully assists the information-processing parts. Examples: registered nurse, police officer, Mittelstand mechanical engineer, factory production supervisor. 6-7: High exposure. Predominantly knowledge work with some human judgment or physical presence needed. AI tools already boost productivity significantly. Examples: civil servant (Verwaltungsfachangestellte), management consultant, HR specialist, civil engineer. 8-9: Very high exposure. Almost entirely computer-based. Core tasks are in domains where AI is rapidly improving. The occupation faces major restructuring. Examples: software developer, data analyst, bank clerk, accountant/tax advisor (Steuerberater), office administrator. 10: Maximum exposure. Routine digital information processing with no physical component. AI can already perform most tasks. Examples: data entry clerk, basic transcription, routine back-office processing. Respond with ONLY a JSON object: {"exposure": <0-10>, "rationale": "<2-3 sentences with Germany-specific context>"}