To what extent does current Australian AI policy implicitly assume that adoption will follow existing digital-infrastructure and higher-education hubs, and how would recommended interventions change if we instead optimised for levelling up low-adoption regions and use-cases (e.g., apprenticeships, small regional firms, remote schools) as the primary objective rather than treating them as spillover beneficiaries?

Answer

Current Australian AI policy mostly tracks existing digital and higher‑education hubs, with only partial, secondary attention to low‑adoption regions. A true “levelling‑up first” objective would shift both the unit of analysis and the intervention mix.

1. Implicit assumptions in current policy

• Most strategies target: research universities, capital‑city innovation precincts, large firms and central agencies.
• Regional and low‑adoption users (apprentices, small firms, remote schools) are usually framed as downstream beneficiaries of national capability, not as primary design targets.
• Funding and pilots are frequently competitive grant–based, favouring already organised, proposal‑capable metro institutions.

2. How this shapes adoption patterns

• It reinforces adoption concentration around existing digital and higher‑education hubs.
• Public‑sector AI guidance, panels and shared services tend to be designed by and for central agencies, with later extension to regions.
• Workforce programs skew to degree pathways and tech occupations rather than apprenticeships and mixed‑skilled regional work.

3. What changes under a levelling‑up objective If the explicit goal were to reduce adoption concentration, not merely raise averages, recommended interventions would change along five axes:

A. Targeting

• Allocate a fixed share of AI funding to low‑adoption regions/use‑cases using place‑based criteria (e.g., outer regional/remote, small firms, non‑university education).
• Make regional TAFEs, RTOs, local councils, and small health/aged‑care providers priority channels, not optional partners.

B. Program design

• Default to simple, entitlement‑style or formula‑based support (per‑student or per‑apprentice AI tools and training; per‑school or per‑firm starter packages) instead of large, one‑off competitive grants.
• Provide centrally procured, low‑friction AI tool bundles with support, so small regional institutions do not have to run complex procurements.

C. Skills and use‑case mix

• Focus on applied AI for trades, care, and regional SMEs (job‑site planning, documentation, compliance, basic data analysis), not just software and research roles.
• Embed AI modules in apprenticeships, VET, and teacher PD for regional and remote schools, tied to simple classroom and admin use‑cases.

D. Institutional supports

• Fund shared regional AI “enablers”: state‑level security/assurance services, helpdesks, and template packages that regional councils, TAFEs and schools can reuse.
• Create a few regional demonstration sites (e.g., one hospital network, one TAFE system, one council cluster) and then standardise and replicate their patterns.

E. Metrics and accountability

• Track and publish per‑capita and per‑worker AI usage by region, sector and education pathway, and link part of funding to reducing these gaps.
• Evaluate programs on narrowing disparities (e.g., remote vs metro student access to AI tools; SME vs large‑firm uptake), not just total dollar spend or headline adoption.

Net effect: shifting to a levelling‑up objective would prioritise simple, low‑friction, region‑first programs and shared infrastructure over research‑centric and metro‑centred innovation initiatives, aiming to pull low‑adoption regions and use‑cases up rather than assuming they will catch spillovers.