AI becomes productive when firms complement it with skills, quality data, management capability and supporting institutions. Turning AI into Productivity draws on a case base of 110 place-based innovation ecosystems and four weeks of site visits to show where Australia is strong, where the gaps are, what seven recommendations could do to capture the opportunities, and how they could be implemented. A UTS research project funded by the Google Foundation. Available from the Ama

Artificial intelligence is generating unprecedented investment and uncertainty. This book argues that AI outcomes depend less on the technology itself and more on what it combines with: data quality, management capability, workforce skills, governance capacity and institutional design. A structured framework for policymakers and executives making decisions before the evidence is complete.

Australia’s National AI Plan makes strong commitments to infrastructure and adoption, but leaves governance and liability unresolved. This uncertainty exposes SMEs to risk, complicates investment decisions, and weakens public trust. History shows that technologies such as electricity became dependable only when standards, liability, and oversight aligned. AI now requires the same institutional attention if it is to move from experimentation to reliable national infrastructure

Australia’s next wave of innovation will depend on how clearly policy separates generic data centres from high‑performance computing (HPC). Bundling them into one “digital infrastructure” bucket directs capital to cloud‑style capacity while underinvesting in leadership‑class HPC needed for frontier AI, climate modelling, defence and advanced industry. The piece argues for explicit “AI‑ready HPC” and “AI‑capable cloud” tracks in national strategy, funding and governance.

The productivity lag from AI reveals competing logics: automation that replaces labour to cut costs versus augmentation that expands capability to create value. For innovation ecosystems, this choice is existential. Strict safety regulations risk creating moats favouring large incumbents, while unrestricted deployment threatens research quality. The narrow path requires public interest AI infrastructure, risk-weighted governance, and incentives rewarding high-impact discovery

Current debates about artificial intelligence often miss the fundamental difference between AI that replaces human capabilities and AI that amplifies them. Drawing on lessons from economic history and organisation theory, this insight argues for a focus on augmentation. For Australia, choosing to augment human expertise with AI is critical for building an economy that thrives on creativity and avoids the long-term risks of deskilling our workforce.