Claims that AI will automate 30% or 50% of jobs make for compelling headlines, but they are generally a poor representation of the underlying research. This series cuts through the noise to explain what AI is actually likely to do to the labour market and what this means for regions and firms.
The four articles combine academic frameworks with practical applications, showing how to move from national-level generalities to specific, actionable analysis. Each article can be read on its own, but together they build a comprehensive picture of how to think about AI's impact on work.
Part 1: Beyond the Headlines—How to Think About AI and Jobs
The framework article explains the economic forces at work: displacement (AI destroys jobs) versus reinstatement (savings get spent elsewhere, creating new jobs). It introduces the J-curve of adoption, firm-level creative destruction, and why regional variation matters. This article also explains how task-based exposure research fits into the broader picture, as a practical tool for identifying which occupations face change most imminently.
Part 2: What AI Means for Your Region—A Place-Based Approach
National averages mask important local variation. This article shows what a rigorous place-based analysis looks like in practice: mapping occupational exposure to local employment data, modeling spillover effects when displaced workers compete for safe jobs, and evaluating retraining pathways. The methodology is illustrated with real analysis undertaken for a sub-regional partnership, demonstrating how scenario-based thinking helps policymakers plan under uncertainty.
Part 3: Rethinking the Org Chart—How AI Affects the Way Work is Structured
For firms, the biggest opportunity is not simply automating individual tasks—it's reorganising work to capture AI's full value. This article explains how tasks are bundled into jobs, why that bundling changes when AI enters the picture, and how to think systematically about restructuring. It introduces four channels through which AI reshapes organisations: automation, augmentation, coordination compression, and skill-requirement reduction. Understanding which tasks sit at coordination hubs versus the periphery is essential to sequencing adoption well.
Part 4: Tracking the Evidence—Staying Current in a Fast-Moving Field
The research on AI and labor markets is evolving rapidly. This article explains the major empirical approaches to measuring AI exposure, how they differ, and what those differences mean for policy and planning. It also sets out how we keep track of new evidence as it emerges, ensuring our analysis remains grounded in the latest research rather than relying on studies that may already be outdated.
For regional and local authorities: We apply this framework to your specific labour market, providing scenario-based analysis, spillover modeling, and skills policy recommendations. Learn more →
For firms and organizations: We help you map your task structure, identify AI opportunities across your operations, and restructure intelligently around them. Learn more →