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To unlock the value of AI in the workplace, a critical step is determining which work tasks should be automated, supported through AI–human collaboration, or remain human-led. Given the scale and complexity of this effort, frameworks can help leaders prioritize where AI creates value without introducing unacceptable risk. A new article offers a practical framework that evaluates tasks across two dimensions: 1) cost of errors—ranging from low impact (e.g., a missed nuance in a draft) to high impact (e.g., legal liability, reputational damage, or flawed medical guidance), and 2) type of knowledge required—whether the task relies on explicit, structured data or tacit knowledge such as empathy, ethical reasoning, intuition, or contextual judgment. These dimensions form a 2×2 matrix that groups tasks into four categories: Creative Catalyst (AI generates options, humans refine), Human-First (humans lead and AI assists due to higher risk and judgment), Quality Control (AI drafts and humans verify), and No Regrets (AI handles low-risk, data-heavy tasks). The article also highlights in the visual “Why Don’t Gen AI Gains Show Up in My P&L?” six ways AI-driven productivity gains are often missed and not articulated. Both frameworks provide useful tools for organizing work tasks and ensuring that projected gains and ROI from AI are adequately captured.
