• •Elezea proposes a decision matrix focusing on 'cost of failure' and 'computational predictability' to assess AI task suitability.
• •The framework suggests that tasks with low stakes and high repeatability currently yield the highest ROI for AI integration.
• •Uncertainty remains regarding how this framework scales as LLMs transition from static knowledge retrieval to autonomous agentic workflows.

Rui Carmo of Elezea introduced a decision-making framework designed to help professionals filter tasks for AI automation based on risk and complexity. This approach contrasts with the 'automate everything' sentiment prevalent in current tech discourse by prioritizing reliability and human oversight. However, the model does not yet account for the rapidly shifting performance benchmarks of newer models, which may render static filters obsolete. Evaluating task suitability based on risk rather than capability may determine whether AI implementation becomes a net efficiency gain or a source of technical debt.