Most AI spending is not failing, it is being measured wrong. - Only 29% of executives say they can confidently measure AI ROI, even though 79% are already seeing productivity gains - Direct financial impact (revenue and profit) doubled to 21.7% as the primary ROI metric tracked in 2026, overtaking productivity as the lead measure Track revenue acceleration, not headcount: Measure deal cycle time, lead conversion speed, and billing throughput, these surface in month 3, not month 18. Build a before-and-after baseline: Without documented pre-AI process benchmarks, you cannot prove value to a board, capture baseline metrics before deployment, not after. Tie ROI to specific workflows: Broad "AI spend" gives you no signal; attach each tool to one measurable output (e.g., support tickets resolved per agent per hour). EDGE grant requires ROI documentation: Budget 2026 EDGE applications require quantified productivity gains — use this as a forcing function to build measurement from day one. You cannot manage what you cannot measure, and most Singapore businesses have no AI measurement baseline at all.