Making AI Count: The Next Measurement Frontier

As organizations are adopting AI @ Scale, measuring productivity gains attributable to AI is also a problem statement that requires deeper research. A recent NBER paper by Diane Coyle and John Lourenze Poquiz reveals why business leaders struggle to quantify AI’s real value. Link to the paper - https://lnkd.in/dhMm4_PV Here is what practitioners need to know: 📉 The Efficiency Paradox: By automating tasks like customer service or scheduling, AI reduces recorded transactions. This means internal efficiency gains can look like a decline in official output, even as organisational effectiveness and customer value soar. ⏳ Time Reallocation is the Key: AI’s true benefit lies in saving time on routine cognitive tasks (like data cleaning or drafting), freeing up your team for higher-value, creative work. 💡 Quality over Quantity: Standard metrics track volume, but AI drives dynamic, unpriced quality improvements and process re-engineering. By automating routine cognitive and logistical tasks, these organizations are capturing massive time savings and quality improvements that legacy reporting structures simply cannot see. For business leaders, the takeaway is clear: if you are only measuring success by traditional headcount or volume metrics, you are blind to the quiet productivity boom happening inside your workflows. To measure your AI ROI, design internal reporting around task-based time savings and outcome-focused quality gains.