Session Details: People Analytics World 2025 New York City

AI promises productivity, but budgets need numbers. This session shows how Etter models job roles as granular workloads, maps them to business metrics, and runs three scenarios – baseline, AI augmented, and fully AI transformed. See demand-side gains such as faster forecasting and shorter cycle times, and supply-side effects including cost optimisation, redeployment and reskilling. The result is a finance-ready view of costs, benefits and risks to steer AI strategy and workforce decisions.

This session will explore

Workload decomposition: translate roles into tasks tied to measurable metrics such as cycle time, error rate, throughput and cost.
Scenario design: baseline, AI augmented and fully AI transformed states, with sensitivity and constraint handling.
Demand- and supply-side value: forecasting speed, decision latency, spend visibility; workforce cost, redeployment and reskilling.
ROI and guardrails: cost pools, benefit streams, payback windows, assumptions, and auditability.
Operating model: required data inputs, governance, and cross-functional adoption with HR, Finance and Technology.

Learning objectives

Model the financial impact of AI using workload-level assumptions that roll up to CFO-ready numbers.
Quantify demand- and supply-side effects credibly while avoiding double counting.
Structure scenarios and sensitivities that withstand Finance and Risk review.
Identify data prerequisites, governance guardrails and stakeholder roles to scale responsibly.