As Copilots and ChatGPT applications get normalized across the industry, organizations want to quantifiably measure the benefits of having these GPT products in their ecosystem – whether it’s in terms of supercharging employee productivity and efficiency in the workplace, decreasing the operational costs of the business (cost-savings!), and/or translating these benefits to their end consumers in the form of increased revenue impact.

Productivity measurement in the enterprise also then flows down to various employee personas, roles and functions, since each group might perceive and consume these GPT applications for their own nuanced use cases, thus gaining varying flavors of productivity benefits along the way.

This talk focusses on three things –
1. How should we define enterprise ROI for GPT & LLM specific apps - areas of impact
2. How to define ROI impact for various personas and roles
3. Data collection approaches to help make the ROI measures come to life