Measuring the ROI of GenAI: Assessing Value and Cost

These will likely come from productivity assistants, such as Microsoft 365 Copilot and Google Workspace. 

Such activities are easy to get started, pilot and buy but are usually task-specific, so measure and value time saved for both those specific tasks and across aggregate tasks related to specific processes — within specific time periods.

Productivity improvements alone may be a diminishing source of differentiation over time, but integrating these capabilities into other business processes can help enterprises maintain a competitive edge.

Time to value is short (less than one year).

Lean on use cases that leverage generative AI within industry or custom applications that allow you to leverage enterprise data in unique ways to extend current processes.

These differentiating initiatives provide a more defensible competitive advantage than quick wins but come with higher and more unpredictable costs and risk.

Direct and indirect financial benefits, including the potential for revenue generation, can generally offset costs, assuming effective underlying process redesign, upskilling and risk management.

Time to value is medium (between one and two years).

Transformative use cases come with higher cost, complexity, risk and potential for technical debt. 

Ongoing innovations in GenAI are refining models and techniques and bringing down adoption costs; however, until lower-cost options emerge, innovators may have to accept difficult-to-quantify hard financial returns and higher cost, complexity and risk in exchange for first-mover advantage.

Investment decision criteria should prioritize strategic benefits that may be difficult to quantify in financial terms over immediately identifiable task- or process-specific financial benefits.

Time to value is long (more than two years).