What metrics would you use to evaluate Icertis's AI-powered contract risk scoring feature?

Introduction

Evaluating Icertis's AI-powered contract risk scoring feature requires a comprehensive approach to product success metrics. This feature, which leverages artificial intelligence to assess and quantify risks in contracts, is a critical component of Icertis's contract lifecycle management (CLM) platform. To effectively measure its success, we'll need to consider metrics that capture both the technical performance of the AI and its real-world impact on contract management processes.

Step 1

Product Context

Icertis's AI-powered contract risk scoring feature is an advanced tool within their CLM platform that automatically analyzes contract language, clauses, and terms to identify and quantify potential risks. Key stakeholders include:

1. Legal teams: Seeking to reduce manual review time and improve risk identification accuracy.
2. Procurement professionals: Aiming to streamline contract negotiations and mitigate supplier risks.
3. Finance departments: Interested in quantifying financial exposure from contractual obligations.
4. Compliance officers: Ensuring adherence to regulatory requirements and internal policies.

The user flow typically involves:

1. Contract upload or creation within the Icertis platform.
2. AI analysis of the contract content, comparing it against predefined risk parameters and historical data.
3. Generation of a risk score and detailed breakdown of identified risks.
4. User review of the AI-generated insights and potential action based on the risk assessment.

This feature aligns with Icertis's broader strategy of leveraging AI to transform contract management, differentiating them in the competitive CLM market. Compared to competitors like DocuSign and Coupa, Icertis's AI risk scoring aims to provide more granular and actionable risk insights.

In terms of product lifecycle, the AI risk scoring feature is likely in the growth stage, with ongoing refinements to improve accuracy and expand risk detection capabilities.