What improvements could Icertis make to its AI-powered risk assessment tools to provide more actionable insights?

Introduction

To improve Icertis' AI-powered risk assessment tools for more actionable insights, we need to analyze the current product, identify pain points, and develop strategic solutions. I'll approach this by examining user segments, analyzing pain points, generating solutions, and proposing metrics for success.

Step 1

Clarifying Questions

• Looking at Icertis' position in the contract lifecycle management (CLM) space, I'm thinking about the primary use cases for their AI risk assessment tools. Could you provide more context on the main types of risks these tools currently assess and for which industries?

Why it matters: Determines the scope and depth of improvements needed Expected answer: Primarily financial and legal risks for industries like finance, healthcare, and tech Impact on approach: Would focus on enhancing specific risk models and industry-specific features

• Considering the AI-powered nature of the tool, I'm curious about the data sources it currently uses. Can you share insights on the types and volume of data the system analyzes, and any limitations in data access or quality?

Why it matters: Identifies potential areas for expanding data sources or improving data processing Expected answer: Uses internal contract data and some external sources, with challenges in data standardization Impact on approach: Would explore ways to enhance data integration and standardization

• Given the rapidly evolving AI landscape, I'm wondering about the current AI capabilities of the tool. Could you elaborate on the specific AI technologies employed (e.g., NLP, machine learning models) and their current accuracy levels?

Why it matters: Helps identify opportunities for leveraging cutting-edge AI advancements Expected answer: Uses NLP and basic ML models with 80-85% accuracy in risk identification Impact on approach: Would focus on incorporating advanced AI techniques to improve accuracy and insights

• Thinking about the end-users, I'm interested in understanding their level of technical expertise. Can you describe the typical user profile and their comfort level with AI-driven insights?

Why it matters: Influences the design of user interfaces and the presentation of insights Expected answer: Users are typically legal and business professionals with varying technical expertise Impact on approach: Would emphasize intuitive UI/UX design and clear explanation of AI-generated insights