How should ADVANCE.AI prioritize improving the accuracy of its facial recognition technology versus expanding its language support in its eKYC solution?

Introduction

The trade-off between improving facial recognition accuracy and expanding language support in ADVANCE.AI's eKYC solution presents a critical decision point for our product strategy. This scenario involves balancing technological advancement with market expansion, each carrying significant implications for user experience, regulatory compliance, and business growth. I'll analyze this trade-off through multiple lenses, considering short and long-term impacts, and provide a data-driven recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm assuming eKYC is a core product for ADVANCE.AI. Could you confirm if this is correct, and how it fits into our overall product portfolio?

Why it matters: Helps prioritize the decision against other product initiatives Expected answer: eKYC is a flagship product, critical for revenue Impact: Would justify significant resource allocation

• Business Context: Based on our current market position, I'm thinking accuracy might be our key differentiator. How does our facial recognition accuracy compare to competitors, and what's our current market share?

Why it matters: Informs whether we need to focus on catching up or maintaining a lead Expected answer: We're slightly behind in accuracy but leading in market share Impact: Might prioritize accuracy improvements to solidify market position

• User Impact: Considering user demographics, I'm guessing language support is crucial for expansion. What percentage of our target market are we currently unable to serve due to language limitations?

Why it matters: Quantifies the potential impact of expanding language support Expected answer: About 30% of the target market requires additional languages Impact: Could shift focus towards language expansion if the number is significant

• Technical: Given the complexity of facial recognition, I'm wondering about our current accuracy levels. What's our current false positive/negative rate, and what's the industry benchmark?

Why it matters: Helps determine how much improvement is needed and feasible Expected answer: Our false positive rate is 0.1%, slightly above the 0.05% industry standard Impact: Might prioritize accuracy if we're significantly behind benchmarks

• Resource: Considering the specialized nature of both tasks, I'm curious about our team composition. Do we have separate teams for AI development and localization, or would these improvements compete for the same resources?

Why it matters: Affects the feasibility of pursuing both improvements simultaneously Expected answer: Separate teams, but limited overall resources Impact: Could influence whether we can pursue a balanced approach or need to choose