Should DataProphet prioritize expanding our AI model's capabilities or improving its explainability for current users?

Introduction

The trade-off we're facing at DataProphet is whether to prioritize expanding our AI model's capabilities or improving its explainability for current users. This decision is crucial as it impacts our product's value proposition, user adoption, and competitive positioning in the AI market. I'll analyze this trade-off by examining our product context, potential impacts, key metrics, and experimental approach to arrive at a strategic recommendation.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking our AI model serves a specific industry or use case. Could you clarify the primary application of our AI model and its current user base?

Why it matters: Helps tailor the solution to specific user needs and industry requirements. Expected answer: Enterprise-level manufacturing optimization. Impact on approach: Would focus on industry-specific explainability needs vs. general AI capabilities.

• Business Context: Based on our positioning, I assume explainability is a key differentiator. How does our current explainability compare to competitors, and how critical is it to our sales process?

Why it matters: Determines the urgency of improving explainability vs. expanding capabilities. Expected answer: Lagging behind in explainability, causing friction in sales. Impact on approach: Would prioritize explainability improvements to address immediate market needs.

• User Impact: I'm thinking about user adoption rates. Can you share insights on whether lack of explainability or limited capabilities is the bigger barrier to user adoption and expansion?

Why it matters: Helps identify the most pressing user need to address. Expected answer: Explainability is a major concern for regulatory compliance. Impact on approach: Would focus on developing industry-specific explainability features.

• Technical: Considering the trade-off, I'm curious about the technical dependencies. How intertwined are our model's capabilities and its explainability? Would improving one significantly impact the other?

Why it matters: Determines if we can pursue both objectives simultaneously or need to choose. Expected answer: Some overlap, but largely independent development paths. Impact on approach: Might allow for a phased approach, tackling explainability first, then capabilities.

• Resource: Thinking about our team structure, do we have separate teams for model development and explainability, or would this decision require reallocating the same resources?

Why it matters: Influences the feasibility of pursuing both objectives in parallel. Expected answer: Shared resources with specialized skills in each area. Impact on approach: Might necessitate a clear prioritization to avoid diluting efforts.