Should Stats Perform prioritize expanding its AI-powered video analysis tools or enhancing its existing manual data collection processes?

Introduction

The trade-off question at hand is whether Stats Perform should prioritize expanding its AI-powered video analysis tools or enhancing its existing manual data collection processes. This scenario involves balancing technological innovation with the refinement of established methodologies in sports analytics. I'll address this trade-off by examining the business context, user impact, technical feasibility, resource allocation, and strategic implications.

Step 1

Clarifying Questions (3 minutes)

• Context: I'm thinking Stats Perform is facing increasing competition in the sports analytics market. Could you provide more context on our current market position and the competitive landscape?

Why it matters: Helps determine if innovation or reliability is more critical for maintaining market share. Expected answer: Strong position, but facing pressure from new AI-driven competitors. Impact on approach: Would influence whether to focus on differentiation through AI or doubling down on our core strengths.

• Business Context: Based on our business model, I assume we have a mix of long-term contracts and project-based work. What's the current revenue split between these, and how might it affect our prioritization?

Why it matters: Helps understand the financial implications of potentially disrupting existing processes. Expected answer: 70% long-term contracts, 30% project-based. Impact on approach: Higher percentage of long-term contracts might favor gradual enhancement over radical changes.

• User Impact: I'm curious about our user segments. Are we primarily serving professional sports teams, media companies, or a mix? How might their needs differ regarding AI vs. manual analysis?

Why it matters: Different user segments may have varying preferences and requirements. Expected answer: Mix of pro teams (60%) and media companies (40%). Impact on approach: Would tailor the solution to address the needs of our primary user segment.

• Technical: Regarding our AI capabilities, where are we in terms of accuracy compared to our manual processes? Is there a significant gap we need to close?

Why it matters: Determines the viability of relying more heavily on AI-powered analysis. Expected answer: AI is 85% as accurate as manual processes for most metrics. Impact on approach: High accuracy would support expanding AI tools, while lower accuracy might prioritize manual enhancements.

• Resource: Can you give me an idea of our current team composition? What's the split between data analysts and AI/ML engineers?

Why it matters: Helps assess our capacity to execute on either option. Expected answer: 70% data analysts, 30% AI/ML engineers. Impact on approach: A higher proportion of AI engineers might favor expanding AI tools, while more analysts could support enhancing manual processes.