Stop Ordering AI Takeout A Cookbook for Winning When You Build In House - Jan Siml

Introduction to AI Strategy 00:01

• Many teams approach AI like ordering takeout, leading to high expectations and disappointing results.
• The speaker emphasizes the importance of aligning AI tools with actual in-house needs rather than overcomplicating with advanced models.

Building vs. Buying AI Solutions 01:19

• The speaker shares a successful case of building AI in-house, which resulted in millions of dollars in annual recurring revenue (ARR).
• Highlights that complex solutions may look appealing but are often overkill for internal requirements.

Understanding User Needs 03:29

• Focus on a single, painful job to be done that has a clear dollar-based outcome.
• Engaging with users to understand their needs is crucial for success.

Revenue Metrics Over Evaluation Metrics 04:47

• Emphasizes the importance of tracking actual revenue generated rather than focusing solely on evaluation metrics like F1 score.
• Encourages tracking metrics that link AI tasks directly to financial outcomes.

Proactive Development and User Engagement 06:17

• Advocates for anticipating user needs rather than waiting for requests.
• Introduces the idea of creating proactive systems that guide user actions to maximize efficiency.

Data Quality vs. Model Complexity 08:01

• Good data consistently outperforms complex models, emphasizing the importance of focusing on user needs.
• Encourages building systems that respond to genuine user feedback rather than chasing model performance benchmarks.

Key Takeaways 09:52

• Start small with a focus on high-impact, dollar-based outcomes.
• Track everything related to revenue generation and ensure proactive engagement with users.
• Invest in the basics and let user feedback guide ongoing improvements.