The_AI_Builders_Playbook_2025

Core Insights - The 2025 State of AI report emphasizes the importance of building and operationalizing AI products as a competitive advantage, focusing on the practical aspects of developing AI-powered offerings [11][12] - The report outlines a "builder's playbook" that includes best practices for product development, go-to-market strategies, talent acquisition, cost management, and internal productivity [12][14] Product Development - Companies are increasingly adopting AI capabilities, with 31% of respondents indicating they are traditional SaaS, 37% AI-enabled, and 32% AI-native [22] - AI-native companies are more advanced in product development, with 47% of their products reaching critical scale compared to AI-enabled companies [28][30] - Agentic workflows are the most common AI products being developed, with 80% of AI-native companies focusing on this area [33] Go-to-Market Strategy - High-growth companies are dedicating 30-45% of their product roadmap to AI-driven features, while AI-enabled companies allocate 20-35% [66] - A hybrid pricing model is prevalent, combining subscription and usage-based pricing, with many companies bundling AI features into premium tiers [69][70] - 37% of companies are exploring new pricing models based on consumption and ROI [75] Talent and Organization - Many companies have dedicated AI leadership by the time they reach $100M in revenue, indicating the increasing complexity of AI operations [86] - AI/ML engineers and data scientists are the most common roles, with a significant focus on hiring dedicated talent [90] - On average, companies plan to have 20-30% of their engineering team focused on AI, with high-growth companies having a higher proportion [95] Cost Management - Companies are allocating 10-20% of their R&D budget to AI development, with plans to increase this in 2025 [100] - API usage fees are cited as the most challenging cost to control, highlighting the unpredictability of external API consumption [106] - High-growth companies spend significantly more on inference and data storage as they scale, with monthly inference costs reaching up to $2.3M at the scaling stage [114][118] Internal Productivity - Internal AI productivity budgets are expected to nearly double in 2025, with companies spending 1-8% of total revenue on generative AI [122] - Approximately 70% of employees have access to AI tools, but only about 50% use them regularly, indicating a gap in adoption [129] - Companies exploring multiple GenAI use cases typically have high employee adoption, with those using AI across 7+ use cases seeing the most impact [139]