• Revenue & Market Strategy (The "Business" Owner) • Monetization Architecture: Move beyond flat SaaS seats. Design and implement usage-based pricing, credit systems, or "value-based" billing tied to AI-automated outcomes. • Competitive Intelligence: Maintain a "Battlecard" mentality. Know exactly what the incumbents and startups are shipping, and ensure our AI capabilities (triage, QA, and automation) are 10x better, not 10% faster. • GTM Execution: Don't just hand off a deck to Sales. Drive the narrative, define the ICP (Ideal Customer Profile), and ensure our positioning clearly articulates the ROI of "AI over Headcount." • AI Native Product Leadership • The "AI Trinity" Tradeoff: Ruthlessly manage the balance between Accuracy, Latency, and Unit Cost. You know when to use a frontier model (GPT-4/Claude 3.5) vs. a fine-tuned small language model (SLM) for specific MSP tasks. • Agentic Workflows: Shift from simple "chatbots" to autonomous agents that can execute ticket resolution, not just summarize it. • Data as a Moat: Define the data collection strategy to create a proprietary feedback loop (RLHF) that makes our models smarter with every MSP ticket handled. • Rapid Execution & Experimentation • 0 → 1 Execution: Lead "Flash Builds"—getting prototypes in front of customers in days, not months. • Performance Benchmarking: Define what "Good" looks like. Build the evaluation frameworks (Evals) to ensure our AI isn't just "cool" but consistently reliable in a production MSP environment.