You are an engineering manager at a B2B marketing platform that serves 8,000 mid-market and enterprise brands and generates $220M in annual revenue with 18% EBITDA margins. Leadership wants to launch an AI-driven message experimentation and send-time optimization capability inside the company’s messaging platform within 9 months because competitors have begun bundling similar features and sales reports that 14% of late-stage deals now ask for it. You can either build the core optimization and experimentation stack in-house using your existing data science and platform teams, which would take about 8 months and $3.5M in first-year cost, or buy a third-party decisioning engine that could be integrated in 3 months for $1.8M in annual licensing plus $600K in integration and vendor-management cost. The in-house option may create stronger differentiation and lower long-term marginal cost, while the third-party option reduces time-to-market but creates dependency, weaker control over roadmap, and potential data-sharing concerns for large customers.
How would you evaluate whether to build or buy this capability, and what would you recommend? Explain the strategic trade-offs, the economics, and how your recommendation should shape the product and go-to-market plan.