Your question is Prioritize Industrial AI Growth Path. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are the Engineering Manager at a B2B industrial AI software company serving manufacturers, energy operators, and heavy industry sites through reliability, computer vision, and connected worker workflows. The company has $48M ARR growing 22% year over year, but growth has slowed from 38% last year, gross revenue retention has fallen from 93% to 88%, and the sales team says deals are increasingly won or lost on time-to-value rather than feature breadth. Leadership is debating where engineering should focus over the next 12 months: accelerate new AI use cases in the core platform to win greenfield logos, invest in deployment and integration improvements to reduce pilot-to-production time from 9 months to 4 months, or build deeper vertical packages for two industries where win rates are already 2x the company average. Engineering capacity is constrained to roughly 60 full-time engineers, and choosing one path will materially delay the others.
How would you decide which engineering priorities to back, and what recommendation would you make to best align the roadmap with business goals over the next year?