Your question is Define Success for Edge AI Launch. 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 leading a new initiative to add an on-device vision inference pipeline to an industrial sensing platform built around an NXP i.MX 8M Plus processor and managed through the eIQ AI software stack. The initiative matters because a flagship customer wants a pilot in one quarter, leadership expects it to become the template for future edge AI deployments, and the sales team is already positioning it as a differentiator. The work is tricky because the model team is optimizing for accuracy, the embedded team is worried about memory and thermal limits, and the platform team is in the middle of a BSP upgrade that could destabilize timelines. You also have one systems engineer who owns most of the camera integration knowledge and is committed 40% to another customer escalation, while product leadership is pushing for a broad demo scope that may not fit the schedule.
| Detail | Value |
|---|---|
| Deadline to customer pilot | 12 weeks |
| Engineering team | 4 embedded, 2 ML, 1 QA, 1 systems engineer |
| Hardware target | NXP i.MX 8M Plus EVK-derived platform |
| Software stack | eIQ, Linux BSP, GStreamer camera pipeline |
| Inference target | 18 FPS minimum on-device |
| Memory budget | 1.5 GB RAM max for full application |
| Thermal limit | No active cooling; sustained device temp under 75°C |
| Budget for external support | $60K |
How would you define success criteria for this initiative and use those criteria to drive roadmap decisions, trade-offs, and launch readiness over the 12-week window? How would you align stakeholders when technical feasibility, customer expectations, and schedule pressure are not fully aligned?