Scenario: Pitching NVIDIA GPUs to Walmart’s CEO

You are a data scientist preparing an executive-ready proposal to justify adopting NVIDIA GPUs across Walmart’s priority analytics and AI workloads.

Deliverables:

1. Discovery: Identify high‑impact, GPU‑amenable workloads (e.g., demand forecasting, route optimization, computer vision, LLM assistants). For each, note data sources, current pain points, latency/throughput needs, and value drivers.
2. ROI and TCO: Build a quick, defensible TCO model comparing GPU vs CPU, including:
   • CapEx and OpEx (hardware, power/cooling, space, software/ops)
   • Realistic utilization assumptions and consolidation impact
   • Latency‑to‑revenue pathways (how faster/more accurate models create dollars)
   • A small numeric example to illustrate payback
3. Pilot Plan: Propose a phased 90‑day pilot with use‑case scope, architecture, success metrics, and acceptance thresholds for scale‑up.
4. Stakeholders & Risks: Identify executive sponsors and operational owners; enumerate risks (edge deployment, vendor lock‑in, data security/privacy, ESG) and mitigations.
5. Objections & Rebuttals: Prepare data‑backed rebuttals to three common objections (e.g., GPUs are too expensive; we lack skills; cloud is enough).
6. Clear Ask & Timeline: End with a crisp executive ask (budget, access, stakeholders, scope) and a 90‑day timeline with milestones.

Make explicit, minimal assumptions where needed, and keep numbers illustrative but defensible.