Frame the decision as a product strategy problem: start by clarifying the company's goals and constraints, then evaluate GenAI/LLM opportunities across both products and workflows using a structured framework. Balance bold experimentation with risk management, and propose a phased, measurable approach that aligns with the company's core value proposition.
Pro tip: Show you understand the automotive industry's unique constraints—safety, regulation, and long product cycles—and propose a portfolio approach: quick wins in internal workflows (e.g., design, supply chain) while piloting customer-facing features with strict guardrails.
Define what success looks like for the company (e.g., cost reduction, customer experience, innovation) and identify hard constraints like safety, regulatory, and brand reputation.
Brainstorm specific use cases in both areas: in-vehicle assistants, predictive maintenance, personalized marketing, and internal tools like code generation or document summarization.
For each opportunity, assess technical feasibility, potential business impact, and risks (e.g., hallucination, data privacy, liability). Prioritize based on value vs. effort and risk.
Propose a pilot-first approach: start with low-risk internal workflows, then move to customer-facing features with human-in-the-loop and safety checks. Define success metrics and go/no-go criteria.
Secure buy-in from engineering, legal, and leadership. Establish a cross-functional team to monitor, learn, and scale successful pilots while killing failures quickly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.