Enterprise Deal Strategy and Execution
This area tests how you qualify, shape, and close complex enterprise deals. Interviewers look for structured thinking, multi-threading, risk management, and precise forecasting. Strong performance includes a clear methodology, quantified outcomes, and credible post-mortems on misses.
Be ready to go over:
- Qualification and prioritization – Your framework for sizing opportunity, technical feasibility, and executive sponsorship.
- Pilot design and success criteria – How you craft short, value-proving pilots with measurable metrics and a clear path to production.
- Negotiation and procurement – Navigating MSAs, pricing models (usage-based/commitments), security reviews, and legal cycles.
- Advanced concepts (less common) – Handling AI-specific DPAs, safety guardrails in contracts, multi-model procurement, consumption forecasting for LLM use.
Example questions or scenarios:
- “Walk me through a 6–12 month enterprise deal you owned end-to-end. What were the exit criteria from pilot to production?”
- “How did you forecast usage-based revenue and manage risk when consumption was uncertain?”
- “Describe a time legal or InfoSec blocked a deal. How did you unblock it?”
Technical Fluency in AI and the OpenAI Platform
You are expected to sell credibly to technical stakeholders. Interviewers probe your understanding of LLM capabilities/limits, data privacy, evaluation, latency/cost trade-offs, and how to position OpenAI versus alternatives. Strong answers use pragmatic language and tie technical choices to business impact.
Be ready to go over:
- LLM fundamentals – Prompting, context windows, fine-tuning vs. retrieval, evaluation strategies, and failure modes.
- Safety and privacy – Data handling, retention policies, abuse prevention, and governance alignment.
- Cost/latency/quality trade-offs – Model selection and how you set expectations with engineering and product teams.
- Advanced concepts (less common) – Multi-agent workflows, structured output, tool use/function calling, enterprise-grade routing and eval harnesses.
Example questions or scenarios:
- “Explain to a CFO how model choice impacts unit economics and gross margin for a new product feature.”
- “A customer asks about data retention and training. How do you respond and what documentation do you provide?”
- “A pilot is failing due to hallucinations. What’s your plan to stabilize results and keep executive confidence?”
Discovery, Value Engineering, and Storytelling
This area examines how you translate ambiguous interest into a prioritized use-case portfolio with quantified outcomes. Interviewers value precise discovery, layered questioning, and clear ROI narratives tailored to each persona. Strong candidates connect technical capability to business metrics and change management.
Be ready to go over:
- Persona-based discovery – Executive vs. builder priorities, risk tolerance, and decision criteria.
- Use-case triage – Impact vs. feasibility matrices; landing one high-visibility win before scaling.
- ROI framing – Baselines, counterfactuals, and how you attribute value to AI in multi-factor outcomes.
- Advanced concepts (less common) – Controlled A/B for AI features, eval-to-ROI mapping, and cost guardrailing for scale.
Example questions or scenarios:
- “Run a 10-minute discovery on my ‘AI strategy’ request and propose the top two pilot candidates.”
- “How do you quantify value when benefits are primarily qualitative (e.g., agent deflection, quality uplift)?”
- “Draft an executive summary email to a VP of Product to secure pilot approval.”
Operating Cadence, Forecasting, and Cross-Functional Leadership
Interviewers assess your ability to run disciplined cadences with internal teams and customers. You’ll be evaluated on forecasting rigor, stakeholder alignment, and escalation management. Strong performance shows proactive risk surfacing and a predictable operating rhythm.
Be ready to go over:
- Cadence design – Weekly internal deal reviews, executive check-ins, and pilot governance.
- Forecasting – Stage definitions, exit criteria, probability discipline, and consumption predictability.
- Cross-functional orchestration – When and how you pull in Solutions, Legal, Security, Product.
- Advanced concepts (less common) – Pilot steering committees, shared OKRs with customers, executive comms templates.
Example questions or scenarios:
- “Describe your forecasting methodology and a time you course-corrected mid-quarter.”
- “When do you escalate to an executive sponsor, and how do you preserve trust?”
- “How do you keep a pilot on track when two internal teams disagree on scope?”
Values, Integrity, and Customer Trust
OpenAI emphasizes responsible deployment and long-term relationships. Interviewers probe how you handle misaligned use cases, safety concerns, and high-pressure situations. Strong candidates show principled decision-making and the courage to say no.
Be ready to go over:
- Use-case vetting – Red flags, mitigation plans, and alternatives.
- Transparency – Setting realistic expectations on capability and timelines.
- Post-incident leadership – Root-cause, customer communications, and prevention measures.
- Advanced concepts (less common) – Ethics committees, regulated-industry adaptations, and external audits.
Example questions or scenarios:
- “Share a time you walked away from revenue to protect user safety or privacy.”
- “A customer wants an aggressive data retention policy. How do you respond?”
- “How do you balance rapid experimentation with responsible use?”
