Enterprise AI Problem Solving

Introduction

Bigger, multi-week enterprise scenarios that mirror real consulting / staff-engineer work. Practise these to prepare for senior interviews.

Understanding the topic

Core concepts to understand:

• Roll out 'company-wide AI assistant' to 50,000 employees.
• Migrate from a single LLM to a multi-provider gateway.
• Add SOC 2 + audit logs to an existing AI feature.
• Build a per-tenant eval program for an enterprise SaaS.
• Reduce monthly LLM bill by 50% without quality regression.
• Bring an AI feature on-prem for a regulated customer.

Syntax reference

Visual workflow / architecture:

Discovery → Architecture → Pilot → Evals → Rollout → Monitor → Iterate

Real-world use

Same projects asked at Accenture AI, McKinsey QuantumBlack, Deloitte AI, and staff-engineer interviews at AI startups.

Best practices

• Always start with stakeholder interviews.
• Pilot small before rolling out wide.
• Quantify ROI rigorously.

Hands-on exercise

Interview preparation — practice these questions:

• Q1. Walk through a company-wide AI assistant rollout.
• Q2. How do you bring AI on-prem?
• Q3. How do you cut cost 50%?