Hybrid AI Rollout: Adding a Model Beside Rule Engines and Human Reviewers
Company: Stitch
Role: Product Manager
Category: Machine Learning
Difficulty: medium
Interview Round: Other
Can a company bring AI into decisions it already handles with hand-written rules and human reviewers, without replacing either of them right away? Explain how the three would work together, and how the model would take on more of the work over time.
```hint Pick one decision flow
Ground the answer in a single workflow, such as screening risky transactions or routing support tickets, and ask what rules, a model and a person are each best at within it.
```
### Clarifying Questions
- Which process should the example center on: transaction risk or fraud screening, identity and document checks, or customer support routing?
- How costly and how reversible is a wrong automated decision in that process, for example a delayed payment versus a wrongly frozen account?
- Do regulators or auditors require each decision to be explainable, or a named person to stay accountable for it?
- Is the main motivation lower cost, higher accuracy, faster decisions, or a volume the current team cannot keep up with?
### What a Strong Answer Covers
- A clear position on augmenting versus replacing, with the reasons the existing rules and reviewers are worth keeping at first.
- Concrete patterns for combining them, showing what the rules, the model and the human reviewer each own in one workflow.
- The evidence and stages a model must pass before cases move away from people, and the criteria for each step.
- Metrics that compare the hybrid with today's process, covering error costs, reviewer workload and speed.
- Risks particular to hybrid setups, such as reviewers deferring to the model, biased training labels and unclear accountability.
### Follow-up Questions
- Reviewers accept the model's suggestion almost every time. Is that enough evidence to let the model decide on its own, and what else would you check?
- When the model and a hard rule disagree about a case, which one wins, and who owns that policy?
- Once most cases no longer reach a person, how do you keep collecting trustworthy labels to retrain and audit the model?
Overview: A concept question for product managers about adding AI to decisions already handled by rule engines and human reviewers, instead of replacing them at once. It tests how a candidate splits work between rules, models and people, decides when a model may take on more decisions, and measures the risks of the transition.