Template for Presenting ML to Non-Tech Stakeholders
- Drawback:
‘We’re shedding $600K/month from fraudulent transactions.’ - Resolution:
‘We constructed an ML mannequin that flags high-risk transactions in real-time.’ - The way it Works (Analogies):
Consider it like a fraud-detection staff that by no means sleeps — with 20 years of expertise. - Affect:
- Reduces fraud losses by 65% ($390K/month financial savings).
- ‘False positives underneath 5%, minimizing buyer friction.’
5. Subsequent Steps:
‘We’d like 3 months to pilot with the funds staff. Funds: $50K.’
From ‘Black Field’ to ‘Checkout’ — How Amazon Makes ML Relatable
If Amazon can clarify deep studying to a retail supervisor, you may clarify logistic regression to your CFO.
The Problem
Amazon deploys ML all over the place: suggestion engines, fraud detection, warehouse robotics, and Alexa. However most decision-makers (e.g., retail managers, ops groups) aren’t information scientists.
How Amazon bridges the hole:
1. Analogies Rooted in On a regular basis Expertise
– Technical Jargon: ‘Collaborative filtering with matrix factorization.’
– Amazon’s Rationalization:
Like a retailer clerk who remembers each buy you’ve ever made and suggests what you’ll need subsequent.
– Technical Jargon: ‘Anomaly detection utilizing neural networks.’
– Amazon’s Rationalization:
A 24/7 safety guard that spots shady transactions quicker than a human can blink.
2. Concentrate on Tangible Outcomes
Amazon’s ML groups lead with enterprise impression, not mannequin metrics:
- Instance: As an alternative of boasting about “99% AUC,” they are saying:
Our ML mannequin decreased counterfeit product listings by 75%, defending $1B in annual income.
3. Visible Storytelling
Amazon’s inner dashboards for non-technical groups keep away from advanced charts. As an alternative:
– Earlier than ML: ‘X% of packages missed supply deadlines.’
– After ML (Route Optimization): ‘Now 98% arrive on time.’
– Present a video of a robotic arm selecting objects with out ML (sluggish, errors) vs. with ML (quick, exact).
4. The ‘Working Backwards’ Framework
Amazon’s well-known PR/FAQ method forces groups to clarify ML initiatives in plain language first:
- Press Launch: Draft a mock announcement (e.g., “New ML device cuts warehouse processing time by 30%”).
- FAQ: Reply hypothetical questions like:
- How does it work? → ‘Makes use of sensors to foretell field sizes, like a sensible packing assistant.’
- What’s the price? → ‘Saves $200K/month in labour.’
5. Dealing with Skepticism
When Amazon rolled out ML-driven demand forecasting:
- Skeptic: Why belief a mannequin over our veteran planners?
- Response:
The mannequin learns from 20 years of gross sales information + climate/site visitors occasions. It’s like giving your planners a crystal ball.
