𝗣𝗹𝗮𝗻𝘁𝗶𝗻𝗴 𝗬𝗼𝘂𝗿 𝗔𝗜 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 𝘄𝗶𝘁𝗵 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗶𝗻 𝗠𝗶𝗻𝗱 𝗙𝗿𝗼𝗺 𝗗𝗮𝘆 𝗢𝗻𝗲

🔗 https://shre.ink/Tobias-Zwing-AI-Prototype-Production

Tobias Zwingmann explains AI prototypes aren't like regular software. They often carry over their flaws – accuracy, user fit, and costs – directly into production. That’s why it’s essential to build with growth in mind.

1. Why AI Prototypes Are Different (and Why That Matters)

In traditional software development, prototypes are meant to be discarded. They’re just quick experiments to test an idea. But AI plays by different rules. The performance, user fit, and cost characteristics of your AI prototype tend to carry over directly into production.This means if your prototype is inaccurate, poorly adopted, or expensive to run, your final product will be too. That’s why AI projects often fail – not because the idea is bad, but because the foundation wasn’t built for growth. Instead of thinking in isolated project phases, teams should treat the AI journey as continuous growth, starting from day one.

2. What Stays the Same vs. What You Can Improve

• What stays the same:

  • Core accuracy: Errors in testing = errors in production.

  • User fit: Poor alignment with user workflows won’t magically improve later.

  • Cost structure: Expensive to test? Expect a high production bill.

• What can improve:

  • Scalability: Systems can be expanded to handle more users.

  • Integration: Connections with existing tools can be refined.

  • Safety features: Mistake detectors and feedback loops can be added.

The prototype is a seed – and how you plant it determines whether it’ll grow into a healthy product.

3. How to Build with Production in Mind from Day One

3.1 Principles to follow:

• Choose the right tools – Not the flashiest, but what fits your current ecosystem.

• Involve users early – Even rough feedback beats no feedback.

• Test in the real world – Real data is messy; your AI must cope with it.

• Think growth – Don’t transplant a tree; grow a seed in fertile soil.

3.2 Thinking Backwards Approach:

• Use familiar tools.

• Spot and fix problems early.

• Test with difficult scenarios.

• Build for flexibility.

• Start simple, keep doors open.

3.3 Not Thinking Backwards:

• Pick hyped tools without integration checks.

• Trust vendor demos over real use.

• Ignore early feedback.

• Launch big, crash hard.

4. Practical Strategies to Bridge the AI Prototype-to-Production Gap

• Start messy, clean up later: Use flexible, low-friction tools (like Google Colab) that don’t lock you in.

• Embed in workflows: AI should fit into the user's daily job, not disrupt it.

• Add feedback loops: Talk to your early users! It’s more valuable than any dashboard.

• Roll out slowly: Launch to a small group, iterate, and scale gradually.

• Lean on MLOps: Use established practices to manage AI in production from day one.

5. Tailoring the Approach to Your Business

Every organization is different, and success depends on:

• Your industry: Regulated sectors need more caution.

• Your team maturity: Agile, cross-functional teams can move faster.

• Your business goals: Choose between speed (e.g. in marketing) or reliability (e.g. in manufacturing).