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About this episode
AI INNOVATOR
Rohit Chikballapur, Board Member at Raining Code & Founder of NettWorth
Rohit Chikballapur serves on the board of Raining Code, an enterprise AI advisory firm working with medium to large companies across Europe and the US. He is also the founder of NettWorth, an AI-native personal finance tool. Previously, he spent years on global SAP deployments and later founded a vertical software company in industrial maintenance.
In this edition of AI Innovators, we talked with Rohit Chikballapur about why production-ready AI is defined by verification rather than accuracy, and why that changes what enterprises should build, buy, and trust. Rohit advises industrial companies on where AI belongs and, just as often, where it doesn't. His core argument: AI is allowed to be wrong, as long as users can tell when it's wrong and correct it quickly. He's equally direct about what a company should own and what it should rent.
Key Takeaways:
- Verifiable, not perfect: Production-ready AI doesn't mean accurate every time. It means mistakes are easy to catch and easy to correct.
- Security is table stakes: Without it, there's no conversation about governance or value. It's the price of entry, not a differentiator.
- Horizontal AI builds IP, vertical AI builds services: Horizontal use cases compound across every customer. Vertical ones only codify one customer's knowledge.
- Own vs. rent, not build vs. buy: The question isn't whether you can build it, but whether the category is moving faster than you can keep up.
- Distribution beats building: Anyone can build fast. The ones who break through solve distribution first.
Read the full conversation with Rohit Chikballapur to learn where AI creates real value in the enterprise, and where it doesn't. Subscribe to AI Innovators for monthly insights straight to your inbox.
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