Generative AI must go beyond the proof of concept (POC) stages. To do so, we have to address several critical factors.

POCs are small in scale. They just test the feasibility. They assess the impact.

Generative must integrate with the existing IT system. POCs must scale seamlessly into long-term consistent revenue streams.

Generative AI must handle large volumes of data and perform consistently. It calls for robust infrastructure. There should be compute resources, say GPU chips.

Models used should not be opaque. There should be explanation for their working.

There are issues of ethics and regulation. There are issues of bias and fairness. The organizations must comply with data protection laws. They should comply with AI governance standards.

Generative AI requires talent and training of talent. There should be collaboration with academia.

Generative AI applications vary across industries. The solutions must be customized. The specific needs of an industry must be understood, along with pain points. Generative AI can immensely help new drug development, personalized medicine, fraud detection and risk management in finance.

Generative AI is evolving very fast. There should be investment in R&D.

Thus generative AI can travel from PoC to a successful revenue stream by adopting a multi-pronged approach.