This blog is an attempt to highlight the ever-evolving landscape of generative AI in the business-to-business (B2B) sector and the crucial role of humans will be playing to make it relevant.

I still remember when I worked @ Rediff , I used to see great editors will give the apt title to the story ( only after verifying it from 3 independent sources ) and highlight what “matters” to make it interesting to read. The otherwise standard ANI or PTI stories were – boring.

Drawing correlation , I can see AI driven response mirrors a time-tested practice in the news industry , where an expert editor crafts a story’s impact through a well-chosen title and slug. In this same manner , when deploying generative AI applications like ChatGPT in the B2B space , the focus shifts to editing and templatizing responses, ensuring relevance and accuracy – only to be done by “responsible” humans.

This approach is essential because, while AI can generate vast amounts of content , it lacks the understanding of a subject matter expert (SME). An SME is not just any human in the loop but they are the responsible humans in the loop. This distinction is crucial because an SME brings depth of knowledge and context-specific insight that a general overseer cannot. In the context of generative AI , such as the GPT models , this expertise becomes invaluable in addressing one of the model’s notable weaknesses : hallucinations , or the generation of plausible but incorrect or nonsensical information which can lead to wrong path.

Having an SME as part of the process is more than a quality check. It is about understanding the nuances of the industry , the specific needs of the business , and the balance between finesse of the language and accuracy of the information that AI alone might miss. This level of involvement ensures that the responses generated by AI are not just accurate but also relevant and tailored to the specific context of the B2B environment.

Furthermore , incorporating SMEs into the AI loop acts as a form of Reinforcement Learning from Human Feedback (RLHF). This methodology allows for the continuous improvement of AI models based on these “responsible” human input. By identifying and correcting errors or shortcomings in AI-generated content , SMEs highlight areas for future work and development in these models. Their insights help in fine-tuning the AI’s output , making it more reliable and effective for B2B applications and truly putting Human + Computer blended Interaction in effect.

To conclude , while generative AI holds immense potential for the B2B sector , its successful deployment hinges on the integration of SMEs into the process. They bring a level of scrutiny , understanding and context that goes beyond what AI can achieve alone. By acting as a bridge between AI capabilities and real-world knowledge , SMEs ensure that the technology is not just a tool for generating content, but a reliable partner in the B2B narrative.

However building too much reliance on well trained AI model resulting into SMEs stop thinking out of the box will hamper the creativity while creations will continue – and time will tell if the danger is real , if the machine will start being creative and if humans will evolve !!

Author – Sumit Rajwade, Co-founder: mPrompto