Why Indian companies are wary of deploying ChatGPT-based bots

However, companies have been cautious in adopting the technology for a number of reasons. Experts believe that the AI ​​is still in its early stages of development, and the fact that it can still generate inconsistent and insensitive answers is a drawback, as is the fact that the platform has to be optimized for Indian languages ​​and usage. Matters need to be localized.

On 6 February, Bengaluru-based fintech platform Velocity announced the rollout of a chatbot on WhatsApp with ChatGPT integrated into its backend. Through the chatbot, users of Velocity will have access to their business data housed in simplified and conversational formats, as well as recommendations on sourcing supplies or analyzing well-known markets.

“While the tool presents a lot of promise, it is true that ChatGPT itself is currently a work in progress. Therefore, any user will initially need to examine the responses given by the chatbot separately, which There can be an initial barrier to adoption,” said Abhirup Medhekar, chief executive of Velocity.

This is what analysts and industry stakeholders expect will create an insurmountable barrier towards adoption of generative AI platforms.

“Many companies still need to work out how to operate generative AI tools with stability and governance in a business environment. For businesses,” said Pamela Kundu, senior director at UiPath, a US-headquartered enterprise automation platform. There is a need for a more complete end-to-end conversational AI solution to communicate with your end-users and impact the bottomline.”

Sanjeev Menon, co-founder and head of technology at Pune-based enterprise automation startup, E42, said one of the challenges businesses face is the difficulty in customizing ChatGPT deployments for their own data sets.

“When an enterprise’s database is brought in, the amount of data will not be large enough to be contextually isolated and trained the way ChatGPT works – its own data sets can be easily accessed by any company are too large to be repeated. This would not only be costly, but would also be a huge affair. In turn, this makes it difficult for businesses to customize a relevant chat environment,” he said.

“The biggest danger with ChatGPT at the moment is that it can generate erroneous and insensitive responses that are contextually correct but semantically irrelevant,” he said.

Along with the operational challenges, companies will also need to evaluate the security aspect of deploying such language models. Bern Elliott, vice president and analyst at Gartner, said one of the biggest challenges may be the exposure of intellectual property and sensitive material.

“It is important to understand that ChatGPT is built without any real corporate privacy governance, which leaves all the data collected and fed without any protection. This applies to organizations such as the media, or even pharmaceuticals. This will be challenging for users, as deploying the GPT model in their chatbots will not give them any protection in terms of privacy. A future version of ChatGPT, supported by Microsoft through its Azure platform, may be offered to businesses for integration. could be a safe bet in the near future,” Elliott said.

Industry stakeholders agree that most businesses experimenting with ChatGPT are not currently seeing a return on investment (ROI), as true real-world enterprise use cases have yet to be created.

“To deliver a high degree of automation and subsequent ROI, conversational AI solutions need to be supported by back-end systems such as contact center platforms, payment gateways and customer relationship management platforms. In terms of business adoption, we are still a long way away from a generative AI tool that can eventually complete a full transaction,” said Kundu of UiPath.