Why Indian cos are wary of deploying ChatGPT-based bots

Firms, however, remain cautious in adopting the technology due to several reasons. Experts feel that the AI is still in early stages of development, and the fact that it can still generate incoherent and insensitive answers is a drawback, as is the fact that the platform itself needs to be localized for Indian languages and use cases.

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

“While the tool presents plenty of promise, it is true that ChatGPT itself is presently a work in progress. So, any user will need to initially vet the responses offered by the chatbot separately, which could be an initial hindrance in adoption of the technology,” said Abhiroop Medhekar, chief executive at Velocity.

It is this that analysts and industry stakeholders expect will pose a contradictory roadblock towards the adoption of generative AI platforms.

“What many companies still need to work out is how to operationalize generative AI tools in a business environment, with consistency and governance. A more complete end-to-end conversational AI solution is needed for businesses to communicate with their end-users, and have an influence on the bottomline,” said Pamela Kundu, senior director at US-headquartered enterprise automation platform, UiPath.

Sanjeev Menon, co-founder and head of technology at Pune-based enterprise automation startups, E42, said among the host of challenges that businesses are facing is the difficulty in customizing ChatGPT deployments for their own data sets.

“When an enterprise’s database is brought in, the data volume would not be large enough to contextually differ the way ChatGPT works and is trained as — its own data sets are simply too large for any company to easily replicate. This will not only be expensive, but also an extremely massive affair. In turn, this makes it difficult for a business to customize a contextual chat environment,” he said.

“The biggest threat from ChatGPT at the moment is that it can produce inaccurate and insensitive responses, which are contextually correct but semantically irrelevant,” he added.

Alongside operational challenges, companies will also need to evaluate the security aspect of deploying such language models. Bern Elliot, vice-president and analyst at Gartner, said one of the biggest challenges could be 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 that it collects and is fed without any safeguard. This would make it challenging for organizations such as media, or even pharmaceuticals, since deploying GPT models in their chatbots will leave them with no safeguard in terms of privacy. A future version of ChatGPT, backed by Microsoft through its Azure platform, which could be offered to businesses for integration, could be a safer bet in the near future,” Elliot added.

Industry stakeholders agree that most businesses experimenting with ChatGPT are not looking at returns on investments (RoIs) currently, since true real-world enterprise use cases are yet to be built.

“To deliver high degrees of automation and subsequently RoIs, conversational AI solutions need to support back-end systems like contact centre platforms, payment gateways and customer relationship management platforms. In terms of business adoption, we are still far away from a generative AI tool that can eventually complete a full transaction,” UiPath’s Kundu added.