GenAI self-regulated at enterprise level due to privacy concerns, says CitiusTech CEO

Generative Artificial Intelligence is self-regulated by companies at an enterprise level due to privacy concerns, explained Rajan Kohli, Chief Executive Officer of CitiusTech in an interview with Business Today.

The CEO said, “Many of the projects we work on involve the data models and data owned by our clients. These are not necessarily using OpenAI or data accessible to everyone on the internet. Large enterprises have a clear approach to data privacy, and they develop solutions in their own controlled environments with their own data. So, privacy concerns are relatively lower in these cases.”

Speaking of the IT sector, which is lately facing headwinds due to the broader macroeconomic slowdown, the CEO noted that CitiusTech seems to be cushioned by this because the majority of their clients are from the healthcare sector. This is in contrast to major IT players like TCS, Infosys, Wipro, etc. because majority of their clients are from the Banking, Financial Services, and Insurance (BFSI) segment.

He said, "In some way, we are quite fortunate to be in healthcare at this time because healthcare is somewhat more resistant to the cyclical winds of recession and cost-cutting. So, to that extent, we are doing better than the average industry that's going through tough times."

"Compared to what we are accustomed to, there has been a slight pullback, but it's still a positive situation. We feel lucky and grateful for this timing,” he added.

Kohli also added that GenAI has vast use cases in the healthcare sector as well. The use cases are not just limited to core medicine but also allied services like insurance, customer service, 

The CEO said, “The first phase, which is already happening, is related to customer-related inquiries and responses, such as customer service. The second phase will involve exception management-type processes, where straight-through processing handles most tasks, but some require human intervention due to exceptions. Call centers often deal with these exceptions. The third phase is more creative work that demands deep thinking and is likely to be the last to be transformed.”

“A few examples of where we are working with clients include health insurance companies. They receive numerous queries from members about eligibility, providers, recommended treatments, and reimbursement issues. Another use case is information exchange between providers and payers, which involves document exchange and communication around reimbursements and claims. These will be the primary focus of the initial phase,” he added.

He also added that clinical decision making via AI is also a viable use case but is not being practiced because of the regulatory and ethical restraints.

Clinical decision-making is a more complex area that may take longer to automate due to regulatory and ethical considerations,” Kohli explained.