It’s the artificial intelligence (AI) chatbot that needs no introduction. As the fastest growing consumer application in history, ChatGPT gained more than 1 million users less than a week after launching last November, according to the bot’s creator OpenAI. Now more than 100 million users log a staggering 1 billion total website visits per month.
From suggesting meal plans to providing relationship advice to drafting resumes, ChatGPT has become a popular personal assistant for people looking to improve their lives. But with generative AI tools like ChatGPT also taking the business world by storm, how should organizations use and govern the technology?
“Everyone is going to see more and more use of ChatGPT and other generative tools in their organizations and with their business partners — and it’s going to provide a lot of benefits,” says Charles King, managing director, Internal Audit and Enterprise Risk at KPMG in Longwood, Fla. “But there are also significant risks to consider, and they need to be factored into any organization’s implementation strategy.”
Balancing caution and innovation may pose challenges for organizational leaders. ChatGPT’s business applications seem virtually limitless, and at least some use of generative AI may be necessary just to keep pace with competitors. But there are also legitimate privacy and other concerns, and mismanagement could have severe consequences. Business leaders need to carefully weigh both sides to effectively navigate this powerful but still nascent technology.
Unlocking Insights and Efficiency
Many organizations have forged ahead with ChatGPT to assist with software coding, customer service, content creation, and more. Some are continually experimenting with generative AI to build compelling use cases and determine how best to leverage the technology.
One application King cites is analyzing large data sets to provide insights and guide decision-making. For example, one of the largest U.S. financial institutions is using generative AI’s vast data-crunching capabilities to inform software development based on user requirements. “The users ask and answer questions, generating a large set of specifications and preferences,” he explains. “The AI then synthesizes that information, which serves as direction to the developers about what users really need so they can better tailor applications to those requirements.”
Similarly, at a U.S.-based health insurer’s call center, generative AI sifts through callers’ history, claims, and preferences to deliver a concise profile to customer agents as they are fielding support inquiries. In real time, the agent can quickly digest this information while on the call to provide better, more efficient service.
As organizations develop new applications for generative AI, King predicts further use of proprietary data sets to aid customer interactions, product and service delivery, and other business operations. “In essence, you would have business AIs that can support work, increase productivity, and access information a lot faster,” he says, “as well as reporting tools that deliver insights much more rapidly.”
More broadly, software integrations will increasingly put generative AI in the hands of all employees for day-to-day tasks. Productivity applications such as spreadsheets, slideshow software, and email clients will have AI bolted onto them. Tasks like drafting memos, creating presentations, and analyzing data will become easier and more efficient, without needing a separate interface to access the AI.