Extract from Kazu Shigenobu’s article “Navigating the New Frontier of Conversational AI Evidence”
From internal chats to collaborative documents, communication has always left behind a trail of discoverable data. But now, with the rise of generative AI, there’s a new kind of conversation emerging: between humans and machines.
Chatbots like OpenAI’s ChatGPT and Google’s Gemini are no longer novelty tools. They’re embedded in daily workflows across industries. Product teams are drafting specs and writing code with the help of AI copilots. HR teams are building interview questions and composing offer letters. Marketing teams are generating campaign briefs, customer emails, and press releases. And employees across the average org are using chatbots to summarize meetings, answer policy questions, and even brainstorm new business ideas.
Every one of those interactions leaves behind data—data that’s often proprietary, sensitive, and potentially discoverable.
Why Conversational AI Data Matters
Over 90 percent of Fortune 500 companies already use OpenAI, and adoption is only accelerating. As AI tools become standard across departments, the kinds of data they produce become more varied and high-stakes. Employees are:
