Generative artificial intelligence is no longer a novelty for most consumers. It is becoming a daily workflow, and the longer people use it, the more they trust it with harder decisions, including their finances.
That is the central finding of the ninth Consumer AI Benchmark Report from PYMNTS Intelligence, which tracks how experience changes the way consumers use AI tools. According to the report, 61% of workplace generative AI users have used the technology for at least a year, while just 12% started within the past six months. Personal use skews slightly newer, but 51% of those users also have at least a year of experience under their belts.
Consumers on the other end of collection calls, emails, and text messages are increasingly comfortable letting AI handle sensitive financial matters. The share of users who consider generative AI essential for managing finances and banking is 129% higher among those with a year or more of experience than among newcomers. Among the one-year-plus group, 31% say AI is essential for financial management, compared with 13% of new users.
Experience also expands the workload. Personal users with at least a year of experience complete an average of 11 tasks with AI, 68% more than the 6.6 tasks handled by newcomers. Use of AI for finances specifically rises from 23% among newcomers to 39% among experienced users.
The tools consumers reach for are changing, too. Longer-tenured users gravitate toward dedicated AI platforms rather than the AI features embedded in search engines or phone assistants. Power users average 2.9 platforms, more than double the 1.4 used by light users. ChatGPT leads with 74% usage among experienced personal users, followed by Gemini at 52%, Microsoft Copilot at 28%, and Claude at 16%.
Search engines are taking the biggest hit. Thirty-eight percent of users with at least a year of AI experience say they use search engines less because of generative AI, up from 26% among newcomers.
For financial services providers, the report identifies a specific opening: as reliance on AI for money matters grows, so does the value of trustworthy data, permission controls, explainable outputs, and smooth escalation paths to a human. That framework should sound familiar to collection operations weighing their own AI deployments.
