Rather than waiting for official enterprise gen-AI projects to overcome technical and organizational hurdles, employees are routinely leveraging personal ChatGPT accounts, Claude subscriptions, and other consumer-grade AI tools to automate tasks. This activity is often invisible to IT departments and C-suites.
Employees are already crossing the GenAI Divide through personal AI tools. This ‘shadow AI’ often delivers better ROI than formal initiatives and reveals what actually works for bridging the divide.
The study was based on a review of over 300 publicly disclosed AI initiatives, interviews with representatives from 52 organizations, and survey responses from 153 senior leaders.
It reveals that while only 40% of companies have purchased official LLM subscriptions, employees in over 90% of companies regularly use personal AI tools for work. In fact, nearly every respondent reported using LLMs in some form as part of their regular workflow.
Many shadow users describe interacting with LLMs multiple times a day, every workday—with adoption often far outpacing their companies’ sanctioned AI initiatives, which remain stuck in pilot stages.
Project NANDA’s analysis highlights key reasons for this divide:
As the report notes, “The organizations that recognize this pattern and build on it represent the future of enterprise AI adoption.”
These advantages contrast sharply with official gen-AI deployments, where complex integrations, inflexible interfaces, and lack of persistent memory often stall progress. This helps explain a “chasm” in between pilots and production.
According to the report, shadow AI usage creates a feedback loop: As employees become more familiar with personal AI tools that suit their needs, they become less tolerant of static enterprise tools.
“The dividing line isn’t intelligence,” the authors write, explaining that the problems with enterprise AI have to do with memory, adaptability, and learning capability.
As a result, 90% of users said they prefer humans to do “mission-critical work,” while AI has “won the war for simple work,” with 70% preferring AI for drafting emails and 65% for basic analysis.
Meanwhile, the study engages in some myth-busting, puncturing five commonly held beliefs about enterprise AI. Contrary to the hype, it finds:
For this story, Fortune used generative AI to help with an initial draft. An editor verified the accuracy of the information before publishing.
