How organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026 suggests that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.
Abstract
We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026. These linked data enable a privacy-preserving analysis of adoption, worker roles, and message-level tasks at scale: for instance, the worker-level sample we analyze at the six-month adoption horizon includes over 1,500 organizations and over 17 million messages. We document four facts about enterprise AI adoption and use. First, ChatGPT Enterprise usage has grown rapidly due to a combination of new firm adoption and growing intensity among existing adopters. Second, U.S.-based public company adoption is concentrated among larger, more valuable, and more R&D- and SG&A-intensive firms. Third, active use within adopting firms spans job functions and seniority levels, with especially high usage intensity among early-career workers. Fourth, ChatGPT Enterprise usage encompasses a broad range of knowledge work tasks, including writing, technical work, communication, and information synthesis. In aggregate, these results suggest that firms differ widely in the speed, breadth and purpose of their enterprise AI adoption, and that they are still actively learning how to integrate AI into organizational workflows.
We study how sophistication in generative AI (genAI) use varies among the back-office workforce of a large firm. Using proprietary data, we observe 713,564 employee prompts and their corresponding large language model responses from nearly 4,000 back-office employees across 15 functional areas over eight months in 2025. We document three main findings. First, senior employees exhibit more sophisticated genAI use, consistent with domain expertise complementing genAI capabilities. Second, sophistication varies considerably across functions and is highest in Strategy, Digital Innovation, and Project Management, three groups that share a focus on firmwide strategic initiatives and organizational change. Third, we observe neither improvements in sophistication over time nor lasting improvements following formal AI training, suggesting that sophisticated use can be difficult to change. Together, our study provides measures of and insights into sophisticated genAI use that managers can use to improve outcomes and that researchers can use in future research.
Nicholas Hallman, Zachary T. Kowaleski, Anu Puvvada et al.· 0 citations
The emergence of Generative Artificial Intelligence (AI) has transformed digital marketing by enabling businesses to create content more efficiently and cost-effectively. Among AI-powered applications, ChatGPT has become one of the most widely adopted tools for generating marketing content, product descriptions, social media posts, blogs, customer responses, and promotional materials. Small online businesses, which often operate with limited financial and human resources, increasingly rely on ChatGPT to improve marketing productivity and maintain a consistent digital presence. However, the practical adoption of ChatGPT raises concerns regarding content quality, originality, ethical use, and dependence on AI-generated information. This study investigates the perception and use of ChatGPT in content creation among small online businesses through a qualitative multiple case study approach. Three small online businesses representing different sectors—an online fashion retailer, a handmade products business, and a digital marketing agency—were selected for comparative analysis. Secondary data were collected from published case studies, company websites, academic literature, and industry reports. The findings indicate that ChatGPT significantly improves content creation efficiency, reduces marketing costs, supports creative idea generation, and enhances customer engagement. Nevertheless, businesses consistently reported that human review and editing remain essential for maintaining brand identity, ensuring factual accuracy, and creating authentic customer experiences. The study concludes that ChatGPT is most effective when used as a collaborative content creation assistant rather than a replacement for human creativity. The findings contribute to the growing body of knowledge on AI adoption in small businesses and provide practical insights into the effective integration of generative AI into content marketing strategies.
R. Parmar, Prashant Sarothiya· International Journal of Cre...· 0 citations
This study analyzes how a government-operated chatbot shapes citizen interactions and public value through emotional, thematic and behavioral dynamics and demonstrates how chatbot-mediated processes influence citizen experience in digital government.
Tehila Tigist Neguse, H. Gabay, Iris Reychav et al.· Transforming Government: Peo...· 0 citations
Although private use of artificial intelligence (AI) is rapidly increasing, employees in large organizations often remain stuck in experimental or sporadic AI use at work. This exploratory study investigates how employees progress from initial AI intentions to frequent and routinized AI use by drawing on the Rubicon Model of Action Phases. Based on semistructured interviews with twelve mid- to senior-level practitioners from large organizations and a Gioia-style analysis with substantial inter-coder reliability (κ = .72), this study reconstructs trajectories of employees’ use of enterprise AI applications (e.g., internal assistants or copilots) across four Rubicon phases: pre-decisional exploration, pre-actional commitment, actional implementation, and post-actional evaluation. The findings map four phase-typical patterns of experiences, behaviors, and decision mindsets and identify three Rubicon-threshold mechanisms that enable employees to translate initial intentions into routinized use of enterprise AI applications: (1) breakthrough value on real work tasks, (2) organizational legitimacy through secure, governed, and workflow-integrated enterprise AI systems, and (3) stabilization via routines supported by calibrated trust and, for some, an “AI-savvy” professional identity. This study contributes a phasebased process account of workplace AI adoption, a mechanism-based explanation of why strong intentions often remain sporadic, and phase-specific intervention guidance for deploying, governing, and workflow-embedding enterprise AI systems to enable sustained, routinized use.
Özgür Kartal, J. Spilski, T. Lachmann· International Conference on...· 0 citations
Key Features
- Leverage the impact of AI on modern accounting, from audits to corporate governance
- Use ChatGPT to streamline your accounting tasks with practical hands-on techniques
- Understand the impact of AI in accounting through in-depth chapters covering various domains, including ethical considerations and data analytics
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
In the fast-paced AI world, accounting professionals are increasingly challenged by the complexities of AI. Many struggle to integrate these advanced tools into their workflows, leading to a sense of overwhelm. ChatGPT for Accounting bridges this gap by not only simplifying AI concepts but also offering practical insights for its application in various accounting domains.
This book takes you from the foundational principles of Generative Artificial Intelligence (GAI) to its practical applications in audits, tax planning, practice management, fraud examination, financial analysis, and beyond. Each chapter equips you with essential skills, showing you how AI can revolutionize internal control systems, enhance recruitment processes, streamline marketing plans, optimize tax strategies, and boost efficiency in audits. You’ll then advance to exploring the role of AI in forensic accounting, financial analysis, managerial accounting, and corporate governance, while also addressing ethical and security implications.
Concluding with a reflective outlook on the promises and challenges of AI, you’ll gain a holistic view of the future of accounting. By the end of this book, you’ll be equipped with the knowledge to harness the power of AI effectively and ethically, transforming your accounting practice and staying ahead in the ever-evolving landscape.
What you will learn
- Understand the fundamentals of AI and its impact on the accounting sector
- Grasp how AI streamlines and enhances the auditing process for high accuracy
- Uncover the potential of AI in simplifying tax processes and ensuring compliance
- Get to grips with using AI to identify discrepancies and prevent financial fraud
- Master the art of AI-powered data analytics for informed decision-making
- Gain insights into seamlessly integrating AI tools within existing accounting systems
- Stay ahead in the evolving landscape of AI-led accounting tools and practices
Who this book is for
Whether you’re a seasoned accounting professional, a C-suite executive, a business owner, an accounting educator, a student of accounting, or a technology enthusiast, this book provides the knowledge and insights you need to navigate the changing landscape in applying GAI technology to make a difference in all you do. An appreciation and understanding of the accounting process and concepts will be beneficial.
As artificial intelligence (AI) tools become embedded in everyday work, employees increasingly engage in adaptive, experiential collaboration with AI systems. While such collaboration often generates valuable employee-level tacit knowledge, organizations struggle to translate this learning into strategic capabilities. This study examines how organizations can harness the knowledge emerging from employee–AI collaboration to build knowledge-based dynamic capabilities (KBDCs).
We conducted an inductive, qualitative study based on 29 semi-structured interviews with frontline employees, middle managers and senior leaders across multiple industries.
We develop a process model showing how tacit knowledge generated through micro-level human–AI collaboration is externalized as articulated experiential insights, validated and simplified into shared heuristics, and ultimately codified and embedded in organizational routines. This bottom-up transformation may help firms to renew internal knowledge resources and enhance KBDCs. The process is contingent on enabling conditions such as managerial support, organizational culture and employees' perceived agency in interacting with AI.
Organizations seeking to build dynamic capabilities from AI use should create structures that facilitate articulation, validation and dissemination of employee-level AI insights. Leaders play a critical role in translating individual experimentation into collective learning. Firms can assess their position in the transformation process and invest in systems that support phase-to-phase transitions.
While prior research has emphasized acquiring codified or external knowledge for dynamic capabilities, this study shifts attention to the internal, tacit and evolving knowledge that arises from employee–AI collaboration. We advance theory by unpacking a bottom-up pathway of KBDC formation through which such knowledge is articulated, validated and embedded over time. In doing so, the study also identifies a recursive organizational learning mechanism suited for high-velocity, AI-enabled environments.
Erica Wen Chen, Hongkun Tang, Ben Nanfeng Luo et al.· Management Decision· 0 citations
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