Big auditors can deploy AI but face structural hurdles: data integration and regulatory uncertainty slow implementation even as large firms' resources allow them to advance, leaving smaller firms strained by cost and capacity and risking concentration of advanced audit services.

Main Finding

Adoption of AI in accounting and auditing is shaped by interacting technological, organizational, and environmental factors. Organizational support (top management, culture, institutional participation) and firm resources determine successful implementation, while regulatory uncertainty, market pressures, data integration challenges, and cost constraints—especially for smaller firms—hinder adoption.

Key Points

• Framework: Study uses the Technology–Organization–Environment (TOE) framework to analyze organizational-level AI adoption.
• Sample / Setting: Semi-structured interviews with professionals from Big Four auditing firms; emphasis on empirical, organization-level insights rather than purely theoretical analysis.
• Organizational factors (enablers): top management support, conducive corporate culture, and active institutional engagement are critical for implementation success.
• Technological factors (challenges): data integration and existing technological infrastructure are significant technical barriers for large firms deploying AI systems.
• Environmental factors (constraints): regulatory uncertainty and market pressures can impede adoption; legislation and regulatory requirements are decisive determinants.
• Size heterogeneity: large firms face technical/integration issues but have more resources; smaller firms are more constrained by cost and limited resources.
• Conclusion: Successful AI adoption requires bolstering technical infrastructure, driving organizational change, and adapting to environmental/regulatory conditions.

Data & Methods

• Data: Qualitative data collected via semi-structured interviews with professionals at Big Four auditing firms.
• Analytical lens: Technology–Organization–Environment (TOE) framework used to structure understanding of adoption drivers and barriers at the organizational level.
• Scope: Organizational, practice-oriented focus (what happens in firms), not a theoretical or macro-level quantitative study.

Implications for AI Economics

• Adoption heterogeneity and market structure:
  • Resource-rich large firms (e.g., Big Four) are better positioned to adopt AI, potentially accelerating concentration of advanced audit services and creating competitive advantages.
  • Smaller firms facing cost barriers may lag, increasing industry fragmentation between AI-enabled incumbents and less-automated smaller players.
• Productivity and labor effects:
  • AI adoption may raise productivity in auditing and accounting but shifts the demand toward higher-skill tasks (analytics, oversight, interpretation), with possible displacement of lower-skill routine work.
  • Complementarities between AI and human capital imply returns to upskilling and organizational change.
• Regulatory and policy implications:
  • Regulatory uncertainty is a major adoption barrier; clearer standards, audit/AI guidance, and compliance frameworks can lower adoption costs and risks.
  • Policymakers could consider targeted incentives, standards for data interoperability, and support for infrastructure or training to reduce barriers for smaller firms.
• Investment and market dynamics:
  • Demand for AI tools, data engineering, and integration services will rise; markets for AI-enabled audit tools and related service providers are likely to expand.
  • Data integration and infrastructure constraints create opportunities for specialized vendors and for standard-setting around data formats and APIs.
• Research implications:
  • Further quantitative work could measure adoption rates across firm sizes, estimate productivity gains, and model how regulation shapes equilibrium adoption and market concentration.