AI and Digital Transformation: Impact on Business Practices, Firm Performance, and Corporate Reporting

1. AI and Digital Technology Adoption Trends

1.1 Adoption by Sector

AI adoption is highly concentrated in knowledge-intensive services. The ICT sector leads with 57.3% of firms using AI in 2025, followed by professional, scientific, and technical services at 36.8%. In 2024, the most recent year with full sectoral coverage, ICT adoption stood at 44.6%, compared with 7.2% in construction and 7.8% in accommodation and food services (OECD, 2026). The pattern reflects both the origin of AI technologies in the ICT sector and the relative ease of integrating AI into digital-first workflows. Among slower-adopting sectors, 2025 growth was strongest in accommodation and food services (62.5% year-on-year) and construction (59.1%), suggesting catch-up is underway (OECD, 2026).

1.2 Adoption by Firm Size

Firm size remains the strongest predictor of AI adoption. In 2024, 40% of firms with 250 or more employees actively used AI, compared with 11.5% of SMEs. The adoption gap has widened: in 2020, large firms were 4.3 times more likely to use AI than SMEs; by 2024, this ratio stood at 3.5 times, but the absolute gap grew from 13.8 to 28.5 percentage points. Among SMEs that use generative AI, only 29% deploy it in core business activities; the majority confine AI to peripheral tasks. Younger firms, including startups, show notably higher AI adoption regardless of scale, suggesting that established routines and habits, rather than resource constraints alone, drive the gap (OECD, 2025). Preliminary 2025 data from the OECD show large-firm adoption reaching 52.0% and small-firm adoption at 17.4%, indicating the gap continues to expand (OECD, 2026).

1.3 Adoption Over Time and the GenAI Acceleration

OECD data reveal a sharp jump in 2024, at the same time that general-purpose generative AI tools such as ChatGPT and Copilot became widely available. Between 2023 and 2025, aggregate AI adoption more than doubled from 8.7% to 20.2%. McKinsey's broader global survey (which counts any use in at least one business function) places the 2025 figure at 88%. The divergence between OECD official statistics (firm-level, 10+ employees, strict definition) and McKinsey survey data (any functional use) highlights how hard it is to define and measure AI adoption consistently (OECD, 2026; McKinsey, 2025).

2. Effects on Firm Productivity, Cost Structures, and Financial Performance

The academic and industry evidence on AI's productivity effects is broadly positive but mixed. OECD estimates suggest AI could add 0.2 to 1.3 percentage points in annual labour productivity growth across G7 economies over the next decade. At the firm level, AI users show productivity gains of 4% to 15%, depending on the study and methodology (OECD, 2025). Productivity gains may follow a J-shaped pattern: dipping temporarily before improving, as firms go through costly restructuring to weave AI into their workflows.

McKinsey identified 46 "GenAI high performers" among 876 surveyed firms (5.3%). These leaders attribute over 10% of EBIT to AI deployment and achieve returns exceeding $10.30 per dollar invested, nearly three times the average. Yet the contrast with PwC's finding that 56% of CEOs report zero measurable AI ROI in the past 12 months shows how large the implementation gap remains. Only 6% of organisations have achieved significant enterprise-wide AI impact (McKinsey, 2025; PwC, 2026).

3. Digital Transformation in Auditing, Financial Reporting, and Management Control

3.1 The Big Four and AI-Driven Audit Transformation

The Big Four accounting firms have invested heavily in AI platforms that now analyse entire populations of journal entries rather than traditional samples. EY's Helix platform analyses 100% of client journal entries. PwC developed GL.ai with H2O.ai to detect irregularities invisible to traditional sampling. KPMG's Ignite platform scans millions of accounting entries using machine learning. Deloitte's Zora AI, built with Nvidia, automates finance and procurement workflows, with projected cost reductions of up to 25% in these functions (PwC, 2023; KPMG, 2024; Deloitte, 2025).

KPMG committed USD 2 billion over five years (2020-2025) targeting USD 12 billion in added revenue. PwC invested USD 1 billion in generative AI through its partnership with Microsoft and OpenAI. Adoption of AI-assisted tax preparation surged, with some firms reporting over 80% of individual return preparation handled through automated workflows in 2025 (KPMG, 2024; PwC, 2023).

3.2 Management Control Systems and Digital Transformation

AI is reshaping management accounting from a reactive, backward-looking function toward proactive, strategic partnership. Machine learning applications in cost control, budgeting, and performance measurement enable predictive and prescriptive analytics that simulate complex business scenarios (Ranta, Ylinen, and Jarvenpaa, 2023). However, Wassie and Lakatos (2024), reviewing 62 articles published between 2019 and 2023, found that Asia and Europe dominate AI accounting research, with the Middle East and Africa showing minimal engagement. The literature reveals critical gaps in governance frameworks, empirical validation, and skill development pathways.

4. Country-Level Digital Readiness and Regulatory Developments

4.1 Digital Competitiveness Rankings

Singapore, Switzerland, and Denmark lead the IMD World Digital Competitiveness Ranking (2024), measured across knowledge, technology, and future readiness pillars (IMD, 2024). Within the EU, Finland (76.0), the Netherlands (72.5), and Denmark (71.0) score highest on the DESI 2024 composite index (European Commission, 2024). The World Bank's Digital Adoption Index, though now dated (2016 for most countries), provides a useful baseline, with a 0-1 scale showing advanced economies clustered above 0.75 and emerging economies substantially lower, with India at 0.42 (World Bank, 2016).

4.2 Regulatory Landscape

The EU AI Act, adopted in March 2024 and entering into force on 1 August 2024, is the world's first comprehensive AI regulation. It uses a risk-based framework with four categories (unacceptable, high, limited, minimal), phased compliance deadlines through 2030, and penalties up to 7% of global annual turnover for non-compliance. Prohibited practices provisions applied from February 2025; high-risk system obligations begin phasing in from August 2026.

In the United States, the SEC's Investor Advisory Committee recommended AI-specific disclosure guidelines in December 2025, and S&P 500 companies have significantly expanded AI risk factor disclosures. Reputational risk from AI is the most frequently cited concern (38% of S&P 500 firms in 2025), followed by cybersecurity risk (20%) and regulatory uncertainty (41 firms explicitly flag the EU AI Act). AI-related securities class actions rose from 7 cases in 2023 to 14 in 2024 (Conference Board/ESGAUGE, 2025; Fisher & Phillips, 2025).

5. Corporate AI/Digital Investment Disclosures and Capital Market Implications

AI-related disclosures in corporate filings have grown rapidly. The Conference Board and ESGAUGE report that 72% of S&P 500 companies disclosed at least one material AI risk in their 2025 10-K filings, up from just 12% in 2023 (Conference Board/ESGAUGE, 2025). Eisfeldt et al. (2023) find that firms with higher exposure to generative AI experienced larger increases in market value following the release of ChatGPT. Earnings call analysis by the European Central Bank confirms that early AI engagement boosted stock market performance beyond the immediate impact on expected earnings (Ca' Zorzi et al., 2025).

Babina et al. (2024) show that AI-investing firms experience higher growth in sales, employment, and market valuations, driven primarily by product innovation. These findings show that AI disclosures carry economically meaningful information to capital markets, but also raise questions about whether firms with limited AI substance are using disclosure language to ride the AI narrative without matching investment.

6. Research Gaps and Opportunities for Accounting Scholars

The intersection of AI, digital transformation, and accounting presents substantial opportunities for important research. The following table identifies specific gaps and relevant journal outlets for accounting scholars.

7. Downloadable Data

The CSV data files used to produce the charts in this brief are available for download below, along with a Python replication script that reproduces all charts and statistics from the source data. All files include commented headers describing variables, sources, and methodology. For full documentation, download ai_README_methodology.txt below or visit the Data & Code page.