AI Revenue Disclosure Moves into the Spotlight
Artificial intelligence has moved from experimentation to execution across the IT services sector, becoming deeply embedded in consulting, software development, cloud services, and digital transformation projects. As AI adoption accelerates, a new question has emerged for corporate reporting and financial disclosure: should companies separately disclose AI-related revenue, and if so, how?
In 2025, this question gained prominence following high-profile disclosures—and reversals—by leading global IT firms. Investors, analysts, and regulators are increasingly seeking clarity on how AI is contributing to growth, margins, and future business strategy, even as companies struggle to define what constitutes “AI revenue” in practice.
TCS Sets a Benchmark with Explicit AI Revenue Disclosure
Tata Consultancy Services (TCS) recently drew attention by disclosing that its AI services business had reached a run-rate of approximately USD 1.5 billion. The disclosure marked one of the first clear, quantified statements by an Indian IT major on AI-related revenue.
The announcement reflects growing demand for AI-driven solutions, including generative AI, automation, analytics, and AI-enabled platforms, across global clients. By providing a specific figure, TCS offered investors greater visibility into the scale and commercial traction of its AI offerings, positioning AI as a distinct growth engine within its services portfolio.
Market observers note that such disclosures can enhance transparency, signal strategic focus, and help stakeholders better assess the return on AI investments. However, they also raise questions about consistency and comparability across companies, particularly in the absence of uniform accounting guidance.
Accenture’s U-Turn: From Standalone AI Metrics to Integrated Reporting
In contrast, global consulting giant Accenture has taken a different path. Initially, the firm prominently highlighted its AI investments, generative AI bookings, and related outcomes, drawing strong market interest. Over time, however, Accenture scaled back standalone AI revenue reporting.
The rationale was straightforward: AI had become deeply embedded across virtually all service lines—strategy, consulting, technology, and operations—making it increasingly difficult and arguably artificial to isolate “AI revenue” as a separate stream. Instead, Accenture shifted towards integrated reporting, treating AI as a core capability underpinning its entire value proposition rather than a standalone business segment.
This shift underscores a key challenge in AI accounting: when AI becomes ubiquitous, separating it from broader digital and technology services may no longer reflect economic reality.
Accounting Perspective: ICAI’s Cautious, Principles-Based Approach
Against this backdrop, accounting and standard-setting bodies are adopting a measured stance. The Institute of Chartered Accountants of India (ICAI), India’s apex accounting body, has indicated a “wait-and-watch” approach to AI revenue disclosure.
In the absence of specific guidance under Indian Accounting Standards (Ind AS) or international financial reporting frameworks, ICAI has suggested that companies focus on robust qualitative disclosures rather than aggressive quantitative segmentation. These may include:
- The nature of AI-enabled services offered
- How AI is integrated into business models and service delivery
- Key risks, governance frameworks, and ethical considerations related to AI
ICAI has also cautioned against “AI washing”—the practice of overstating or loosely classifying revenues as AI-driven purely for market perception, without clear underlying substance.
This approach aligns with broader regulatory thinking globally, where standard setters are yet to define precise recognition, measurement, and disclosure norms for AI-related income.
The Challenge of Defining ‘AI Revenue’
A central issue in the debate is definitional. Unlike traditional product or service lines, AI often operates as an enabling layer rather than a standalone offering. AI may enhance productivity, automate processes, improve decision-making, or personalise services—making it difficult to isolate incremental AI-specific revenue.
Additionally, AI projects are frequently bundled with cloud, data, cybersecurity, and consulting engagements. Disaggregating these elements may introduce subjectivity, reduce comparability, and potentially mislead users of financial statements.
The Road Ahead: Transparency Without Overstatement
As AI continues to reshape business models, the pressure for clearer disclosures will only grow. For now, the contrasting approaches of TCS and Accenture illustrate that there is no single best practice—only evolving judgment based on business context and maturity.
Until dedicated accounting standards or regulatory guidance emerge, a balanced approach appears prudent: combining thoughtful qualitative disclosures with cautious quantitative metrics, supported by strong governance and professional judgment. For the accounting profession, the challenge lies in ensuring that innovation in reporting enhances transparency and trust—without sacrificing consistency, comparability, or credibility.
Source : economictimesGPT
