Navigating the Expense vs. Capitalization Challenge
The biotech industry is experiencing a significant transformation as artificial intelligence (AI) plays an increasingly central role in drug discovery, clinical trial optimization, and genomic research. AI-powered platforms can now screen billions of molecules, predict drug interactions, and generate data-driven insights at an unprecedented scale. However, this transformation presents a significant challenge for financial reporting and audit professionals:
How should AI-driven R&D costs be accounted for?
The answer is not straightforward. As AI reshapes research and development (R&D), biotech CFOs, controllers, and their auditors must navigate complex accounting and compliance considerations under GAAP (ASC 730 & ASC 350), SEC scrutiny, and PCAOB audit expectations.
The Expanding Role of AI in Biotech R&D
AI has become an integral part of biotech research, enabling companies to:
- Identify new drug candidates faster and more cost-effectively.
- Optimize clinical trials by identifying ideal patient populations and reducing dropouts.
- Analyze genomic data to predict disease pathways and potential treatments.
While AI enables breakthrough discoveries, it also raises new financial reporting and audit challenges—especially in determining the appropriate classification of AI-related R&D costs.
Expense or Capitalize? AI’s Impact on R&D Accounting
Biotech firms must determine whether AI-related R&D expenses should be expensed immediately under ASC 730 or capitalized as an intangible asset under ASC 350. The distinction depends on whether the costs result in a tangible economic benefit.
Expensing AI-Driven R&D Costs (ASC 730)
Under ASC 730 (Research & Development Costs), most early-stage R&D costs must be expensed as incurred, including:
- Training and developing AI models used for drug discovery.
- Costs associated with unsuccessful AI-generated drug candidates.
- Data collection and AI-driven hypothesis testing that does not yield a patentable result.
Example: If a biotech firm invests $5 million in training an AI model to analyze genomic data, but the algorithm fails to produce a viable drug target, those costs must be expensed under ASC 730.
Capitalizing AI-Driven R&D Costs (ASC 350)
Some AI-related R&D costs may qualify for capitalization under ASC 350 if they result in an identifiable intangible asset, such as:
- An AI-generated discovery that leads to a patentable drug compound.
- Internally developed software used for regulatory submissions or clinical trials.
- AI models that generate data or predictive insights with measurable future economic benefits.
Example: If an AI-powered platform discovers a viable drug candidate that enters the FDA approval pipeline, the company may be able to capitalize related AI expenses. Similarly, if a biotech firm develops proprietary AI software to streamline clinical trials, costs related to its software’s development phase may be capitalized under ASC 985-20.
However, many AI-generated discoveries exist in a gray area between research and development, requiring significant judgment in accounting treatment.
Financial Reporting Challenges
AI’s role in biotech R&D creates new complexities for financial professionals, including:
- Subjectivity in AI-Generated Discoveries
- Unlike traditional lab-based research, AI models can produce millions of potential drug candidates.
- Key Question: When do AI-driven insights become development assets rather than research expenses?
- Auditor Consideration: How can auditors verify that AI-generated data provides a measurable economic benefit?
- Black Box AI Risk
- AI algorithms often operate as a “black box,” meaning even scientists may not fully understand how the model arrived at a particular result.
- Auditor Consideration: Are AI-generated discoveries sufficiently documented to justify capitalization?
- Regulatory Risk: If AI-driven costs are misclassified due to a lack of transparency, companies could face potential SEC scrutiny.
- Internal Controls Over AI in Financial Reporting
AI introduces new risks that biotech companies should address in their internal controls:
- AI-generated financial projections: Are they reasonable, or do they overstate potential future revenues?
- Data integrity risks: Is the AI model trained on verifiable, high-quality data?
- Audit trail challenges: Can AI-driven discoveries be traced back to supporting documentation?
SEC & PCAOB Considerations for AI-Driven R&D
Regulators are beginning to scrutinize how biotech firms account for AI-driven discoveries and financial projections. Key focus areas include:
- Companies must clearly disclose the impact of AI on financial reporting.
- Overstating AI-driven efficiencies in financial projections could lead to SEC enforcement actions.
- The SEC has questioned biotech firms about the reasonableness of AI-generated valuations.
Final Thoughts: The Future of AI in Biotech Financial Reporting
Given these challenges, biotech CFOs should implement strong accounting and governance frameworks for AI-driven R&D.
- Establish Clear AI Accounting Policies
- Define capitalization criteria for AI-generated discoveries.
- Maintain detailed documentation of AI models and their impact on financial reporting.
- Strengthen Internal Controls Over AI in R&D
- Implement AI model governance frameworks to ensure auditability.
- Use human oversight to validate AI-generated financial data.
- Work Proactively with Auditors
- Engage auditors early when AI-driven discoveries impact financial statements.
- Provide clear audit trails for AI-generated R&D insights.
AI is transforming biotech R&D, but its financial reporting implications remain a gray area. As AI-generated discoveries become more common, accounting and audit standards may need to evolve to provide clearer guidance. By developing robust accounting policies, internal controls, and transparent AI governance, CFOs can confidently navigate this complex landscape.
