For many technology and Software-as-a-Service companies, artificial intelligence is fundamentally reshaping how value is created, scaled and delivered. As AI becomes more embedded in core operations and revenue models, finance leaders are weighing how this innovation interacts with accounting judgment and regulatory expectations. While AI adoption may not create immediate compliance risk, it does introduce important accounting considerations related to revenue recognition, cost structures and financial reporting.
The following areas highlight where CFOs, controllers and audit committees should direct their attention as AI continues to influence product offerings, pricing models and internal processes.
1. Revenue Recognition and Contract Economics
As technology and SaaS companies integrate chatbots, add-on features and other AI-powered tools into their platforms, AI is becoming a core part of the customer offering. As a result, companies should evaluate whether their current revenue recognition conclusions appropriately reflect how customers receive and consume value under Accounting Standards Codification Topic 606, Revenue from Contracts with Customers (ASC 606).
AI Functionality and Performance Obligations
AI-enabled features, such as copilots, predictive analytics and automated workflows, are often tightly integrated into a company’s core platform. This integration raises questions about whether these features constitute distinct performance obligations or are part of a single bundled offering under ASC 606.
Expectations around ongoing model updates, enhancements and added functionality can further complicate this analysis. If customers reasonably expect these improvements as part of the product, they may create implicit promises that affect the timing of revenue recognition, particularly when functionality is delivered over time rather than at a single point.
Usage-based Pricing and Variable Consideration
Many AI offerings rely on consumption-based pricing tied to compute usage, API calls or inference volume. This pricing model introduces revenue variability that may require companies to estimate and constrain variable consideration under ASC 606.
For example, technology and SaaS companies may need to consider:
• Whether usage-based fees are recognized only as usage occurs
• Whether certain fees qualify for the usage-based royalty exception
As AI revenue becomes more material, companies may wish to assess whether their accounting policies and disclosures clearly explain the drivers of revenue variability and the underlying economics of these arrangements.
Scale-driven Contract Modifications
Contracts are often amended as customers scale usage or add capacity. Under ASC 606, companies must evaluate whether these changes should be treated as separate contracts, prospective modifications, or cumulative catch-up adjustments.
2. Cost Accounting and Capitalization
AI-driven business models are also reshaping cost structures, introducing new areas of judgment under ASC 606, ASC 730, ASC 350 and ASC 340.
R&D vs. Operational Costs
Under ASC 730, research and development includes activities aimed at discovering new knowledge or significantly improving existing capabilities. AI development often blurs the line between R&D and operational activities.
For example, activities such as model training, experimentation, data labeling, and ongoing optimization may fall within R&D, cost of revenue or a bifurcated approach, depending on the nature and the purpose of the work. Companies should evaluate these classifications using the examples in ASC 730 and document their conclusions to support consistent application over time.
Software Development Capitalization
AI development rarely follows a linear path, complicating the application of the internal-use software guidance under ASC 350-40. Determining when a project moves from the preliminary stage into the application development stage and when technological feasibility has been achieved requires close coordination between finance and engineering teams.
As AI development cycles accelerate, companies may benefit from reassessing their capitalization policies, time-tracking practices and feasibility criteria to maintain alignment with projects.
Costs to Fulfill AI-related Contracts
For customer-specific implementations, data ingestion or model customization, ASC 340 and ASC 606 provide guidance on capitalizing costs to fulfill a contract. Especially for those with enterprise customers, significant upfront efforts to tailor AI solutions can directly impact margins and earnings trends.
3. Governance, Internal Controls, and Disclosure Implications of AI
As AI becomes more intertwined with operational and financial reporting processes, governance and transparency considerations become increasingly important.
Internal Controls
When AI is used in areas such as revenue analytics, contract review, billing, or accounting automation, companies may wish to evaluate whether internal controls adequately address the accuracy, completeness, and oversight of AI-generated outputs. Relevant considerations arise under SOX and COSO frameworks.
Auditors and regulators are increasingly focused on how management validates AI-generated outputs, documents assumptions, and governs changes to financial reporting models and algorithms.
Cybersecurity and Data Governance
AI heightens reliance on data integrity, infrastructure and third-party vendors. Companies should assess whether their cybersecurity controls are robust enough to protect sensitive data used in AI processes and whether related risks warrant enhanced disclosure under ASC 275 or recognition or disclosure under ASC 450, Contingencies. Organizations are also increasingly aligning IT controls, SOC reporting and financial reporting processes to address these risks.
Disclosure Transparency around AI Impacts
As AI becomes a more significant driver of revenue, cost, and margin variability, companies may wish to reassess disclosures under ASC 606, ASC 730, and Regulation S-K, including management’s discussion and analysis of risk factors. Thoughtful disclosures can help investors better understand how AI affects scalability, cost structure and long-term potential.
Guidance for Finance Leaders Navigating AI Adoption
For finance leaders, AI introduces a new dimension of accounting judgment and an opportunity to better align financial reporting with how value is created and sustained.
Grassi helps finance leaders at technology and software companies navigate the accounting and reporting considerations associated with AI-enabled business models. Through audit and advisory support, we help organizations enhance transparency and support informed decision-making that moves your business forward. To discuss how Grassi can support your organization, connect with a Grassi advisor today.
