AI Value Assessment — Lesson 7 of 10
A company spends $15 million developing an AI-powered underwriting engine that reduces credit losses by 30% and processes applications 10 times faster than the manual alternative. The engine has a useful life of at least five years. Its replacement cost is estimated at $20 million. When the company is acquired two years later, the acquirer's purchase price allocation values the AI engine at $25 million as an identifiable intangible asset.
Yet on the company's own balance sheet, the AI engine may appear as nothing — or, at best, as a partially capitalised development cost. The $15 million investment was expensed through the income statement over two years, reducing reported profits and suppressing the balance sheet. The company's book value understates its true economic value by the full amount of the AI asset.
This is the accounting paradox at the heart of AI investment. The standards that govern financial reporting — primarily IAS 38 for individual entities and IFRS 3 for acquisitions — create systematically different treatments for the same asset depending on whether it was built internally or acquired. Understanding these rules is essential for CFOs making capitalisation decisions, investors evaluating AI-intensive businesses, and executives positioning their companies for M&A.
AI investments exist in an accounting grey zone. Most are expensed as incurred, which depresses reported profits and understates balance sheet value. Some qualify for capitalisation under IAS 38's development criteria. And in M&A, the same assets that were invisible on the seller's balance sheet are separately identified and valued on the acquirer's. CFOs who understand these rules can make strategic decisions about capitalisation, disclosure, and investor communication that materially affect how the market perceives their AI investment.
The IAS 38 Framework for AI
IAS 38 distinguishes between the research phase and the development phase of an internally generated intangible asset. Only development costs can be capitalised, and only when six conditions are simultaneously met.
The Six Capitalisation Criteria
| Criterion | IAS 38 Requirement | AI Application |
|---|---|---|
| Technical feasibility | Demonstrate the asset can be completed for use or sale | Model achieves target accuracy on validation data |
| Intention to complete | Entity intends to complete, use, or sell the asset | Board-approved project with committed resources |
| Ability to use or sell | Entity can use the asset or sell it | Clear deployment plan or licensing strategy |
| Probable future economic benefits | Asset will generate future revenue or reduce costs | Business case with quantified benefits |
| Adequate resources | Technical, financial, and other resources available to complete | Team staffed, infrastructure provisioned, budget allocated |
| Reliable cost measurement | Expenditure during development can be reliably measured | Time tracking, cost allocation methodology in place |
Research vs Development in AI Projects
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