Should You Capitalise Your AI Costs? A Practical Guide

The Accounting Gap: Why Most AI Spend Is Expensed

Trillions in AI investment worldwide, yet most of it flows straight to the income statement. This is because AI systems are notoriously difficult to recognise as distinct assets under existing accounting standards.

The core problem is identifiability. Traditional intangible asset standards (IAS 38 or ASC 730) were written for patents, licences, and acquired software—assets you could point to, separate, and sell. An in-house trained AI model embedded across your operations? That fails the identifiability test in most regulatory frameworks.

The result: your P&L is inflated with what should arguably be capital investment. And from a strategic perspective, you lose visibility into the true cost of building AI capability.

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IAS 38: The Six-Criterion Test

Under International Accounting Standards, an intangible asset—including AI systems—can be capitalised only if it meets all six criteria:

The identifiability hurdle is the killer. If your AI system is tightly woven into your core product or operations, regulators will argue it is not separately identifiable. Standalone AI tools (chatbots, recommendation engines, internal analytics platforms) have a better chance.

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ASU 2025-06: US GAAP Takes a Different Path

In September 2025 the FASB issued ASU 2025-06, which updates the US GAAP rules for internal-use software (ASC 350-40). It is not an AI standard, but it covers most AI a company builds for its own use. For annual periods beginning after 15 December 2027 it replaces the project stages with a simpler test: capitalise once management has committed funding and completion is probable, and not while significant development uncertainty remains.

For a US company, more AI development cost can reach the balance sheet. Costs that are traced to the project and incurred once the US GAAP threshold is met are capitalised; staff training, data conversion and work done while the development is still highly uncertain are expensed. For an IFRS reporter, the burden is far higher.

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What Can Be Capitalised? A Cost-by-Cost Breakdown

Once the AI system moves into production and is available for use, the capitalisation phase ends. Any costs after that point—maintenance, monitoring, retraining, infrastructure for live inference—are expensed.

The dividing line is when the asset becomes available for use. IAS 38 and US GAAP both use this gateway. Once your AI system is live, capitalisation stops, amortisation begins, and operational costs are expensed.

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The Capitalisation Decision Tree

Walk through these questions in order. If you answer 'no' to any question, the costs are likely not capitalisable:

1. Are you tracking costs separately? If labour, compute, and data costs are mixed into a general IT budget, capitalisation is off the table. Audit trail required.
2. Can you prove technical feasibility? Do you have prototype results, MVP metrics, or pilot data showing the AI works? Speculation does not count.
3. Are probable future benefits quantifiable? Revenue, cost savings, or decision-quality improvement tied to the AI? Vague 'strategic value' is insufficient.
4. Does the company control the AI (IFRS), or is it internal-use software (US GAAP)? A model hosted and run by a vendor is usually a service, and its costs are expensed under IFRS. US reporters capitalise more implementation cost.
5. Have you reached 'available for use'? Is the system deployed in production? Amortisation starts only then; until then, qualifying costs are capitalised and, under IFRS, the asset is tested for impairment every year (IAS 36.10(a)).

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Impact on Financial Statements

Capitalising AI development costs moves expense from the P&L to the balance sheet, affecting four key metrics:

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Amortisation: How Long Does an AI Asset Live?

Once capitalised, the AI system is amortised over its expected useful life. For AI, this is typically 3–5 years for general-purpose models (large language models, commodity NLP systems) and 5–10 years for proprietary, domain-specific AI (drug discovery, oil reservoir forecasting, bespoke decision engines).

The shorter amortisation reflects the rapid pace of AI advancement and the risk of technological obsolescence. A chatbot deployed today may be superseded by a radically better model in 18 months.

Under IFRS, IAS 36 requires an impairment test whenever there is an indication the asset's value has fallen (loss of competitive advantage, regulatory restriction, market shift); US GAAP tests when events indicate the carrying amount may not be recoverable. A poorly adopted or deprecated AI system triggers an impairment write-down.

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