The article was originally published by The Business Times
This budgeting season, chief financial officers are facing complex artificial intelligence investment decisions.
Choosing between models or deciding a depreciation schedule for graphics processing units is relatively straightforward. Estimating return on investment is a moving target, evidenced by Gartner’s April 2026 survey that found only 28 per cent of AI use cases met expectations.
Accounting for the needed investment in organisational capability to enable the return is trickier still.
The standard AI business case will assume that somewhere in the firm is a seasoned executive who has the competence to supervise the machine, and to tell it when it is wrong.
To the extent that person exists, they gained that capability from years of building domain expertise, perhaps starting as a junior analyst who drafted memos and did anything and everything to learn.
Yet tasks once done by junior staff are now being offloaded to AI, cannibalising the apprenticeships that produce future supervisors capable of assessing AI outputs and designing workflows.
Breaking the pipeline of expertise creates an impairment risk for the asset itself.
To protect against this, a complementary investment in organisational reinvention is required. It should sit within the AI business case rather than being expensed as an overhead cost.
It should be planned with the discipline of capital expenditure, with a replacement schedule, funded from the start and running for as long as the asset runs.
Such organisational reinvention can be expensed in the accounts but governed as capital allocation in the decision, just as companies do in funding R&D.
How capability is depreciating
AI depreciates human and organisational capability through two distinct mechanisms.
First, technology forces some skills into obsolescence. Accountants stopped doing manual arithmetic; spreadsheet mastery, long prized in finance, is now being handed off to a chatbot. People retain the skill but the business stops valuing it.
Disuse, in contrast, is a function of management, policy and company culture.
Cognitive offloading has always been a rational way for humans to preserve mental resources. But now, AI inadvertently facilitates the delegation of judgment, a quality that exists only as a by-product of doing the work.
When arithmetic (a terminal skill) was lost, nothing downstream suffered. But judgment is generative – and precisely what is required to govern the AI doing that same work.
The company’s own capital decisions may therefore be degrading its essential organisational capability.
In our work at The Global Institute For Tomorrow, we have seen this trend accelerate within leadership cohorts over the past year.
Writing skills, conceptual thinking, and sadly, even the ability to communicate clearly are in decline.
A cohort in 2024 toiled over a flip chart mapping a new business model in boxes and arrows. In 2026, the default is to ask a large language model (LLM) to visualise it for them.
Where once a non-native English-speaking professional struggled through articulating his or her ideas, thereby mastering business concepts and linguistic nuance, text and visuals are now generated in seconds.
Through polished outputs, LLMs give the appearance of competency so the human professional demurs.
Addressing this strange new dilemma through human-resources learning and development – budgeted as an operating expense – is unreliable and structurally vulnerable to the vagaries of the business cycle.
Traditional development spending cannot be capitalised because the firm lacks sufficient control over the benefits: Staff can leave after undergoing training. Indeed, AI fluency makes them more attractive in a competitive talent market.
Here lies the problematic asymmetry: AI hardware and software are capitalised, depreciated and reliably funded, while the human capability required to make them productive is relegated to discretionary expenditure – making it the first line item to cut when margins tighten.
S&P’s 2025 Voice of the Enterprise survey found 42 per cent of companies abandoned most AI initiatives that year. The mispricing and implementation failures of early enterprise resource planning roll-outs serve as a recent precedent: Similar mistakes in underfunding and process redesign should be avoided.
Companies cannot change accounting requirements but they can change how internal decisions are made and who owns the outcomes.
Governing AI as capital allocation
Like R&D, organisational development requires an extended payback time, produces uncertain returns and confers benefits that accrue across multiple years.
No serious company manages R&D as discretionary overhead; it is expensed in the accounts for compliance but governed as capital allocation.
When thinking machines work alongside humans, capability development deserves the same treatment. It must be managed through a capital allocation framework, and should not sit as an overhead owned by a function with no accountability for the asset it enables.
Organisational reinvention should produce durable firm-owned artefacts: verification standards specifying which classes of LLM output require human adjudication and deliberate mechanisms, such as for preserving supervisory capacity. Unlike training outcomes, these are codified, auditable and persist even if individuals leave the firm.
Governed as a capital allocation, this complementary investment in organisational reinvention should incur similar obligations: being tied to a named asset, producing defined artefacts, measured against a baseline, subject to a specific hurdle rate, and owned by an executive accountable for the technology’s return instead of sitting with the people function.
Depreciation is continuous, in professional skills as in hardware. In an AI era, it may be that human skills depreciate even faster.
The critical 2027 planning question is therefore not how much to spend on AI, but rather what degree of organisational investment is required, who owns it, and how to measure whether it worked.
Eric Stryson is Managing Director of the Global Institute For Tomorrow (GIFT) in Hong Kong. He possesses expertise in governance, business model innovation, leadership transformation, talent development, and sustainability. He coaches leaders from business, government, and civil society to critically examine their roles, look beyond conventional wisdom, deepen their understanding of global issues, and take ownership of their impact on their organisations and society at large.
