The DEMPE framework determines which group entity is entitled to the return on an intangible by examining which entity performed the Development, Enhancement, Maintenance, Protection and Exploitation functions, made the key decisions, bore the risk, and had the people and capability to control what was happening. The analytical apparatus, functional interviews, organisation charts, decision logs, and the contemporaneous email trail showing an engineer approving a design change, rests on an assumption that a human being performed the function. A recent piece of Australian tax commentary notes that this assumption is becoming harder to sustain. The Australian Taxation Office's guidance on intangibles migration, PCG 2024/1, sets out the evidence it expects a multinational to produce when documenting its DEMPE functions. That evidence regime, the commentary observes, presumes human decision-making and physical performance. It does not address what happens when the decision is made by a model.
PCG 2024/1 has been in force since January 2024. The law itself has not changed; what has changed is a growing recognition that the gap between the rule and the operating reality is becoming practically significant. AI systems are themselves valuable intangible assets under ordinary transfer pricing principles: the algorithms, the training data, and the fine-tuned models sitting inside a captive centre's codebase. A separate issue arises one level up. When an AI system performs the actual development and enhancement work, writing and testing code, running design iterations, flagging defects, optimising a process, the question is who counts as the DEMPE contributor. It could be the engineers who built and supervise the model, the entity hosting the compute, the parent that trained the underlying model elsewhere, or no clearly identifiable party if performance becomes diffuse. The commentary frames this as a mismatch between a human-centric international tax architecture and an operating reality that is automating functions the architecture was built to track.
The implications are likely to be more pressing for India than for Australia. India's transfer pricing landscape relies heavily on captive centres and GCCs performing the kind of granular, iterative technical work, software development, testing, engineering support, and increasingly research, that is most amenable to AI augmentation and, potentially, AI substitution. For two decades, the Indian TP dispute over these centres has centred on characterisation: whether a centre is a routine cost-plus service provider or a genuine value-creating R&D contributor, and which comparables support either position. That analysis has assumed the underlying question, who did the work, could be answered by examining headcount, job descriptions, and reporting lines. If an increasing share of the development and enhancement work inside these centres is performed by AI tools that the centre operates but did not necessarily build, the functional analysis becomes harder to write convincingly in either direction. A TPO could argue that the Indian entity performs less genuine DEMPE work than its cost base suggests, because the model does the underlying processing. A taxpayer could equally argue that the Indian entity deserves more than a routine cost-plus return, because supervising, curating, and directing an AI system that performs high-value technical work is itself a sophisticated function that current benchmarking studies are not equipped to price.
The functional analysis may need a distinct category, one that asks not only who performed the function but who exercised meaningful control and judgment over the system performing it. Whether existing documentation templates, functional interviews, organisation charts, decision logs, can capture that distinction without substantial revision is unclear.
A related institutional question follows. If DEMPE evidentiary standards assume human decision-makers, and the CBDT's own scrutiny and risk-selection processes are moving toward AI-driven flagging at the same time, the tax administration sits on both sides of the same conceptual gap: applying a human-centric framework through tools that are themselves not human-centric. For practitioners, the practical implication is to begin documenting, now, the extent of human supervision, curation, and judgment exercised over AI tools used in development and enhancement work inside Indian captive centres, since existing DEMPE templates may not capture that distinction without adaptation.
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