How Will Generative AI Affect Value Chains and Transfer Pricing?

Adapting to business change is nothing new. From technological advancements and evolving commercial models to shifts in where and how people work, there has always been a need to understand how change impacts value chains. But what happens when a technological advancement, such as Generative Artificial Intelligence (GenAI), comes along with the potential to enhance business models, influence value creation across supply chains, and shift the composition of an organization’s global workforce? And what if this is happening at a speed and scale never seen before?

While we are still in the early stages of the GenAI revolution, its impact on business models and value chains is starting to take shape.

GenAI marks one of the latest advancements in artificial intelligence, building upon earlier developments such as rule-based systems, machine learning, and deep learning. Today’s generative models can produce original text, images, code, music, and video at unprecedented speed and scale, democratizing access to advanced computational capabilities through natural language interfaces. Beyond content creation, GenAI enables data analysis, scenario simulation, and human decision-making support, making it adaptable across industries and supply chains.

Companies are leveraging GenAI to create new revenue streams and enhance existing products and services, driving top-line growth. At the same time, GenAI can enhance effectiveness and productivity by automating routine tasks, streamlining operations, and optimizing resource allocation — all of which contribute to the reshaping and realigning of investments in people and processes. Examples of recent GenAI advancements include:

• Health care and life sciences: Accelerated drug discovery and molecule design and automated medical diagnostics
• Manufacturing and supply chain: Predictive maintenance, enhanced quality control, and optimization of inventory management
• Retail and e-Commerce: Personalized product recommendations, targeted marketing content, and dynamic pricing
• Media and entertainment: Automated content creation (articles, scripts, music, video editing) and personalized user experiences
• Technology and software development: Rapid prototyping and product innovation, automated code generation, and streamlined software deployment

Spectrum of Business Models

GenAI is driving a diverse spectrum of business models, each with distinct implications for how value is created, captured, and distributed across supply chains.

The categories of business models emerging from the adoption of GenAI include:

• Model developers: Create and optimize foundational AI models, focusing on research and development (R&D) and intellectual property (IP) creation
• Hyperscalers: Provide large-scale cloud computing infrastructure (e.g., compute, storage, network, etc.) and platforms for training and deploying GenAI models. Hyperscalers may develop their own large language models (LLMs) or offer access to third-party LLMs
• Retrieval-Augmented Generation (RAG) users: Build domain-specific applications by integrating proprietary or curated data sources with existing foundational models (for purposes of this article, “RAG users” broadly refers to companies which create customized applications by combining proprietary or curated data sources and platforms with foundational models. This may involve RAG or other integration techniques, depending on the requirements)
• Off-the-Shelf users: Use commercially available, pre-trained GenAI tools for targeted internal and/or commercial initiatives

From developing foundational technologies and infrastructure to deploying company-specific applications and leveraging ready-made tools, these business models bring about new monetization strategies, asset ownership and risk profiles, and increased levels of operational and capital investments. The breadth of GenAI’s influence means every business model, including RAG users and Off-the-Shelf users, may want to revisit their value chains to fully understand the implications of GenAI.

Value Chains Impact

A company’s value chain is the sequence of activities and processes through which it creates and delivers products and services, from R&D, through production and marketing, to sales and after-sales support. Each link in the value chain contributes to the enterprise’s competitive advantage and profitability.

Typically, the value drivers underpinning a value chain include IP (such as technology, brand intangibles, know-how, etc.), supplier and customer relationships, workforce, operational efficiencies, supply chain management, investments and access to capital, and effective risk management.

Given its use as both an internal and commercially facing technology, GenAI has the potential to influence each of these value drivers. GenAI can accelerate the creation of IP, automate operational processes, enhance customer engagement, optimize supply chains, and reshape workforces. It can also lead to new forms of collaboration, governance, and data-driven decision-making, all of which affect how and where value is created within the enterprise.

While the impact of GenAI varies by enterprise and industry, the potential to reshape value chains should be top-of-mind for nearly every tax department. Understanding how GenAI may impact value-creating activities is critical for accurately allocating profits and managing transfer pricing risk in a rapidly evolving environment.

Here are examples of areas of GenAI’s potential influence on specific areas of the value chain:

• New commercial models: For model developers and hyperscalers, new monetization strategies include core technology licenses, usage-based fees and charging for underlying computational resources and hosting. For RAG users and Off-the-Shelf users, new GenAI enabled offerings allow companies to better monetize their proprietary internal knowledge in real time. Examples include new GenAI-driven customer platforms, automated customer “owned” offerings and hyper-personalized customer service agents. These models may shift a company’s focus from traditional product sales or service contracts to scalable, recurring and data-driven revenue streams, introducing new margin dynamics.
• Technology and innovation: Model developers and hyperscalers are advancing GenAI R&D by developing large, multimodal, and specialized models, prioritizing efficiency, safety, and customization, and building robust infrastructure for scalable deployment. Though model developers and hyperscalers are developing new IP, these initiatives typically align closely with the structure of their existing operations, meaning that the impact on transfer pricing operating models may be minimal. Meanwhile, RAG users and Off-the-Shelf users are increasingly using their know-how, workflow solutions, analytics, and feedback loops to tailor their use of LLMs to specific domains, potentially creating new layers of technology and innovation. This domain-specific knowledge serves as a bridge between the LLM and the companies’ internal and commercial operations.
• Infrastructure strategy and investment: The importance of compute power, energy consumption, and hardware demand is reshaping cost-efficient model deployment. Gartner forecasts worldwide GenAI spending to reach a total of $643.9 billion by the end of 2025 with infrastructure accounting for about 90% of spend. Optimizing infrastructure, hardware design, cloud architecture, network performance, security, software efficiency, and sustainability has emerged as a potentially significant new value driver for model developers and hyperscalers. The sheer volume and scale of infrastructure buildout comes with significant capital risk, which must be factored into GenAI influenced value chains.
• GenAI-enabled platform investment: Infrastructure buildout is not the only area of significant investment needed for GenAI. RAG users are increasingly investing in high-quality, proprietary data and specialized platforms to enhance LLM performance, enabling more accurate, context-aware and tailored outputs. These investments improve model relevance and utility to drive competitive advantages. Particularly with respect to customer-facing GenAI-enabled platforms, companies are making significant investments in technologies and resources. Similar to the capital investments for infrastructure, the management and funding of these GenAI investments is critical to establishing the impact on value chains.
• Emerging high-value activities: Certain business activities, which may have historically been routine in nature, are potentially contributing more to the value chain. Newly emerging high-value activities are gaining prominence as GenAI becomes more deeply embedded within enterprises. Key functional areas such as digital and data strategy, data and analytics, procurement and partnerships, security and risk controls, and productization are increasingly critical to capturing value and supporting these new business models. This shift highlights the evolving role of these functions in driving GenAI-enabled transformation and competitive advantage.
• Shifts in workforce: GenAI is starting to reshape workforces by automating roles and functions, with some organizations announcing significant changes in R&D and sales headcount due to GenAI-driven efficiencies. Over the last year, there have been a number of publicly announced workforce reductions in engineering and sales teams across sectors in response to GenAI investments. These workforce shifts raise transfer pricing questions around how to measure, allocate, and attribute the value of automated activities, costs, and returns across entities and jurisdictions as GenAI transforms traditional functions within the value chain.
• Strategic decision-making: Typically, contributions to value drivers are heavily influenced by strategic decision-making. Strategic planning is increasingly becoming accelerated by GenAI, as rapid analysis of complex data enables more centralized, informed, and agile leadership. As global insights become more accessible, there may be opportunities to centralize previously dispersed strategic decision-making and governance across the supply chain.
• Importance of trust: As GenAI technologies become more integrated into enterprise and consumer offerings, the concept of trust is moving from an expectation to a strategic differentiator. Traditionally, brand intangibles, such as reputation and reliability, have always mattered. However, with GenAI, the stakes are higher and the landscape is less familiar. Security, privacy, and ethical stewardship are not just operational necessities; they are critical components of brand equity for GenAI and GenAI-enabled offerings and can become a source of differentiation.
• Internal efficiencies: Companies are increasingly leveraging GenAI to enhance internal efficiencies, driving significant improvements in operational productivity and process optimization across the supply chain. These internal changes may have a significant impact on profit margins and may play a more important role in the value chain than ever before. In a 2025 KPMG Boardroom survey, 76% of respondents noted “optimized operations-efficiency, productivity, cost savings” as the top benefit to the company of adopting or leveraging GenAI.

Based on the breadth and scale of GenAI, most businesses will face a range of questions regarding how changes to these factors may affect their value chains. Tax departments may want to take a comprehensive view of their current value chains and transfer pricing structures to truly understand GenAI’s impact.

Transfer Pricing Considerations

GenAI can influence an enterprise’s transfer pricing model and intercompany transaction pricing as key sources of value change. However, the extent of the impact is heavily dependent on the enterprise’s underlying IP ownership structure and legal entity function and risk profiles associated with GenAI.

First, the impact on transfer pricing depends on whether newly created IP resulting from GenAI (GenAI IP) aligns with the current IP ownership structure.

In a simplified example, when an enterprise maintains a centralized IP ownership model—where all IP, including the newly developed GenAI IP, is held by a single entity or shared among participants in a cost-sharing arrangement (often delineated by territorial IP rights)—the introduction of GenAI typically has a limited effect on the underlying transfer pricing model. In this scenario, the value generated by GenAI is consolidated within the central IP-owning entity, and existing transfer pricing policies (e.g., royalty arrangements or cost-sharing agreements) can generally accommodate the new IP without significant modification. The key is to ensure that the IP owner oversees, directs, and funds the IP contribution inclusive of GenAI IP.

However, if newly created GenAI IP does not align with the centralized IP ownership model, the importance of the global value chain analysis becomes paramount. In this scenario, the creation, ownership and exploitation of GenAI IP may span multiple jurisdictions, resulting in IP being held or developed by different legal entities across the enterprise. This geographic and functional dispersion of IP generating activities introduces complexity to the transfer pricing model, as it requires a careful assessment of where value is actually being created and captured within the enterprise.

Second, existing functional, asset and risk profiles may be altered by GenAI. The use of GenAI by an enterprise may, under certain circumstances, change the relative value contribution of existing activities within the value chain. A previously routine activity may become viewed as a high value activity to the extent that it is identified as a key value driver (e.g., data & analytics, model development, procurement, etc.), which may change the transfer pricing operating model. Even if the same functions are driving those changes and decision-making, it may still require updating current transfer pricing documentation to explain how the changes are impacting the organization.

Changes in functional profiles may necessitate a review of the transfer pricing operating model and policies. Entities performing different functions may warrant a reassessment in their share of profits or require revised intercompany pricing arrangements to reflect their enhanced contribution to the enterprise’s overall value. Similarly, asset and risk profiles associated with GenAI (e.g., ownership of proprietary algorithms, capital investment risk, etc.) may need to be re-evaluated.

As GenAI’s impact on a value chain is unique to each enterprise, the impact on transfer pricing through a change in a value contribution should be continuously monitored to ascertain that the intercompany pricing reflects the relative contribution of the global affiliates within an enterprise.

Proactive Transfer Pricing

Proactively understanding the GenAI impacts on value chains, which should be reflected in the transfer pricing operating model, will help tax departments anticipate risks, identify opportunities for optimization and establish robust and defensible transfer pricing strategies in this rapidly changing environment.

As senior leadership, governments, tax authorities, and other stakeholders start to ask these questions, tax teams should take the following steps to proactively manage GenAI’s impact on existing transfer pricing structures:

• Align with the overall business to understand how GenAI is deployed throughout the supply chain and how this may impact value chains and existing value drivers
• Monitor changes to global workforce resulting from GenAI’s automation and initiatives
• Assess functions, assets, risks, and key value chain segments and locations potentially affected by GenAI
• Analyze the impact of GenAI on intercompany agreements
• Identify and manage any new intercompany transactions and pricing resulting from GenAI
• Monitor and manage IP ownership, development, enhancement, maintenance, protection and exploitation activities and governance of newly developed GenAI IP
• Understand impact to existing Advanced Pricing Arrangements and rulings
• Develop a proactive strategy and narratives in preparation for potential tax authority questions
• Understand how tax authorities may use GenAI in their processes

Tax Authority Views

Digitalization of the economy is not a new topic for tax authorities. However, GenAI is increasingly being recognized for its transformative impact and is likely to attract close scrutiny from tax administrations in search of new sources of taxable value. While there is limited guidance on an enterprise’s use of GenAI, tax authorities would expect to understand how the use of GenAI affects business operations in areas including IP development, workforce changes (i.e., DEMPE activities),the introduction to new products and services, as well as emphasizing the importance of properly characterizing functions, assets, and risks. In this rapidly evolving era, we can anticipate tax authorities scrutinizing how heavily GenAI is integrated into business activities, whether it changes how functions operate together, and whether value creation is distributed throughout the enterprise related to GenAI.

Takeaways

GenAI affects business models in different ways, with the magnitude of impact varying significantly across each. As a result, whether—and how—to reflect GenAI’s influence on the value chain within the transfer pricing model will differ from one organization to another. However, the common thread is clear: Every company should pay close attention to how GenAI is impacting its value creation, as these changes are critical for ensuring that transfer pricing policies remain accurate, relevant, and compliant.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Nick Stavrakis is a Managing Director, Olivia Osier is a Manager, and Saurabh Dhanuka is a Partner in the Economic and Valuation Services—Transfer Pricing practice at KPMG US. Lonnie Brist is a Senior Managing Director and Kisa Kiya is a Senior Manager in Washington National Tax—Economic and Valuation Services practice at KPMG US.

The information in this article is not intended to be “written advice concerning one or more Federal tax matters” subject to the requirements of section 10.37(a)(2) of Treasury Department Circular 230. The information contained herein is of a general nature and based on authorities that are subject to change. Applicability of the information to specific situations should be determined through consultation with your tax adviser. This article represents the views of the authors only, and does not necessarily represent the views or professional advice of KPMG LLP.

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