AI is creating a sourcing problem that conventional technology categories struggle to capture. As capabilities become embedded across software, cloud services, consulting and autonomous agents, procurement needs visibility not only into what the enterprise spends, but also into the capabilities, data rights and dependencies those purchases create.
Fragmented AI Buying Creates a New Category Challenge
Gartner says chief procurement officers should treat artificial intelligence as a dedicated category management domain as AI purchasing spreads across the enterprise.
The research firm argues that AI sourcing increasingly determines where expertise and enterprise capabilities reside. Procurement decisions can therefore influence how work is performed, which capabilities remain internal and where organizations become dependent on external providers.
The difficulty is that AI does not fit neatly within a conventional supplier market or technology spend category. Organizations can acquire AI through standalone software, embedded functionality, cloud infrastructure, consulting engagements, enterprise applications and purchases initiated directly by business functions.
That fragmentation makes consolidated oversight harder. Two AI investments purchased through different budgets may still raise similar questions around governance, security, data rights, commercial exposure and measurable business value.
Traditional category management typically organizes sourcing around defined supplier markets, contracts and spend areas. Gartner’s proposed approach instead treats AI as a capability that cuts across those boundaries, creating a common management layer around purchases that might otherwise be evaluated separately.
This becomes particularly important as AI assistants and autonomous agents take on more work. Decisions about suppliers increasingly determine whether capabilities sit with employees, external service providers, software platforms or digital workers.
For procurement, that changes the scope of the sourcing decision. Price and contractual terms remain important, but organizations also need to understand what capability they are acquiring, how extensively it may be consumed and what dependence could develop around the provider.
AI Economics Put Greater Pressure on Commercial Governance
Gartner identifies fragmented spending as one of the central obstacles to effective AI management. Expenditure can be distributed among standalone tools, embedded software functions, cloud services, consulting projects and business-led purchases, leaving organizations without a single view of AI exposure.
Consumption adds another complication. Usage can increase faster than governance processes can respond, while limited pricing transparency can make future costs difficult to anticipate. Embedded AI can also make it harder to distinguish the price of the underlying product from the incremental value and cost of its AI capabilities.
Data rights require similar scrutiny. Contracts need to establish how enterprise information can be accessed, processed and used, particularly as AI functionality becomes incorporated into a wider range of applications and services.
Gartner recommends creating enterprise-wide visibility into AI capabilities, spending, suppliers and use cases. Organizations should also establish common standards covering AI acquisition, governance, data rights and risk management.
That requires coordination among procurement, IT, legal, security, risk, data governance, HR and business functions. Procurement’s contribution is commercial discipline and visibility across purchases that may originate from very different parts of the organization.
The framework also calls for AI investments to be measured against business outcomes and workforce effects rather than acquisition activity alone. That can help distinguish capabilities producing measurable value from overlapping tools, underused subscriptions or investments made before the organization is equipped to absorb them.
Build, Buy or Blend Becomes the Bigger Sourcing Decision
The most consequential AI sourcing decisions may ultimately concern capability ownership rather than vendor selection. Gartner recommends deliberately deciding which AI capabilities should be built internally, purchased externally or managed through a combination of both.
That choice can shape future switching costs, negotiating leverage and access to critical expertise. Procurement therefore has an opportunity to map AI dependencies alongside spend before individual tools become deeply embedded. The strongest AI category strategies may not be those that consolidate purchasing most aggressively, but those that preserve enough commercial and technical flexibility to change course as capabilities, pricing models and enterprise requirements develop.