Finance and procurement teams are buying more AI software while struggling to determine whether they are paying fair prices, according to SpendHound’s AI Spend Report: 2026 Edition. The report, based on a survey of 172 finance and procurement leaders and proprietary spend data from more than 1,300 companies, finds that 57% of respondents are not confident they are paying a fair price for AI tools or do not know.
Enterprise AI adoption is creating a new problem for finance teams: figuring out what AI should actually cost.
SpendHound, a YipitData subsidiary focused on SaaS spend management, has released its AI Spend Report: 2026 Edition, combining survey responses from 172 CFOs, finance directors and heads of procurement with proprietary spending data from more than 1,300 mid-market and enterprise companies.
The research points to a growing gap between AI adoption and financial visibility. Fifty-seven percent of respondents said they are not confident they are paying a fair price for their AI tools or do not know whether their pricing is reasonable. Nineteen percent believe they are overpaying. Only 51% said their AI investments are currently producing measurable ROI.
The findings highlight a structural difference between AI software and conventional SaaS. Traditional software procurement often relies on relatively predictable seat counts, contract terms and established market benchmarks. AI can introduce consumption-based pricing, API usage, model selection and rapidly changing workloads, making budgets harder to forecast.
AI pricing lacks a mature benchmark
SpendHound’s research found that 46% of respondents exceeded their AI budgets in 2025, compared with 37% for traditional finance and accounting software. Meanwhile, 81% expect spending on general-purpose AI to increase in 2026.
The combination suggests that AI adoption can move faster than annual budgeting processes. A team may approve an AI tool based on an initial usage assumption, only to see consumption increase as employees integrate it into more workflows.
That problem becomes more complicated when organizations use multiple foundation models, AI coding tools, enterprise assistants and API-based services.
Gartner estimates worldwide AI spending will reach approximately $2.7 trillion in 2026, up 49.5% year over year. AI software spending alone is forecast at $461.6 billion, while spending on AI agents and assistants is projected to reach $29.2 billion.
For finance organizations, the expansion means AI spend is increasingly becoming an operating-model issue rather than a niche technology expense.
AI is adding to the software stack
One of the more notable findings in SpendHound’s report is that AI has not yet delivered the software consolidation that some early forecasts anticipated.
Only 7% of respondents said general-purpose AI had reduced their traditional finance software spending. Sixty-two percent reported no change, while 17% said their traditional software spending had increased. The report identifies adding AI-native tools as the largest reason respondents expect software bills to rise, cited by 65% of respondents.
AI is also becoming a factor in software replacement decisions.
SpendHound reports that 76% of respondents are actively reconsidering existing vendor relationships because of AI. Twenty-eight percent are very likely to replace or consolidate finance and accounting applications in the next year specifically because of AI capabilities, while another 48% are somewhat likely to do so.
Better AI-native alternatives were cited by 41% as a switching trigger, roughly matching stack consolidation at 42% and poor value or excessive cost at 41%.
That creates a more complicated competitive environment for enterprise software providers. Vendors increasingly have to defend their products not only on features, support and pricing, but also on whether their AI capabilities can replace, augment or justify existing workflows.
AI cost management becomes a distinct discipline
The emerging problem is not simply reducing AI consumption. Finance teams need to understand where AI is being used, which business units are responsible for it, how pricing is calculated and whether the resulting output justifies the cost.
SpendHound’s report found that 22% of organizations do not have a single owner for their AI budget. That fragmentation can make it difficult to distinguish strategic AI investment from decentralized experimentation.
Gartner’s research reinforces the importance of cost visibility as AI pricing evolves. In July, the firm said enterprise AI budgets were coming under greater scrutiny, with buyers increasingly focused on usage efficiency, cost control and measurable outcomes. Gartner also noted that usage-driven spending makes cost transparency and usage tracking increasingly important capabilities for AI platforms.
That puts AI spend management closer to the FinOps model used for cloud infrastructure, although the variables are different. Organizations may need visibility into model selection, tokens, API calls, inference workloads, application-level usage and business-unit consumption.
SpendHound says its AI Spend Visibility module is designed to provide that view across services including OpenAI, Anthropic, Cursor and Amazon Bedrock, with usage and cost data available at model, API and team levels. Those capabilities are company-reported product functionality rather than an independent assessment.
Procurement is becoming part of the AI architecture
The pricing issue also changes how organizations approach vendor negotiations.
SpendHound says its platform uses contract data from more than 1,300 companies to provide pricing benchmarks for SaaS and AI renewals. The underlying proposition is that procurement teams can negotiate more effectively when they have comparable pricing information rather than evaluating a vendor quote in isolation.
That matters as AI software moves toward more variable pricing structures.
Gartner’s broader AI spending forecast shows the scale of the transition. The firm expects AI spending to continue expanding as enterprises embed generative AI models and agents into existing software and workflows.
At the same time, AI could eventually challenge the economics of conventional SaaS itself. Gartner estimates that up to $234 billion of enterprise application software spending could be exposed to “agentic arbitrage” through 2030 as AI agents perform tasks across multiple systems and potentially reduce the need for users to interact with individual applications.
That creates two simultaneous financial questions: how much organizations should spend on AI today, and how AI will change the software costs they already have.
For finance and procurement teams, the SpendHound report suggests the first problem is arriving before the second has been solved. AI budgets are expanding, software stacks are becoming more layered, and pricing benchmarks remain less mature than those of traditional SaaS.
The result is a new category of enterprise AI infrastructure that sits outside model hosting and application development: the financial infrastructure needed to understand, govern and negotiate AI consumption.
Market Landscape
AI spend management is emerging alongside rapid growth in AI platforms, models and agents. Gartner forecasts $2.7 trillion in worldwide AI spending for 2026, including $461.6 billion in AI software and $29.2 billion in AI agents and assistants.
Gartner also forecasts $64.3 billion in 2026 spending on AI models and platforms, up 63.4% from 2025, while highlighting cost control, usage efficiency and transparency as increasingly important to enterprise buyers.
The market is therefore developing on two connected tracks: companies are deploying more AI, while finance and procurement teams are building the visibility and controls needed to manage consumption, vendor pricing and ROI.
Top Insights
- 57% of surveyed finance and procurement leaders lack confidence that they are paying a fair price for AI tools.
- 46% exceeded their AI budget in 2025, highlighting forecasting challenges associated with usage-based AI pricing.
- AI is adding to existing software stacks for many companies rather than immediately replacing traditional SaaS applications.
- 76% are reconsidering vendor relationships because of AI capabilities, making AI a factor in enterprise software procurement.
- Gartner forecasts $29.2 billion in worldwide spending on AI agents and assistants during 2026.
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