# 73% of AI Pricing Professionals Favor Flexible Models. Blanket Premiums Are Collapsing

**Analyst:** Anubha Garg

G2 Research examined AI pricing, packaging, premium tolerance, value proof, and cost governance through 588 in-depth interviews with business professionals who had direct experience with AI pricing, budgeting, and cost management decisions. The report finds that AI buyers increasingly expect vendors to prove value before charging a premium. Buyers want measurable workflow impact, visible spend, and commercial models that adjust as usage changes.

The central buying shift is proof before premium. Customers approve higher AI pricing only when a vendor connects price to measurable outcomes, makes spend visible, and keeps the pricing model flexible enough to scale with usage. Novelty may open a conversation, but it no longer creates automatic pricing power.

## Key Findings

Seventy-four percent of respondents treat AI as recurring operating expense or subscription spend rather than a one-time capital purchase. This makes AI easier to start and forecast, but it also puts the spend into ongoing finance, procurement, and renewal review.

Sixty-six percent of buyers accept only a modest AI premium when the value is justified. Fourteen percent will pay more for clearly superior AI, while another 14% treat any premium as a dealbreaker. The premium gate is narrow, and vendors need differentiated proof instead of relying on a broad AI uplift.

Sixty-four percent of respondents say efficiency and workflow gains justify AI spend, while 31% require hard ROI or measurable business impact. Workflow gains open the budget conversation, but the premium holds when operational gains connect to cost, margin, revenue, quality, contract performance, or risk.

Seventy-three percent of respondents prefer usage-based or hybrid consumption pricing over flat bundled subscriptions. Buyers want a predictable base and a variable layer that reflects understandable usage, compute intensity, output, or realized value.

## How AI Spend Gets Approved

AI budgets most often enter organizations as recurring software spend. This lowers the initial barrier to adoption because finance teams can forecast a subscription or operating expense more easily than a large one-time investment. That same recurring structure also raises the standard for ongoing proof, because the cost remains visible at renewal and expansion.

Buyers want AI spend to be manageable without requiring them to operate the underlying infrastructure. Fifty-eight percent want visibility into usage and spend, 32% prefer vendors to manage backend costs, and 10% want direct internal control through bring-your-own-key or self-management. The practical expectation is clear usage reporting, team-level accountability, forecasts, thresholds, alerts, and renewal logic.

For vendors, the implication is to make control part of the offer. AI should be framed as forecastable operating spend, with usage and cost visibility included in the product experience. Volume commitments and multiyear terms are easier to defend after pilot usage establishes a credible demand curve.

## What Makes a Premium Defensible

Efficiency is the front door to the AI business case. Respondents described time savings, task automation, faster execution, and manual work reduction as the most common reasons to approve AI spend. But productivity alone is often not enough to sustain a premium. Buyers want to know what changed because of the saved time and which business measure records the gain.

The report describes a proof ladder: observe the workflow, verify performance, and record the business consequence. Vendors should name the task removed, shortened, or improved; establish a baseline; measure throughput, accuracy, error reduction, quality, or service improvement; and connect those improvements to cost removal, margin, revenue, contract performance, or reduced risk.

For vendors, the recommendation is to build a proof stack rather than a feature list. The AI premium becomes easier to defend when the value narrative starts with a specific workflow and ends with CFO-ready financial or performance evidence.

## Pricing That Scales With Value

Usage-based and hybrid models dominate buyer preference. A predictable base can support adoption and reduce friction, while a transparent variable layer can reflect high-volume or compute-intensive use. This structure lets customers start using AI without treating every interaction as a procurement event, then lets vendors protect margins when usage scales.

The report also notes that buyers often expect baseline AI capability to be included in the core product or platform tier. Scaled or differentiated usage can then be metered separately, as long as the units are understandable and customers can forecast, monitor, and control the bill.

For vendors, hybrid pricing should be treated as the default commercial architecture. Include baseline AI value in the core offer, meter advanced or high-volume consumption with clear unit economics, and give buyers forecasts, thresholds, alerts, and renewal visibility before usage expands.

## Buyer-Vendor Pricing Gap

A supplementary classification of the same interviews found that 76% of buyer-side respondents with a clear preference want usage-based or hybrid pricing, while 68% of vendor-side respondents describe bundling AI into an enterprise tier. Buyers optimize for accountability and want the bill to move with observable use. Vendors often optimize for packaging simplicity, sales motion, and forecastability.

The mismatch appears across seniority levels. Buyer-side C-suite leaders, directors, managers, and individual contributors all lean toward usage-based pricing when their answers are classifiable. Vendor-side respondents across levels more often describe bundling AI into enterprise tiers.

The role and business-model classifications are directional because they were reconstructed from free-text transcript mentions rather than asked directly in the interviews.

## Strategic Patterns

Five recurring behaviors sit beneath the headline statistics. Finance prefers adjustability over novelty. Pricing power grows with specificity. Visibility matters more than infrastructure ownership. The premium has to remain explainable after pilots and trials. Every pilot needs a commercial graduation path before usage grows.

Together, these patterns show that buyers treat pricing, governance, workflow proof, and renewal as one decision. A low-friction pilot still needs a clear path to scalable, defensible spend.

## Recommendations

AI vendors should lead with a workflow-level proof stack. They should identify the job improved, measure the operational gain, and connect it to cost, revenue, quality, or risk before discussing a premium.

AI vendors should make spend visibility part of the product. Customers need team-level usage, run-rate forecasts, thresholds, alerts, and value measures so recurring AI costs remain manageable.

AI vendors should build hybrid pricing with a clear graduation path. A predictable base can support adoption, while transparent metering can cover differentiated or compute-intensive use.

## FAQ

### How are most organizations budgeting AI today?

Most organizations budget AI as ongoing software spend. In G2's AI pricing research, 74% classify AI as recurring operating expense or subscription cost, compared with 5% who frame it as capital expenditure.

### Are buyers willing to pay more for AI features?

Buyers are willing to pay more for AI, but only within a narrow range. Sixty-six percent accept only a modest premium when value is justified, while 14% reject a premium.

### What kind of value justifies AI spend?

The strongest value proof is workflow impact connected to measurable business outcomes. Efficiency earns attention, but cost, revenue, margin, quality, or risk outcomes sustain the premium.

### Why are governance and visibility important in AI buying?

Governance and visibility matter because they keep recurring AI spend legible. Buyers want forecasts, thresholds, alerts, and team-level accountability before usage expands.

### Which pricing models best fit buyer expectations?

Usage-based or hybrid pricing best fits current buyer expectations. A predictable base supports adoption, while a transparent variable layer ties expansion to usage, compute intensity, outputs, or realized value.

## Methodology

G2 Research interviewed 588 business professionals across roles, industries, and company sizes, including technology, financial services, healthcare, manufacturing, and retail. Participants had direct experience with AI pricing, budgeting, and cost management decisions. Interviews lasted up to 31 minutes and covered AI pricing structure and packaging, tolerance for AI premiums, outcomes used to justify spend, and AI cost governance, visibility, and operating control. G2 used AI to analyze the transcripts for semantic understanding, then ran multi-iteration validation and cross-verification. G2's AI Custom Research team independently reviewed each transcript to inform the narrative, context, and conclusions.

Question-level bases vary because percentages reflect the interviews that addressed each topic. The supplied research materials did not include verified field dates or a structured geography field, so the report does not claim a collection window or geographic representation.

**Topics covered:** Software Buying, Pricing & Monetisation