Organizations Signal Willingness to Pay for AI in 2026
Survey data shows enterprise AI budgets shifting from experimentation to monetization, improving long-term return visibility
In this week’s Playbook Kickoff, I flagged the growing willingness to pay for AI in 2026; today’s deep dive examines what sits beneath that signal.
Context: What the Survey Reflects
The RBC Capital Markets survey sampled 117 IT leaders across enterprises with revenues ranging from $250 million to over $25 billion. The respondents operate across multiple industries and geographies.
The core signal is not adoption. Adoption is already settled. The signal is willingness to pay—at scale, and with intent.
This matters because most prior AI narratives stalled at pilots. This survey reflects a move beyond that phase.
The Monetization Shift
Ninety percent of CIOs plan to increase AI spending in 2026. More notably, 90% are creating new, dedicated AI budgets. That figure was 85% in 2025.
This distinction matters. The spending is additive. It does not displace existing IT budgets. It expands them.
Production deployment supports this view. Sixty percent of organizations already run AI workloads in production, up from 39% year-over-year. Another 32% expect production readiness within six months.
In practical terms, over 92% of enterprises are either paying for AI today or preparing to do so imminently. This weakens the “perpetual pilot” thesis that underpinned much of the AI bubble skepticism.
Why Willingness to Pay Is Increasing
Strategic alignment has shifted.
Seventy-six percent of CIOs now frame AI around both cost reduction and revenue generation. Earlier cycles emphasized efficiency alone. That framing has matured.
AI now leads incremental software spending for 2026, ahead of cybersecurity and IT service management. It is no longer a side project. It competes for top budget priority.
Returns are being measured.
Between 75% and 80% of organizations report positive returns on generative AI investments. Over 80% of use cases meet or exceed expectations. Among scaled deployments, that figure approaches 90%.
Seventy-two percent of enterprises now formally track GenAI ROI. Productivity and incremental profit dominate the metrics. Boards appear uninterested in abstract benefits.
Cost Structure: What AI Actually Costs
Enterprise AI budgets rarely fail because models are expensive. They fail because models are only part of the bill.
Most 2026 forecasts anchor on API usage and inference costs. That is the visible line item. The larger cost sits underneath—running continuously, scaling quietly, and rarely appearing in pilot-era spreadsheets.
A recent practitioner analysis captures this imbalance well. In “Your AI Budget Is Approved, But…”, Hiren Dhaduk shows that model usage often represents ~30% of total AI spend, while the surrounding infrastructure accounts for the remaining ~70%. Vector databases, observability tools, staging environments, and recurring embedding maintenance persist regardless of query volume.
This explains why moving from proof-of-concept to production typically increases total investment by 250–400%. The increase is not driven by model sophistication alone. It reflects operational reality.
In mature deployments, cost distribution tends to follow a consistent pattern:
Model usage and complexity: ~30–40%
Data preparation and embedding workflows: ~15–25%
Core infrastructure and platform services: ~15–20%
Observability, staging, and governance: material, often underestimated
Annual operating costs for mid-to-large enterprises commonly land between $96K and $540K+, driven by cloud consumption, monitoring, security controls, and retraining cycles. These costs recur. They behave like infrastructure, not experiments.
The implication is straightforward. AI budgets break not due to overreach, but due to under-forecasting. Teams that price the full stack upfront avoid mid-year corrections. Teams that do not tend to discover the gap only after production systems are live.
As AI shifts from experimentation to operations, execution models are adjusting accordingly. Hybrid approaches—combining internal ownership with external, AI-integrated operating capacity—are increasingly used to manage cost, complexity, and scale with fewer surprises.
The Present Enterprise AI Landscape
Adoption is broad and accelerating. Seventy-eight percent of organizations now use AI in at least one business function, up from 55% year-over-year.
Generative AI usage stands at 71%, rising steadily since early 2024. In India, 47% of enterprises already run multiple GenAI use cases in production, with another 23% in pilot.
Yet a funding gap remains. Ninety-five percent of organizations allocate less than 20% of IT budgets to AI. Only 4% exceed that threshold.
Belief outpaces commitment. Conviction is high. Capital discipline remains cautious.
Constraints That Still Matter
Talent availability remains structural.
Forty-four percent of executives cite lack of in-house expertise as the primary constraint. AI job demand has grown 21% annually since 2019. Supply has not kept pace.
Compensation continues rising at roughly 11% per year. The imbalance is expected to persist through 2027. Hybrid delivery models are no longer optional.
Governance has become a cost, not a blocker.
Sixty-nine percent of leaders cite data privacy concerns, up from 43% in late 2024. Yet fewer than 30% of large enterprises operate mature AI governance frameworks.
Most organizations use AI before fully governing it. This introduces regulatory, legal, and reputational exposure. These risks are now embedded operating costs.
Implementation complexity persists.
Data integration absorbs 15–25% of project budgets. Data annotation costs range from $10K to $90K per 100,000 samples. Legacy systems compound the problem.
This explains why pilots scale poorly without structural planning.
Strategic Considerations
For business leaders, AI budgets now behave like infrastructure. They require portfolio oversight, not experimentation cycles. Delayed scaling introduces competitive risk.
For technology teams, governance-first planning reduces long-term exposure. Proofs-of-concept understate production costs. Hybrid execution models improve time-to-value.
For finance teams, cost variance must be assumed. OPEX dominates. Vendor pricing pressure is rising, with forecasted increases near 9% in 2026.
As AI budgets become structural, tool selection matters less in isolation and more in how tools integrate into existing operating models. I previously examined this through a practical lens in Best AI Tools for Online Presence, Content, and Outreach in 2026, where the emphasis was on operational fit rather than feature depth.
Where Execution Models Matter
The survey implicitly highlights an execution gap. Willingness to pay does not equal readiness to build. Talent shortages, governance gaps, and integration complexity require structured delivery models.
This is where blended approaches—combining internal leadership with external, AI-integrated operating teams—have begun to gain traction. Models that reduce hiring friction while maintaining control over data and governance tend to scale more predictably.
Providers such as Expert360.ai, which operate at this intersection, reflect a broader market adjustment rather than a tooling trend. The emphasis has shifted from acquiring AI tools to operationalizing AI capability.
The Bottom Line
The survey reflects a real inflection point. Production deployments are rising. Dedicated budgets are forming. ROI is being measured.
This materially improves the outlook for long-term AI returns in 2026–2027. The bubble narrative weakens.
Execution risk, however, remains substantial. Organizations that manage talent constraints, governance exposure, and scaling economics will convert intent into returns. Others will continue paying tuition.