CloudBees, the leading software delivery solutions provider for enterprises, today released the State of Code Abundance 2026, finding that AI-generated code is straining the enterprise systems built to deliver it, revealing a widening gap between confidence in AI-readiness and operational reality. The survey of more than 200 enterprise technology leaders reveals rising infrastructure costs, weak governance frameworks, and mounting operational risk, with 81% reporting production failures tied to AI-generated code. Meanwhile, "token anxiety" is emerging as finance teams struggle to forecast AI spend quarter to quarter. The pattern mirrors the early days of cloud adoption, when limited visibility and control left enterprises exposed to runaway costs.
81% of enterprise technology leaders report production failures tied to AI-generated code.
Only 31% of AI spend can be attributed to specific business outcomes.
54% report a significant increase in CI/CD infrastructure spend over the past 12 months.
Only 27% have set hard limits or quotas on token usage.
70% view test suite maintenance as a bigger burden than writing code itself.
CloudBees introduces CARE Index measuring enterprise AI readiness, with industry baseline at 83.6/100.
AI is now deeply embedded in enterprise software development, with 64% of leaders saying it is widely adopted or fully integrated into engineering workflows. But increased code output has not translated into clear business impact, leaving organizations struggling to connect AI-driven development to measurable ROI.
CloudBees' findings reflect broader industry trends: external research shows that despite 75% of developers using AI coding tools, most organizations report no measurable improvement in business results.
Key findings include:
Code volume surges: 67% of enterprise technology leaders report a significant increase in code volume over the past 12 months, while 52% cite higher development output in features and pull requests.
Value realization lags: Despite this surge, 36% of organizations track AI spend without measuring ROI or don't measure ROI at all.
Attribution gap persists: Organizations rate themselves highly on ROI measurement confidence (51% very confident), yet only 31% of AI spend can be attributed to specific business outcomes.
AI-related costs are escalating across multiple layers, not just in token consumption, but in the downstream expenses building across testing, infrastructure, and security.
Additional key findings include:
Infrastructure costs are climbing: 54% report a significant increase in CI/CD infrastructure spend over the past 12 months, while 53% say testing, security scanning, and deployment costs have risen alongside growing code volume.
Cost management remains reactive: Only 27% of organizations have set hard limits or quotas on token usage, and just 18% have implemented automated controls.
Budget forecasting remains unresolved: Only 45% describe their AI spend as very predictable quarter-to-quarter.
AI is compressing the time between code creation and deployment, but governance, validation, and accountability frameworks are not keeping pace. When no human fully engages in the cognitive process of building, ownership of failures becomes harder to assign.
Production issues rise as governance lags: 81% have experienced an increase in production issues attributable to AI-generated code.
Validation can't keep up with volume: 70% now view test suite maintenance as a bigger burden than writing code itself, as AI generates more code than teams can effectively validate.
Accountability defaults upwards: 46% say the CTO or VP of Engineering is ultimately accountable for AI-related failures, while only 12% report having a dedicated governance function in place.
"Enterprises are living through the same movie they watched with cloud. Adopt fast, figure out the economics and security implications later, and panic when the bill arrives," said Anuj Kapur, CEO of CloudBees. "This time, the spend is less visible, the benchmarks less clear, and the technology changes week to week. Developers are responding to unclear governance— like teenagers with a credit card and no spending limit. At CloudBees, we believe you can't replace one black box with another and call it progress. Winning isn't just about moving fast. It's about building something you can actually control."
"We've never been able to build and write code like this before; it's exhilarating. But we need to remember that code is just an artifact; it's not the actual outcome organizations are pursuing. The real outcome is a quality product. You don't get that until the code is verified, tested, audited, and deployed into the hands of users." – Phil Nash, Developer Relations Engineer - AI, Agents & MCP, Langflow Project, IBM
As part of this research, CloudBees introduces the Code Abundance Readiness Evaluation (CARE) Index, a proprietary composite score designed to assess how effectively enterprises can track, attribute, and forecast AI-driven costs against productivity outcomes. Based on six dimensions of operational readiness, the CARE Index establishes an industry baseline for AI governance maturity and will serve as a recurring benchmark for measuring enterprise progress year-over-year.
The 2026 industry baseline: 83.6/100 — reflecting strong self-reported confidence in AI readiness across enterprises. However, when measured against operational data, a significant gap emerges between perceived preparedness and actual capability.
About CloudBees
CloudBees is the trusted software delivery solution for the agentic era, bringing control, governance, and intelligence to modern enterprise software delivery.
As organizations adopt AI-driven development and manage increasingly fragmented toolchains, they face growing challenges around visibility, compliance, and operational consistency. CloudBees addresses these challenges by unifying DevSecOps across teams, tools, and environments, providing a consistent layer of control, policy, and insight across the entire software delivery lifecycle.
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