Enterprise technology leaders believe they are ready for AI-generated code at scale.
In the Code Abundance Readiness Evaluation (CARE) Score self-assessment, organizations scored themselves an average of 83.6 out of 100 across critical readiness categories including governance, cost visibility, productivity measurement, and pipeline visibility.
But the operational reality tells a different story.
Despite high confidence in AI readiness, 81% of enterprise leaders report an increase in production issues tied to AI-generated code. Many organizations still struggle to attribute AI spend to business outcomes, predict rising infrastructure costs, or consistently enforce governance policies across the software delivery lifecycle.
This disconnect sits at the center of the newly released 2026 State of Code Abundance Report from CloudBees.
Based on independent research conducted with more than 200 enterprise technology leaders, the report examines the growing gap between enterprise confidence in AI-powered software delivery and the operational, financial, and governance challenges emerging underneath it.
AI coding tools have fundamentally changed how software gets built. Across the enterprise, engineering teams are generating more code, shipping faster, and integrating AI deeply into development workflows. But as AI accelerates software delivery, enterprises are discovering that producing more code is only part of the challenge. Governing, validating, attributing, and controlling that output at scale is becoming significantly harder.
The Era of “Code Abundance” Has Arrived
AI has solved one of software development’s oldest constraints: writing code.
The result is what we’re calling code abundance: the phenomenon where code is being generated faster than enterprises can effectively test, govern, attribute, and manage it downstream.
And while AI is accelerating developer output, many organizations are still struggling to answer foundational questions:
Is the code actually production-ready?
Can teams trace AI-driven development back to business outcomes?
Who owns failures when AI-generated code causes incidents?
How do enterprises control the growing cost of AI-driven delivery pipelines?
The findings in this year’s report suggest many organizations are still figuring those answers out in real time.
“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
More Velocity Doesn’t Automatically Mean More Business Value
One of the clearest themes from the research is that AI-driven development velocity is outpacing enterprise operational maturity.
Organizations report major increases in code volume, pull requests, and delivery throughput. AI is now widely embedded across engineering workflows, and confidence in AI adoption is remarkably high.
But confidence alone isn’t translating into measurable business outcomes.
The report found that while most organizations believe AI is delivering value, many still struggle to connect AI investment to actual ROI. Teams can measure activity. They can measure output. But attribution remains far more difficult.
While 68% believe AI has clearly delivered business value, organizations can only attribute a third of their AI-related spend to specific business outcomes.
This disconnect creates a dangerous dynamic: enterprises are accelerating software delivery without a clear framework for understanding whether the increased velocity is improving the business or simply increasing operational complexity.
“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.”
Anuj Kapur
President and Chief Executive Officer
CloudBees
Everything Is Green. Nothing Is Ready.
Perhaps the most striking finding in the report is this:
Despite 92% of leaders expressing confidence in the production readiness of AI-generated code, 81% of enterprise technology leaders reported an increase in production issues tied to AI-generated code.
That statistic captures the core tension of the AI era in software delivery. Enterprises trust AI-generated code enough to move it through pipelines faster than ever before, yet many are simultaneously experiencing more downstream instability.
The challenge isn’t necessarily the code itself. It’s everything that happens after the code is written.
Testing, governance, validation, release management, infrastructure scaling, and accountability frameworks are all being pressured by the sheer increase in output AI enables.
As one theme repeatedly surfaced in the research: writing code is no longer the primary bottleneck. Governing it is.
Token Anxiety Is the New Cloud Anxiety
The report also highlights a growing financial challenge organizations are beginning to face: unpredictable AI-related spend.
Much like the early days of cloud adoption, enterprises are finding themselves in an environment where consumption is easy to scale but difficult to forecast and govern.
AI costs are not isolated to model usage alone. They compound downstream across CI/CD infrastructure, testing, security scanning, deployment systems, and operational tooling.
Many organizations still lack mature controls around token consumption, automated governance, and cost attribution. This uncertainty leads to “token anxiety”: growing concern around how AI spend scales, where costs originate, and how to predict them quarter to quarter.
For enterprise leaders, the issue is becoming less about whether AI increases productivity and more about whether organizations can sustainably operate AI-driven delivery at scale.
Introducing the CARE Index
As part of the research, CloudBees is introducing the Code Abundance Readiness Evaluation (CARE) Index. This new framework is designed to measure enterprise readiness for governing AI-powered software delivery.
The CARE Index evaluates organizations across key operational dimensions including:
Cost visibility
Budget predictability
Governance maturity
Productivity measurement
Pipeline visibility
Token governance
The goal isn’t simply to measure AI adoption. It’s to measure whether enterprises have the operational foundation required to manage AI-generated software responsibly at scale.
One of the most important findings from the CARE analysis is that enterprise confidence often exceeds operational reality. Many organizations report strong confidence in their readiness — even while simultaneously reporting production failures, weak attribution models, and governance gaps.
This will become increasingly important as agentic AI moves deeper into enterprise delivery pipelines.
Download the Report
AI now generates or assists in writing 61% of the average enterprise codebase, yet most organizations still lack the visibility, governance, and attribution needed to manage that scale of code production.
Get the full picture in the 2026 State of Code Abundance Report.
Inside the report:
The rise of “code abundance” and its impact on enterprise delivery
Why production failures are increasing despite high confidence in AI-generated code
The growing cost pressures across CI/CD, testing, security, and infrastructure
The emergence of “token anxiety” and unpredictable AI spend
How enterprises are approaching governance, accountability, and ROI measurement
The CARE Index, including how to calculate your own
Download the report to see how enterprise organizations are navigating the realities of AI-powered software delivery and how your organization stacks up.