The real reason your AI initiatives are failing

AI has made it faster and easier to change a codebase than ever before. But in a system as complex and interdependent as modern software delivery, writing code has never been the biggest challenge. For most teams, the real constraint is getting that code safely into production.

So while AI assistants and autonomous coding agents have dramatically accelerated the pace of change, for many organizations those changes are piling up against bottlenecks that were already slowing them down. And the consequences are dire.

Study after study has shown that the vast majority of enterprise AI implementations have so far failed to deliver meaningful returns despite investments of hundreds of billions of dollars in the technology. For software teams specifically, Google’s DORA research and CircleCI’s own reporting have found that delivery throughput (the amount of new code reaching customers) stagnated or even declined in 2024 despite growing adoption of AI tooling to accelerate code generation.

Confidence gap, productivity paradox, 70% problem: no matter what you call it, the fact is, faster coding isn’t translating to increasing team velocity. As throughput flatlines and work in progress piles up, more and more organizations are wondering if and when this potential value will be realized.

More code, more problems: The SDLC bottlenecks AI hasn’t solved

If faster code generation isn’t increasing delivery velocity, what’s really holding teams back?

Industry analyst Rachel Stephens recently wrote, “My hypothesis is that we’ve collectively identified and elevated the wrong constraint…We can make individuals more productive at creating more code, but that is not the same as making our entire SDLC more effective and more stable.”

Think of AI-generated code as a pressure test on the plumbing that connects developer laptops to end users. AI has opened the faucet, sending more code surging into the system, but the leaks are showing downstream, in integration, testing, and release pipelines that were never built to handle this much flow.

Let’s look closer at where those cracks are showing up.

Integration bottlenecks

The more code you generate, the more integration points you create. AI-generated pull requests are flooding repos faster than traditional CI pipelines can validate them. Teams report integration queues backing up, sometimes for days, because every PR needs builds, dependency checks, and environment setups. A team that once merged dozens of PRs daily may now face hundreds, each competing for the same pipeline capacity.

Review fatigue

Even when code clears initial builds, it still needs human eyes. But AI-generated pull requests often arrive in bulk, many with sprawling diffs that touch multiple parts of the codebase. Reviewers are left sorting through walls of machine-written code, looking for hidden hallucinations and brittle workarounds lurking underneath. Over time, reviewers may start to burn out under the cognitive load.

Testing delays

Automated testing was designed for the pace and scale of human-led changes. A 10-minute feedback loop once matched the rhythm of manual development, but with AI accelerating how fast developers can write and iterate on code, those same tests now feel like a drag. Every small tweak hits the full suite, forcing developers to wait on results that lag behind the pace of their workflow.

Release friction

Even after passing integration and tests, code still has to run the gauntlet of manual approvals, change reviews, and compliance gates. These steps exist for good reason, but they were built for a world where changes were slower, smaller, and easier to track. Now, AI-generated code moves faster than governance can follow, and release velocity slows.

The business consequences of AI deadlock

All of this adds up to two bad options for organizations:

• Code stagnation. Work piles up in queues, waiting for validation or signoff. On paper, velocity is up, but in reality cycle times lengthen and morale sinks as developers watch their contributions stall in limbo. CFOs see ballooning spend on cloud compute and licensing without the offset of accelerated feature delivery.

• Risky releases. To escape the bottlenecks, many teams cut corners. They skip tests, batch larger pull requests, or bypass manual approvals. That pushes fragility into production, where the costs are far higher. Outages, security vulnerabilities, and compliance breaches become systemic risks.

Faced with the pressure to deliver value, most organizations default to the second option, shipping unvalidated code to keep pace. As a result, they’re accumulating a growing deficit of understanding: more and more software is running in production than anyone on the team truly knows or trusts.

When no one fully understands what’s been shipped, it gets harder to debug issues, explain changes, or feel confident in what’s live. Ultimately, trust breaks down across the business.

AI-generated code has changed the economics of software delivery. The marginal cost of writing code has plummeted, but the marginal cost of safely shipping it has risen. Until organizations rebalance that equation, they’ll be stuck choosing between latency and fragility, both of which undermine the business case for adopting AI in the first place.

What comes next

AI has turned code creation into the cheapest part of the SDLC, shifting the real opportunity to everything that comes after. Validation, orchestration, and release are the new constraints and the next frontier for intelligent automation. Applying AI beyond code generation can help restore confidence, reduce bottlenecks, and accelerate delivery without compromising trust.

At CircleCI, that’s exactly where we’re focused. The first step is already here: an autonomous agent that eliminates CI/CD toil and keeps your pipelines healthy around the clock. It fixes flaky tests, repairs red builds, optimizes configs, and finds efficiencies while you stay focused on building. It learns from your team’s patterns, improves with every run, and continuously streamlines validation so bottlenecks never pile up. Instead of waiting on results or chasing failures, you wake up to green builds and more time to solve the problems only humans can.

And we’re not stopping there. We’re investing in:

• Smarter pipelines that prioritize critical jobs and surface results faster.

• Adaptive testing that runs only the tests relevant to each change.

• Risk-free delivery with automated approvals and safe rollbacks.

• A more intuitive platform with simpler interactions and more powerful automation.

As the volume of changes accelerates, the systems responsible for validating and delivering those changes need to keep up. Otherwise, organizations risk being overwhelmed by code they can’t confidently ship or don’t fully trust.

We’re building for this future now. To help shape how AI improves the rest of the software delivery lifecycle, from validation through release, we’d love your input. Sign up for early access to preview what we’re building.