As companies transition toward the next stage of implementing AI prototypes and test cases, they face unprecedented deployment complexity. Issues range from managing data drift and retraining LLMs to handling new challenges around scaling compute. A key element in the build, test and deploy process is pipeline automation. It eliminates manual bottlenecks in continuous integration (CI) and continuous delivery (CD) workflows and accelerates the processes for updating code as well as scaling infrastructure for AI. 

In addition, pipeline-as-code (PaC) adds further control, security and consistency to build processes and offers a blueprint for AI deployments. However, the complexity can be daunting. Research from Gartner illustrates the gap between deployment goals and the current environment, with only 17% of organizations successfully deploying agentic AI in 2026 and 60% still planning to do so within two years. 

As C-suite and IT leaders look for ways to better align automated CI/CD processes and realize their AI initiatives, we consider the benefits and challenges inherent within automation pipelines. We also explore the requirements for pipeline-as-code adoption to help streamline the deployment process.

How AI pipelines differ from traditional CI/CD workflows   

In general, two distinct pipeline domains exist in today’s modern enterprise:

1. The infrastructure orchestration of data layers along with specialized hardware for AI, and
2. Software delivery mechanisms contained within CI/CD. 

Aside from the ongoing enterprise adoption of MLOps, one problem for IT teams is their continued reliance on manual CI/CD methodologies. For example, standard code pipelines comprise predetermined steps, triggers and version controls that contrast sharply with the dynamic, data-driven delivery that today’s LLMs require. 

The AI environment within many enterprises entails a mix of specialized hardware (e.g., GPUs, TPUs, edge, etc.), orchestration, data layers and hybrid cloud deployments. Indeed, the trend for many organizations is adaptive data pipelines and machine learning operations (i.e., MLOps). However, limitations persist in managing configurations and model registries as volumes of data, source code and deployment packages continue to grow. And recent industry research indicates that for software delivery, frequent bottlenecks have shifted heavily toward code review, CI/CD pipeline congestion and main branch validation limits.

In terms of AI deployments, these build queues and bottlenecks have hindered organizations' ability to meet their AI deployment goals. At the same time, executives and IT leaders are wrestling with security and governance constraints of AI deployments, along with financial costs and overcoming workforce skill shortages. 

For Rivers Morrell, Founder and CEO of AI-native data intelligence and interoperability company HeyDonto, automation has distinct requirements, depending on the methodology (e.g., AI, software development, etc.). “CI/CD is deterministic: the same commit produces the same artifact every time, and automation exists to keep it that way. The AI data and infrastructure layer is a different animal, though. Models drift, schemas change underneath you, and the answer that was right on Tuesday can be wrong by Friday, so whatever you build has to move with it,” he states.

How pipeline automation improves AI deployment and CI/CD

Automation is a key component and enabler that benefits both the infrastructure orchestration of data layers and software delivery via CI/CD by removing manual bottlenecks, ensuring repeatable configurations and scaling resources dynamically. It not only offers advantages for software development but also for handling the data-driven validations of AI. 

When it comes to AI, C-suite and IT leaders are transitioning from static CI/CD pipelines to data-driven automation frameworks. They’re looking to simultaneously handle LLM build requirements and resolve software development queue congestion. In terms of AI delivery, pipeline automation eliminates manual hand-offs during model packaging, testing and deployment. It reduces human error and accelerates release cycles for fast-moving AI production. 

“You need automation in both places, but I'd advise IT leaders not to stretch their CI/CD tooling across the AI data layer. It was built to verify artifacts, and it won't notice when the meaning of a data feed shifts,” according to Morrell. 

In software development, CI/CD automation improves code quality, reduces failures and streamlines pipeline management and collaboration. Successful automation minimizes repetitive work while enabling DevOps teams to ship software faster and catch regressions through improved testing before those builds hit production. 

This helps reduce the burden of broken pipelines due to dependency failures and inefficient tests that stall releases. Based on robust version control, pipeline automation enforces policies while maintaining overall consistency — development teams can be assured of fewer failures, gain faster feedback, and automatically roll back as necessary. 

“On adoption, four things I'd keep in mind: Start where it hurts, meaning the pipelines that page somebody at 2 a.m. Treat pipeline code the way you'd treat production code, so it gets reviewed and tested, and somebody owns it. Say what the pipeline is meant to accomplish rather than listing out every step… And keep an eye on your vendor's abstraction, because it can turn into your architecture before you notice. The pipeline definition is yours, and what runs it underneath should be swappable,” Morrell states.

Why pipeline-as-code improves control and consistency for AI deployments

Functioning as an orchestration layer, pipeline-as-code (PaC) works in tandem with automation to instantly register changes in software builds and provide traceability. In relation to AI deployments, PaC automatically activates, tests and deploys AI workflows whenever code or data is modified. That foundation becomes crucial for end-to-end AI pipelines by sequencing automated stages and helping to transform raw data into reliable model predictions.

Moreover, in certain enterprise AI scenarios, PaC is necessary to handle data ingestion, train models, perform evaluations, and service requests. Traditional software pipelines only handle compilation and text-based code. By contrast, AI development introduces massive datasets and heavy compute cycles, making hands-on management impossible.

“Writing your pipeline as code means every change gets tracked, reviewed and reversed if it goes wrong, with a record of all of it,” says Morrell. “We’re in health care data, so sooner or later an auditor asks what this pipeline did to this record and when. If the pipeline only exists in somebody's console settings, we can't answer that.”