As AI coding assistants gain traction in legacy enterprise environments, Eradani is positioning its DevOps platform as the missing infrastructure layer for IBM i development teams. The company says many IBM i shops are experimenting with tools such as ChatGPT, Claude, and IBM Bob, but lack the Git-based workflows and CI/CD pipelines needed to safely operationalize AI-generated code. The message arrives at COMMON POWERUp 2026, where modernization of IBM i systems is a central theme.

Artificial intelligence is reaching one of enterprise computing’s most durable platforms: IBM i.

Long associated with RPG development, core business systems, and mission-critical workloads, IBM i environments are increasingly being explored for AI-assisted software development. Tools such as IBM Bob, Anthropic Claude, and OpenAI ChatGPT are showing growing capability in generating and refactoring RPG code.

But Eradani argues the larger challenge is not code generation itself.

It is the development foundation underneath it.

At COMMON POWERUp 2026 in New Orleans, the company is highlighting what it sees as a structural gap in many IBM i organizations: AI coding tools assume modern source control workflows, while many IBM i teams still rely on legacy source management models such as PDM-based environments and source physical files stored directly on the system.

Why AI Coding Hits a Wall on IBM i

Modern AI coding assistants generally work best when source code exists locally on a developer machine or inside connected repositories. That enables code context, version awareness, branching workflows, and automated review pipelines.

Many IBM i environments were not designed that way.

Historically, code often resides on the IBM i server itself as the operational source of truth. Developers may use remote tooling such as RDi or terminal-style workflows, but local Git-native workflows are less common in traditional shops.

That creates friction.

If source code must be manually exported, edited, re-imported, and compiled, AI-assisted development becomes slower and riskier. Instead of accelerating delivery, teams can end up adding operational complexity.

Eradani’s pitch is that IBM i modernization now requires more than new coding assistants—it requires Git-native engineering processes.

Real Git vs “Git-Compatible”

The company draws a distinction many enterprise IT leaders are beginning to recognize: having code mirrored in Git is not the same as working natively in Git.

Some IBM i environments synchronize source to repositories for backup or visibility, but developers may still lack branch-based collaboration, pull requests, commit-level traceability, rollback workflows, and CI/CD triggers.

That matters more in the AI era.

Generative coding systems can produce changes rapidly. Without version discipline, review systems, and automated testing, speed can amplify risk rather than productivity.

Eradani says its DevOps platform gives IBM i teams native support for workflows common across modern engineering organizations, including:

• Branching and merge strategies
• Pull request reviews
• CI/CD pipelines
• Local repository clones
• Integration with GitHub, GitLab, Microsoft Azure DevOps, and Bitbucket
• Connections to Jira, ServiceNow, and SonarQube

For organizations trying to attract younger developers or integrate IBM i into broader engineering teams, those capabilities can be strategically important.

AI Code Requires Governance at Scale

The second part of Eradani’s argument centers on software governance.

Human developers might write dozens of lines of code in an hour. AI assistants can generate hundreds or thousands in minutes. That changes the economics of review.

Manual approval processes that once seemed manageable may become bottlenecks—or worse, ineffective control points.

As enterprises adopt AI coding, automated quality gates become increasingly necessary:

• Static code analysis
• Security scanning
• Pull request approvals
• Test automation
• Deployment traceability
• Rollback controls

Eradani says AI-generated changes should pass through the same pipelines as any other production code, rather than bypassing controls because they were machine-generated.

That stance aligns with broader enterprise software trends. According to Gartner, organizations adopting generative AI in engineering increasingly need governance frameworks covering risk, security, and software lifecycle controls. IDC has also projected continued investment in AI-assisted developer productivity tooling tied to enterprise DevOps modernization.

Why IBM i Teams Face a Talent Issue Too

The issue is not only technical.

Many IBM i organizations are balancing veteran developers who prefer long-standing workflows with newer hires expecting Git, open-source tooling, VS Code environments, and collaborative development models.

That creates internal cultural tension.

Platforms that preserve IBM i strengths while enabling modern workflows may help enterprises bridge generational transition without forcing full platform migration.

Competitive Context

Eradani operates in a broader modernization ecosystem that includes IBM tooling, managed service providers, DevOps vendors, and consultancies focused on legacy transformation.

Its differentiator appears to be practical enablement: helping IBM i teams adopt modern engineering discipline without abandoning the platform.

As AI coding spreads into legacy systems, that niche may become increasingly valuable.

What It Means for Enterprise IT Leaders

The bigger lesson extends beyond IBM i.

Generative coding tools alone do not modernize software delivery. They increase the importance of source control, testing, deployment governance, and developer workflow maturity.

For IBM i shops, AI may be the catalyst that finally forces overdue DevOps transformation.

Eradani is betting that moment has arrived.

Market Landscape

Legacy enterprise platforms are entering a new modernization cycle driven by AI-assisted development. Key trends include:

• Generative AI support for older programming languages
• Git adoption inside legacy environments
• DevOps standardization across hybrid infrastructure
• Developer talent shortages in legacy systems
• AI code governance and secure deployment controls
• CI/CD modernization for mission-critical workloads

IBM i environments are increasingly part of this transition.

Top Insights

• Eradani says IBM i teams adopting AI coding tools often lack Git-native workflows needed for practical implementation.
• The company positions DevOps modernization as the missing layer behind ChatGPT, Claude, and IBM Bob productivity gains.
• AI-generated code increases the need for pull requests, automated testing, and deployment traceability.
• IBM i organizations also face generational pressure from developers expecting modern engineering tools.
• Legacy platforms may use AI adoption as a catalyst for broader software delivery transformation.

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