The AI-enabled enterprise: Why we are applying software engineering principles to business operations

Over the past year, the conversation around artificial intelligence has undergone a massive evolution, shifting from isolated pilots or experiments with standalone tools. For leaders, the true challenge—and the ultimate prize—lies in building a fully AI-enabled enterprise. This means moving beyond experimentation into true operationalization, creating a scalable framework where AI drives both front-office revenue growth and back-office efficiency.

At Red Hat, we are approaching this transformation with a dual mandate. First, we are focused on building enterprise-grade AI products and platforms for our customers. Second, we are operating as our own "Customer Zero," deploying these very same platforms internally to reshape how we run our business.

To achieve an efficiently scaled internal experience, we look at our internal AI strategy through a comprehensive lens: one that empowers our people, unlocks deep institutional knowledge and anchors everything in an operational philosophy we call Business as Code. At its core, Business as Code is the application of software engineering constructs to business operations using AI. This is how Red Hat transformed our business operations by treating them like a codebase, turning abstract business processes into repeatable, automated systems. 

Empowering the AI-enabled associate through community

True enterprise transformation isn’t a top-down mandate.  It’s what happens when you equip your entire workforce with the right capabilities. We achieved widespread associate adoption by putting advanced AI tools, including coding assistants, self-hosted LLMs, and GPU infrastructure, directly into the hands of our teams.

But tools alone are not enough. Our goal is to have every Red Hat associate intuitively understand and efficiently deploy the capabilities that AI provides. We invested in targeted skills training and established vibrant internal communities of practice. These are spaces where Red Hatters across every department come together to learn, share prompt strategies, and build AI literacy. 

By providing access to these technologies to everyone across the company, and cultivating an AI mindset, we unlocked a powerful new capability for non-technical associates. Today, the majority of our cross-company AI initiatives didn’t start in engineering. They came from the creative problem-solving of our accounting, legal and HR teams. We’re moving way beyond just automating repetitive tasks. By rethinking our workflows, how we solve problems and launch new offerings, we aren’t just cutting costs but also clearing a path for faster revenue. It’s not enough for AI just to make processes more efficient and cheaper; it needs to fully open new business opportunities. 

From bespoke agents to connected insights across the enterprise

As our teams’ abilities expanded, our internal AI solution pipeline evolved to include a sophisticated portfolio of tailored agents designed to solve complex business problems.

On the revenue-generating side of the house, we deployed specialized tools like our Sales Assistant and RFP Agent to streamline customer engagements, accelerate response times, and provide our front-line teams with real-time, actionable data. For back-office tasks, we emphasize efficiency and optimization, whether using an LLM to help create optimized workflows or tapping AI agents to handle repetitive tasks that free our skilled workers to support business growth.

The ultimate realization of this strategy is the development of connected domain-specific insight agents. These agents deliver a broad mix of analysis, governed by the identity of the users, yet connected to provide cross-company understanding. Trained on a mix of enterprise structured and unstructured data and using open, self-hosted LLMs, the agents collaborate across roles and departments, such as finance, legal, and operations, to synthesize complex information. For an executive team, this means no more having to wait for cross-department reports. Instead, we’ve gained the ability to ask a unified network of agents and receive real-time, highly accurate operational insights to inform high-stakes decisions.

The foundation: The "Business as Code" philosophy

While bespoke agents and intelligent insights represent the visible layer of our AI strategy, the underlying engine that makes it scalable is Business as Code. 

For decades, business operations have been shaped by legacy efficiency frameworks, from traditional consulting methodologies to rigid workflow studies. Every business is ultimately a system made of data, policies and processes. Historically, maintaining and governing these elements has been a grueling, human-managed chore. While the logic of turning these processes into machine-readable formats worked on paper, the reality was a little different. Relying on humans to continuously update and maintain documentation just didn’t scale. 

Generative AI completely flips this paradigm.

GenAI gives us the unique ability to better understand, manage and evolve our data, policies and processes naturally. But to do this safely and predictably, we must apply software engineering principles to business processes.

By translating our institutional knowledge, standard operating procedures and runbooks into standard machine-readable formats like Markdown and YAML, we treat our business logic just like software code. This allows us to apply the exact same engineering rigors that Red Hat is famous for in building our open source enterprise platforms for our customers:

• Version control: Every business process and policy is checked into a repository, providing a transparent, auditable history of how our business operates.
• Pull requests and code review: Any change to a business policy must be proposed, reviewed by stakeholders (legal, finance, compliance), and approved before it goes live. This guarantees a clear separation of duties and creates a documented approval trail. Policy changes go through the same peer review rigor as production software code.
• Branching and environment promotion (Dev/Staging/Prod): Policy changes can be piloted in isolation before company-wide rollout. For example, a new expense approval workflow gets tested with one region or department ("staging") before being promoted to the entire organization ("production"). This is the business equivalent of feature branches and deployment environments.
• Automated testing and regression testing: Business rules become testable assertions: "If a discount exceeds 30%, VP approval is required." When a policy changes, automated regression tests catch unintended downstream side effects, before an agent acts on the new rules.
• Rollback and revert: If a new policy or process causes problems in production, it reverts to the last known good version instantly, rather than scrambling to manually undo changes. Every version is a safe checkpoint. 

In addition to structured management of our operations, with the advent of AI and insights agents, our business policies and procedures now become a reliable data source for our connected agents to understand our business more holistically. In doing so, our operations workflows become a significantly more valuable component of our corporate knowledge and are useful both in historical and planning insights.  

Co-creating the future management system

Just as running a CI/CD software shop requires an enterprise software development lifecycle (SDLC), running Business as Code requires a similar foundation. As the industry matures with AI, we see traditional engineering lifecycles naturally evolving into an agent development lifecycle (ADLC). Since AI agents are inherently unpredictable, this new lifecycle model moves beyond static code deployments with continuous automated evaluations, prompt versioning and active safety guardrails to ensure enterprise reliability.

We execute this entire architecture at scale on our own hybrid cloud infrastructure, powered by Red Hat Enterprise Linux (RHEL), Red Hat OpenShift and Red Hat AI.

When an FP&A report process, a legal review workflow, or a sales playbook is treated as code, it stops being a static document gathering dust on a shared drive. It becomes a compounding enterprise asset that can be seamlessly inspected, evaluated, modified, managed and executed by an AI agent, governed by a human, and continuously optimized over time. 

Moving from a fragmented landscape of AI experiments to a unified, code-driven enterprise capability is a journey, but it is one that provides an undeniable competitive advantage. The work we are doing internally to operationalize AI sharpens the exact blueprint, validation and platform credibility we deliver to our customers.

The future of enterprise management will be open, collaborative and grounded in code. Red Hat has always believed that the best way to solve a challenge is to share it, and by sharing these frameworks openly, we hope to co-create the next generation of AI-enabled enterprises together.