Feature title:  Proof-of-concept for accelerating AI tooling adoption to enhance package onboarding effectiveness

Feature Overview:
This initiative aims to accelerate the adoption and implementation of artificial intelligence tools and automation within our organization. By strategically leveraging AI, we will enhance engineering effectiveness, improve productivity, and foster a culture of innovation to support AIPCC's transformation into an "AI-First Organization".

Product(s) associated:

RHAIIS: yes
RHEL AI: no
RHOAI: yes

Goals:

The primary goal is to increase engineering effectiveness and productivity by integrating AI tools into our workflows.

• Who benefits from this initiative, and how?
  • AIPCC Engineers benefit from streamlined workflows, tangible time savings, and increased output through the use of AI copilots and automated processes.
  • The AIPCC Organization benefits from increased productivity, a stronger culture of innovation, and alignment with the strategic goal of becoming an AI-First Organization.
  • Red Hat AI component teams benefit from a faster onboarding process to get what they need in their components.
• What is the difference between today’s current state and a world with this Feature?
  • Today: Engineering workflows are largely manual.
  • Future State: AI tools are deeply integrated into engineering processes. AI copilots assist with code, testing, and documentation; platform builds are highly automated; and Jira refinement is significantly faster. A high percentage of engineers will be "AI-First+" users.

Requirements:

• Identify and evaluate AI solutions that can improve engineering workflows.
• Prioritize high-impact AI initiatives that align with organizational goals.
• Guide the implementation of approved projects, ensuring they are properly resourced and delivered.
• Establish a system for tracking AI adoption and impact through quarterly updates.
• Develop and share best practices, playbooks, and implementation guides for approved AI tools.

Done - Acceptance Criteria: TBD

Use Cases - i.e. User Experience & Workflow:

Automated package integration: a Red Hat AI engineer needs to add a new package to a component. They use an AI-automated process that handles integration with minimal manual input. An AIPCC engineer can pick up the work where AI stopped and do the last 25% of work.

Out of Scope:

• Developing new, foundational AI tools from scratch.

• Implementing AI features directly into customer-facing products (the focus is on internal engineering tools).

• Projects that do not align with the stated Key Results for CY2025.

• Creating new workflows, new processes, that would not be compatible with the overall Red Hat AI strategy.

Documentation Considerations :
All documentation will be created for an internal audience of AIPCC engineers and project managers.