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Ways to Get Involved in AI4SDLC

The AI for Software Development Life Cycle (#AI4SDLC) initiative welcomes contributions and collaboration from the DoW community, industry partners, and academic institutions. There are multiple pathways to participate based on your capacity, expertise, and interests.

Work Group Membership

Full membership in the AI4SDLC work group represents a leadership role in shaping the direction and content of our guidance. Members actively contribute to strategy, research, and guidance development. Membership is selective based on specific criteria that ensure alignment with our mission and capacity to sustain engagement.

View Membership Criteria

Domain Reviewers

Domain expertise is invaluable in ensuring our guidance plays are technically sound, practically applicable, and address real-world challenges. Domain reviewers provide focused feedback on specific guidance plays within their area of expertise.

As a domain reviewer, you will:

  • Review guidance plays relevant to your domain area
  • Provide insights and recommendations for improvement
  • Help ensure content reflects current best practices and challenges
  • Contribute to the quality and credibility of our guidance

This pathway requires a moderate time commitment focused on your specific domain of expertise.

Early Adopters

Early adopters are organizations or teams actively implementing AI in their SDLC processes. This pathway offers two complementary forms of engagement:

Interactive Feedback and Influence

Early adopters work directly with the AI4SDLC team to:

  • Test and validate guidance plays in real operational contexts
  • Provide feedback on practical applicability and gaps
  • Influence the direction of future guidance development
  • Contribute to shaping DoD-wide AI adoption practices

Rapid Case Study Development

Early adopters also participate in generating lessons-learned case studies by: - Sharing before and after metrics related to their AI adoption efforts - Contributing both successes and challenges encountered - Allowing us to capture your organization's real-world experience - Reviewing brief (approximately two-page) case studies for accuracy before publication

Case studies will be attributed to named organizations to ensure credibility and specificity of lessons learned. This approach allows DoD and industry to learn from both successes and challenges in a transparent way.

Early adopters typically focus their participation on one or two specific SDLC phase areas to keep the engagement manageable while generating high-value insights.

Structural Collaborations

Beyond direct participation, the AI4SDLC initiative actively builds structural connections across the broader DoW ecosystem:

We establish relationships with DoW and industry technical communities to:

  • Prevent duplication of effort across related initiatives
  • Enable broad collaboration and knowledge sharing
  • Link community members to relevant AI4SDLC guidance
  • Coordinate on common challenges and solutions

Cross-Linked Knowledge Repositories

We create bidirectional links with established knowledge bases and guidance repositories (such as GenAI.mil) to:

  • Direct users to complementary resources and expertise
  • Build a comprehensive web of AI governance and adoption information
  • Reduce fragmentation of knowledge across DoD
  • Create pathways for discovery and navigation across related bodies of work

Getting Started

Interested in participating? The specific pathway that fits your situation depends on your role, expertise, and capacity:

  • Work Group Member: You're ready to lead strategy and guidance development
  • Domain Reviewer: You have deep expertise in a specific area covered by our guidance
  • Early Adopter: You're actively implementing AI in your SDLC and can share lived experience
  • Community Collaborator: You lead or participate in a DoD technical community
  • Knowledge Bridge: Your organization maintains complementary guidance or resources

For inquiries about participation or to learn more about any of these pathways, please reach out to the AI4SDLC team.