Following our initial announcement on June 16th, where we launched AI-Native SAFe as the operating model designed for the Age of AI, we promised to guide our community through this evolution step by step.
On August 12th, we took the next step with Session 5 of our virtual event series: Building AI-Empowered Products with Continuous Innovation and Governance, and released a new set of Framework guidance circled below.
An overview of the key themes covered in session 5 is summarized below.
AI Innovation Pipeline
To support the four primary work items used by Teams and ARTs: Prototypes, Experiments, Features, and Enablers, organizations need an innovation pipeline designed to handle high volumes of AI outputs and automated flows. The AI Innovation Pipeline accelerates learning and ideation by continuously delivering validated solutions to drive customer and business outcomes.
The AI Innovation Pipeline integrates four primary components:
- AI-Empowered Workflows enable people and agents to collaborate as they move outputs through the lifecycle.
- Embedded Policies specify the boundaries, approvals, and stopping rules that keep work safe and ensure accountability for agents and people alike.
- Insights and Evidence capture the pipeline’s audit trail of decisions, actions, and data, and the insights it reveals about both the product and the process that builds it.
- Shared Platforms provide a shared technical foundation that organizes, links, and expands the workflows, policies, insights, evidence, and broader data collected that support the product development process.
AI-empowered Go-to-Market
Go-to-Market (GTM) is what transforms a released product into realized value. In AI-Native SAFe, GTM is treated as an integrated system operating at two levels: a durable GTM strategy that defines how an organization reaches its market and who it serves first, and recurring GTM cycles designed to help customers realize value while connecting results directly back to outcomes.
Steps in the Go-to-Market Cycle
Because AI-empowered products are released at unprecedented speed, functions such as product marketing, sales enablement, and customer success are embedded directly within AI-Native ARTs. Throughout the Go-to-Market Cycle, AI strengthens the system by assisting with cycle execution and continuously refining the overall strategy.
Key considerations for GTM activities include:
- Value activation: communicating the product’s value to its intended market
- Adoption enablement: ensuring users can successfully adopt and use the product
- Outcome measurement: confirming that the product is generating its intended results for customers and the organization
AI Governance and Ethics
Using an AI Governance and Ethics framework helps organizations manage the unpredictability of generative AI and its widespread use across the enterprise. Established by Portfolio Leaders, the VMO, Finance executives, and Enterprise Architects, AI governance and ethics guardrails run continuously across value streams.
AI Governance and Ethics focus on four key pillars:
- Controlling AI Spend: Tracking AI spending and cumulative usage-based token costs across the portfolio. Spend tracking allows leadership to set operating-cost guardrails and make smart choices around which products and roles receive higher token limits to effectively achieve desired outcomes and protect margins.
- Enforcing Responsible AI: Promoting responsible practices to keep AI systems trustworthy, explainable, and compliant. This pillar addresses emerging AI laws and incorporates human-centric controls through formal checkpoints in portfolio reviews, syncs, and business cases.
- Monitoring Agentic Behaviors: Monitoring independent AI actions through automated checks and human review. Portfolios establish multi-layered monitoring across teams and ARTs to prevent autonomous agents from triggering infinite recursive loops or deploying local workflows that break across broader product contexts.
- Managing and Exploiting AI Risk: Managing AI risks while actively seeking opportunities for innovation. Using systems thinking, leadership transforms risk controls into competitive advantages, such as aggregating mandatory AI evaluations into a shared portfolio library, enabling future features to be built faster and more cheaply.
AI Ethics serves as the foundation for these pillars, providing a moral compass for decision-making. Core organizational values are embedded directly into curated data and context, giving teams the psychological safety to ask “Should we?” alongside “Could we?”.
Two Supported Operating Models: Core SAFe and AI-Native SAFe
Transitioning to an AI-Native organization is a journey. To support enterprises at every stage of this transition, the SAFe Framework site now offers two fully supported operating models side by side:
- Core SAFe: The foundation for enterprise agility. Core SAFe remains the essential starting point for scaling Lean and Agile practices successfully across teams, ARTs, and portfolios.
- AI-Native SAFe: The outcome-driven operating model designed for organizations ready to adapt their core practices for the speed, learning cycles, and governance required in the Age of AI.
AI-Native SAFe Virtual Event Series
If you missed the live broadcast of Session 5, watch the full recording below to learn how to build an AI Innovation Pipeline, power your Go-to-Market strategy, and establish robust AI governance and ethics.
To dive deeper into these topics, we encourage you to access the new guidance articles directly from the AI-Native SAFe Big Picture and explore the details at your own pace.
And join us for the last session in the AI-Native SAFe series. To secure your spot and participate in the live Q&A, visit our registration page.
We are excited to be on this journey with you and build the future together.
Stay SAFe,
— Andrew Sales, SAFe Chief Methodologist