Launching Your AI Program from Zero to One
For organizations launching, or re-launching AI, we’ve built a ramp-up program and framework that helps establish formal governance, accelerate adoption, and build a foundation for scalable success.
Reading Guide
Below includes our latest content on AI enablement. Jump to any specific section, or begin with the Introduction to read all our posts in order.
Parallel Tracks to Launch
Introduction: AI advantage comes from enterprise and pilot tracks: build governance and infrastructure while testing real workflows. Start with practical use cases to build fluency, validate guardrails, create champions, and scale toward deeper, differentiated AI integration.
Core Team Design
Effective AI programs rely on three connected roles: strategist, tech implementer, and AI operator. Clear ownership enables scalable deployment, prevents stalled adoption, and turns experimentation into sustained, organization-wide AI execution.
Pilot Setup
This eight-week AI pilot builds hands-on fluency through real workflow experimentation, anchored by a baseline survey and a defined AI stack with guardrails. Participants deliver validated use cases, reusable assets, and enterprise-ready insights.
Guidance & Governance
This section establishes practical AI guidance using a risk spectrum, decision framework, and workflow examples. It helps teams match AI use to risk and impact while forming a scalable foundation for consistent, enterprise-wide governance.
Use Cases & Upskilling
This section guides pilot participants through progressive AI upskilling and use cases, from prompts to workflows. Structured starter use cases and workflow breakdowns help teams deliver quick value while preparing scalable, enterprise-ready AI execution.
AI Onboarding Process
This section defines a Golden Path for AI onboarding, supported by an AI portal and shared learning communities. Together, they remove friction, clarify guardrails, centralize access, and turn individual experimentation into scalable, organization-wide AI adoption.
Pilot Execution
This section outlines how the pilot executes through structured learning, experimentation, shared communities, and standardized assessments. Together, these elements generate real evidence to validate tools, refine governance, and guide enterprise-scale AI rollout.
Program Scalability
This section outlines how to scale AI through a shared reference architecture, support structure, and staged path to production. Together, they align business and IT, sustain adoption, and turn experimentation into durable, enterprise-grade AI capabilities.
Champions Network
The Champions Network connects pilot learning to enterprise scale through a cross-functional community that shares use cases, enables workflows, and validates readiness. It sustains AI adoption by turning experimentation into reusable practices and shared momentum.