Generative AI is no longer a fringe experiment, it’s becoming a foundational capability across industries. But as organizations race to adopt these tools, many overlook a foundational principle: AI success isn’t just about better technology, it’s about organizational maturity.
Welcome to Understanding AI Maturity, a new Sakara Digital Leadership Insights series inspired by the work of Professor Melissa Valentine and her team at Stanford University. Drawing from her AI-Driven Leadership course, this series explores the four stages of generative AI maturity and the leadership practices that support each phase. Whether you’re a startup founder, enterprise strategist, CIO, or operations leader focused on making AI a core differentiator, this framework will help you navigate the evolving landscape of AI adoption with clarity and confidence.
Why Maturity Matters
Generative AI (GenAI) tools like ChatGPT, Claude, and Gemini can produce amazing outputs with a simple prompt. But the difference between individual tinkering and enterprise transformation lies in how these tools are integrated, governed, and scaled. Professor Valentine’s maturity model helps us understand this journey, not just in terms of tech, but in terms of readiness, risk, and return.
Her model outlines four distinct stages:
Stage 1: Ad Hoc Use – Individual experimentation with little coordination
Stage 2: Localized Projects – Team-level pilots and MVPs
Stage 3: Programmatic Use – Cross-functional deployment and governance
Stage 4: Enterprise Ecosystems – Fully embedded agentic systems and strategic alignment
Each stage reflects a deeper level of integration, requiring new roles, evaluation methods, and leadership mindsets. And each stage brings its own risks, from shadow IT to agentic breakdowns, that must be thoughtfully managed.
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Agentic vs. Non-Agentic Use Cases
One of the most powerful insights from Professor Valentine’s work is the distinction between agentic and non-agentic use cases. Agentic systems involve autonomous AI agents that perform tasks, make decisions, and interact with other agents or humans. Non-agentic use cases, by contrast, focus on enhancing human workflows, like auto-generating summaries or drafting emails.
Both can be transformative. But they require different deployment strategies, governance models, and cultural shifts. Understanding this distinction is key to designing AI systems that are not only powerful, but sustainable and ethical.
A Glimpse into the Salesforce Case
To illustrate the model, Professor Valentine often references Salesforce’s Help page, a mature, enterprise-grade agentic deployment. While it may look like a simple Q&A tool, it’s actually a sophisticated system that integrates internal data, permissions, and audit trails.
It’s a reminder that GenAI success depends on more than clever prompts—robust architecture, intentional leadership, and a commitment to continuous, iterative evaluation are essential.
What’s Next
In next week’s post, we’ll dive into Stage 1: Ad Hoc Use where curiosity sparks innovation, but shadow IT and security risks begin to surface. We’ll explore how leaders can support bottom-up experimentation while laying the groundwork for responsible growth.
Join the Conversation
Stay tuned. And if you’ve seen early GenAI experiments in your own organization, we’d love to hear what worked, what surprised you, and what you’re still exploring. Please share your insights in the comments to help others learn.
This post is part of a series. View the full series Understanding AI Maturity.
This article was created in collaboration with GenAI and shaped by intentional human insight.
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