Reinvent or rust: The learning leader’s guide to AI maturity

In the late 1990s, Jeff Bezos spent years defending Amazon’s strategy to skeptical investors. The company hemorrhaged money while building infrastructure for a future most couldn’t yet see. Bezos wasn’t wrong — he was early. His conviction that the internet would fundamentally reshape commerce was eventually proven right, but it required enduring years of losses and criticism.

Learning leaders are facing a parallel moment of truth. The learning function has reached a critical juncture where incremental progress isn’t sufficient. Organizations that once tolerated L&D as overhead are now demanding that it justify its existence or face elimination through cost reduction. Our choices are: 

1. Reinvent the function as a driver of competitive advantage.
2. Watch it diminish into obsolescence.

For learning leaders, the challenge is to reimagine L&D’s fundamental purpose — shifting from a reactive service that responds to training requests to a proactive force that drives organizational capability. Success requires positioning the learning function not as an expense that delivers courses, but as a business partner that generates resilience, accelerates performance and creates advantages for the company that competitors cannot easily replicate.

An AI maturity model: A strategic framework for organizational transformation

Learning practitioners require tactical guidance for AI workflow integration. Learning leaders, however, need a strategic lens for driving systemic change. Our AI maturity model serves both purposes — offering a diagnostic tool to evaluate organizational readiness while charting an intentional path toward becoming a future-capable learning function.

1. Phase 1 | Ad Hoc: Individual contributors experiment with AI sporadically. The organization lacks formal protocols, oversight mechanisms or knowledge-sharing infrastructure. While some employees may grasp AI fundamentals, overall utilization remains minimal and unstructured.
2. Phase 2 | Exploratory: Informal teams begin collaborative AI exploration. Knowledge exchange emerges organically through shared examples and early templates. Initial workflow patterns and role definitions start taking shape, though capabilities remain confined to specific job functions.
3. Phase 3 | Structured: Organizations implement consistent AI usage protocols. Leadership establishes governance frameworks, change tracking systems and ethical guidelines. Teams deliberately select and customize AI tools while beginning to incorporate AI insights into decision-making processes.
4. Phase 4 | Integrated: AI integration extends throughout core business operations and across departments. Strategic cross-functional coordination between L&D, HR, compliance and IT enables process optimization at scale.
5. Phase 5 | Transformational: Organizations leverage AI as a catalyst for strategic reinvention. Enterprise-wide implementation, systematic refinement and quantifiable business impact become standard practice. Collaborative human+AI work becomes normalized, with ethical AI principles embedded in organizational values.

Learning organizations are primarily operating within Phase 2 or are just entering Phase 3. Teams are experimenting, perhaps documenting guidelines and starting to standardize their toolsets. While this represents meaningful advancement, it falls short of genuine transformation.

The distance separating Phase 3 from Phase 5 isn’t merely incremental progress. It represents the fundamental distinction between accelerating existing practices and reconceiving what learning makes possible. For CLOs, the critical question becomes: How do you guide your organization across this transformational divide while simultaneously meeting immediate stakeholder needs?

The dual challenge: Deliver now, transform for tomorrow

Stakeholders expect faster, better, more effective learning solutions today — ideally leveraging AI to increase efficiency and reduce costs. Simultaneously, learning leaders must prepare their organizations for a future where the very nature of learning delivery has fundamentally changed. 

The journey to transformation has two parallel tracks that must progress simultaneously:

Track 1: Operational Excellence — embedding AI into current workflows, establishing governance, building team capabilities and demonstrating measurable value in the near term.

Track 2: Strategic Positioning — reimagining the L&D function’s role, shifting from content delivery to performance enablement and building the structural foundations for that change.

Many CLOs make the mistake of treating these as sequential. They think: “We’ll get good at Phase 3, then we’ll worry about phases 4 and 5.” But transformation doesn’t work that way. The organizations that will lead in the future are those that are building toward Phase 5 even as they execute in Phase 3. This dual challenge creates real tension, but it is navigable. 

Four fundamental performance-enabling shifts

The essential insight most learning organizations overlook in this conversation is that AI maturity fundamentally concerns purpose, not technology. It requires redefining the learning function’s core mission and the value it generates. 

Under the legacy paradigm, L&D’s mandate centered on creating and distributing training. Success metrics focused on courses completed, participants trained and, occasionally — for sophisticated operations — knowledge retention. This model is collapsing, and AI is accelerating its decline.

The future belongs to learning organizations that embrace performance enablement as their primary objective. This entails:

1. Embedding learning in the flow of work. Not as an afterthought, but as a fundamental design principle. When learning happens in real time, embedded in daily workflows, it becomes immediately applicable and drives sustained behavior change. 
2. Personalizing at scale. Moving beyond one-size-fits-all programs to experiences that reflect individual career aspirations, learning preferences and performance needs. AI makes this possible in ways that were previously unimaginable, but only if L&D teams shift from controlling content to enabling choice.
3. Building future-ready capabilities. Strategic thinking, digital fluency, adaptability, data analytics, AI literacy — these aren’t nice-to-have skills. They’re the foundation of workforce resilience. L&D must lead capability building in these areas, not just for learners, but for learning teams themselves.
4. Proving strategic value. L&D must be seen as a strategic business partner. According to McKinsey research, while 60 percent of companies plan to increase L&D budgets, these investments are contingent on demonstrating measurable results. Learning leaders must show how their initiatives influence performance, productivity and strategic business goals — not just participant rates.

This shift from content delivery to performance enablement is what separates Phase 3 thinking from Phase 5 reality. It’s the difference between using AI to create courses faster and using AI to build adaptive learning ecosystems that evolve with individual learner needs.

The vision: Atomic instructional design and learner-centric ecosystems

If performance enablement is the destination, atomic instructional design is the vehicle that gets us there. This is not a traditional design methodology. This is a reconception of how learning assets are created, assembled and delivered.

In an atomic approach, learning is broken into modular, reusable components that can be rapidly assembled and personalized by AI for every learner’s specific needs. It’s agile, scalable and perfectly suited for a world where speed and adaptability are top priorities.

More important, it enables a shift in power from learning designers to learners themselves. Instead of prescriptively writing every objective and controlling every outcome, L&D teams create collections of modular assets with clear instructional intent. Learners — guided by AI and supported by human expertise — increasingly determine their own objectives and pathways based on their context, challenges and goals. This is what true empathetic design looks like. Atomic design is understanding your audience’s reality and shaping solutions around it.

For learning leaders, this vision requires a significant mindset shift from “we create and control the content” to “we architect environments where learning happens.” It means investing in new capabilities, new technologies and new ways of measuring success. But it’s also the only path to making L&D indispensable in the age of AI.

The structural imperative: Rebuilding L&D for a new era

This transformation isn’t possible without structural change. The operating models, governance frameworks, technology stacks and team capabilities that worked for traditional L&D will not carry organizations to Phase 5.

Learning leaders must drive evolution across multiple dimensions:

• Governance that enables rather than constrains: Traditional centers of excellence and communities of practice need reimagining, not as command-and-control structures, but as advocacy and enablement mechanisms. A hub-and-spoke model can balance enterprise-wide consistency with local flexibility, ensuring transparency and accountability while empowering teams to innovate.
• Technology infrastructure that supports transformation: Modernizing the learning tech stack isn’t optional. AI-enabled authoring tools, digital asset management systems, adaptive learning platforms and real-time analytics infrastructure are the minimum for Phase 5 organizations. But technology must follow governance; adopting tools without clear strategy leads to fragmentation and wasted investment.
• Enterprise-wide collaboration: L&D cannot transform in isolation. The future belongs to organizations where L&D, HR and business leaders co-own the learning agenda. When learning is integrated with talent acquisition, onboarding, development and retention strategies, it becomes a lever for organizational agility and growth — not just employee development.
• New capabilities and mindsets: The most successful learning teams will reimagine their role and embrace human+AI collaboration, not as a threat but as an opportunity to focus on higher-value work: creativity, strategic thinking, ethical oversight and the nuanced judgment that only humans can provide.

Leading through disruption

The brutal truth is this: If L&D doesn’t evolve, cost-containment pressures will erode its value regardless. Organizations are already questioning the ROI of traditional learning functions. As AI speeds up and lowers the cost of content creation, the value proposition of teams that only deliver training collapses.

But there’s also a more optimistic framing: Organizations that get this right won’t just survive; they will build a genuine competitive advantage. In an era where change is the only constant, the ability to rapidly build new capabilities, adapt to market shifts and unleash human potential is a defining differentiator.