In the final article in this series on preparing L&D for the AI-powered workforce, we tackle the next frontier: how to distribute centralized control without losing alignment—empowering departments with the autonomy to innovate, while keeping the entire enterprise moving in the same strategic direction.
In this playbook, we designed a roadmap outlining how the strategic integration of AI into the talent development practice is a key enabler of competitive advantage. We argue that the need for intentional realignment of the entire talent lifecycle is necessary for companies to effectively prepare their workforce for the changes AI is bringing.
We also emphasized the necessity to reimagine the Chief Learning Officer role to become a central component of workforce readiness as AI continues to unfold. In Action 5, we shared how a powerful, centrally-governed performance-as-a-service operating model for a rescoped L&D function and CLO role will close the execution gap in the workflow. This, however, introduces a new and more sophisticated strategic challenge: a powerful center, if not managed with foresight, can become a bottleneck that stifles the very innovation and speed it was meant to enable at the functional level.
This tension is already creating friction across the enterprise. Functions are rapidly adopting specialized AI tools to solve local problems, but this uncoordinated adoption creates significant enterprise-level risks in governance, security, and strategic alignment. This is not simply an L&D challenge, but a core business challenge where L&D must play a pivotal role. The solution isn’t to double down on central control, which would stifle innovation. It is to enable a federated model that balances functional autonomy with enterprise-wide coherence.
A federated autonomy becomes a force multiplier: accelerating innovation, increasing workforce relevance, and driving faster adaptation to AI-powered business change.
L&D’s shifts: from centralized to decentralized to federated autonomy
For decades, companies have long wrestled with how to organize their L&D functions, traversing between centralized, decentralized, and various hybrid approaches. Centralization, often embodied by iconic corporate universities like those at GE and Motorola, promised enterprise-wide standards and strategic alignment. When this model became too slow or disconnected from business needs, the pendulum would swing to Decentralization, empowering business units to create their own relevant, function-specific training. Inevitably, this would lead to fragmented quality and duplicated costs, causing executives to pull the pendulum back to the center.
The emergence of the Chief Learning Officer (CLO) role in the 1990s was a direct attempt to manage this tension. As online learning matured, the federated model became the most common compromise, with a central L&D function providing core technology and governance. This led to a new philosophy that placed the responsibility for career mobility and skill development on the individual. Organizations responded by empowering subject-matter experts and providing vast content libraries, shifting the central L&D function toward being a curator and enabler of “self-directed” learning.
While well-intentioned, this individual-centric model ran headlong into a harsh operational reality: the “time for learning” paradox. Year after year, the number one reason employees fail to engage in learning is a lack of time. Placing the full burden of development on an already time-poor employee, without structurally creating the space for it, is not empowerment; it is a strategic abdication of responsibility. It creates a culture where learning is valued in principle but impossible in practice.
The advent of AI finally offers a solution to this paradox. It allows us to further develop and improve the federated model, transforming it from a fragile compromise into a dynamic and powerful operating system. It’s not that the hub-and-spoke structure is a new invention; it’s that every component within that structure must now be re-architected to deliver learning within the flow of work, not in competition with it.
L&D as a critical enabler of the AI economy
The historical models of L&D are insufficient for the AI era because the strategic imperatives have fundamentally changed. As we have argued throughout this series, success now requires a complete re-architecting of the L&D function, grounded in two new realities. First, workforce enablement must be elevated to a C-suite priority, with the Chief Learning Officer acting as a central driver of business strategy. Second, capability must be built through integrated ecosystems like Capability Academies, not just fragmented training programs.
Critically, leaders must now grapple with the dual nature of AI itself. It acts as both a centralizing and a decentralizing force simultaneously, creating a powerful new tension that the old federated model cannot resolve. This requires L&D to change its focus in two fundamental and seemingly contradictory ways:
- AI as a centralizing imperative. The rapid infusion of AI into core business processes creates an urgent, enterprise-wide capability mandate. This requires a strong center to lead the overall strategy, manage systemic risks, and ensure a consistent approach to governance and ethics.
- AI as a decentralizing catalyst. At the same time, AI’s power as a tool for content creation and personalization unleashes functional teams. It enables them to build and deploy tailored learning and support solutions at a speed and relevance that was previously impossible, demanding greater local autonomy.
It is this dual reality that makes a more sophisticated, AI-native federated model the only viable path forward.
The hub-and-spoke model: A new charter for federated L&D
The solution to the tension between enterprise scale and functional speed is not a binary choice. The heart of the hybrid hub-and-spoke model is a clear and explicit charter of roles and responsibilities. This charter acts as the “constitution” for L&D’s performance operating model, defining the social contract between the central hub and the functional spokes. It provides a framework of distributed ownership within a unified system, empowering functions to innovate at speed while ensuring the entire enterprise remains strategically aligned.
The hub-and-spoke model is not a loose agreement; it is a disciplined system built on a clear charter of roles and responsibilities. This charter ensures that the central hub can provide scale and stability, while the functional spokes can operate with the speed and relevance their business demands. Here is how the decision rights are distributed:
What the hub (central L&D) owns:
The central L&D function, led by the CLO, is responsible for the foundational assets that protect and enable the entire enterprise.
- The core technology. The hub owns and manages the agentic performance engine itself — the core AI, the integration architecture, the LMS, and the enterprise-wide data models. Why? To ensure technical stability, security, and the economies of scale that come from a single, world-class platform.
- The enterprise governance. The hub, through what we call the AI Accelerator Group, also owns the universal ethical rules, data privacy standards, and risk management protocols. Why? To mitigate systemic, enterprise-level risk and ensure a consistent, responsible approach to AI.
- The universal user experience. The hub owns the core brand, look, and feel of the agentic engine. Why? To provide a single, consistent, and high-quality performance support experience for all employees, regardless of their function.
What the spokes (functional Capability Academies) own:
The functional teams, through their Capability Academies, are empowered with the autonomy to build workforce capability in their specific domains.
- The domain-specific knowledge. In this new model, the role of the functional “spoke” evolves from a simple content creator to a curator of learning experiences. They are not just building courses; they are curating a dynamic, structured knowledge base that serves as a subject-matter source of truth for their part of the central Agentic Performance Engine. This is the creation of a constantly updated body of knowledge to serve as a foundation for real-time learning as the business changes. The Sales Academy, for example, is responsible for ensuring the engine’s information on competitor tactics is updated as often as needed to keep the sales team informed as necessary. This autonomy is critical because deep expertise lives in the functions, and only they can provide the authentic, up-to-the-minute knowledge required to make the central engine truly intelligent.
- The business-driven priorities. The spokes have the primary say in prioritizing which of their specific workflows and friction points the agent should address next, as defined in their Capability Mandates. Why? Because the functions are closest to the customer and the business imperatives. They know what they need to win.
- Contained experimentation. The spokes have a safe harbor to innovate and pilot new learning and support tactics for their specific needs, without requiring central approval for every small test. Why? To foster agility, speed, and solutions that are tailored to the unique culture and demands of each function.
Making autonomy real: the three pillars of empowerment
A charter that assigns responsibility without providing the necessary resources is an unfunded mandate, and it is destined to fail. To make functional autonomy real and sustainable, the enterprise must formally support the spokes with a clear commitment. This commitment is built on three essential pillars of empowerment:
- A clear resource model. Autonomy requires a sophisticated and sustainable funding model, not just a one-way budget allocation. This is a key differentiator from past federated approaches. In this new model, the central L&D hub and the functional spokes engage in a formal co-investment partnership. The core, shared infrastructure of the hub is funded collectively by the spokes, transforming them from passive consumers of a corporate service into active investors with a vested interest in the hub’s efficiency and effectiveness. In turn, the central hub co-invests back into the spokes. The CLO, acting as an internal venture capitalist, can use central funds to match a spoke’s investment in a high-priority or innovative capability project. This two-way funding model creates a powerful dynamic of shared ownership, ensures the hub is directly accountable to the business needs of the spokes, and allows the enterprise to strategically amplify its most promising innovations.
- A defined support model. The spokes are not expected to go it alone. The central hub’s expert talent, the Performance Architects, are available to the spokes as internal consultants and strategic partners. This support model ensures that functional teams have access to world-class expertise in learning design, data analytics, and change management, enabling them to execute on their charter with excellence.
- An aligned incentive model. What gets measured gets done. If functional leaders are measured solely on short-term output, they will always de-prioritize long-term capability building. To make this model work, the organization must align its incentives. Building team capability and successfully executing on the learning charter must become a formal component of leadership performance scorecards, directly linking a leader’s success to the growth and readiness of their people.
These three pillars transform the federated model from a corporate initiative into a fully-resourced, business-integrated hub-and-spoke operating model.
The CLO’s future role: an ecosystem orchestrator
The hub-and-spoke federated model requires a final evolution in the role of the CLO. Having transitioned from an execution partner to the product manager of a performance-as-a-service offering, the CLO’s ultimate mandate is to become something like an ecosystem orchestrator. The CLO role moves beyond managing a single function to stewarding the health, performance, and strategic alignment of the entire federated capability network. As a C-suite peer, the Orchestrator acts as the organization’s portfolio strategist for capability, making the critical investment decisions on which functional spokes to onboard and in what sequence. They manage the efficiency dividend from the performance model as an internal venture capitalist, seeding promising experiments and ensuring the ecosystem is continuously improving. Critically, they serve as the chief diplomat, arbitrating the inevitable high-stakes conflicts between the central hub and the spokes. And finally, as the head of the profession, they are ultimately responsible for developing the new talent, the Performance Architects and spoke leaders, required to operate this new model at scale. This is the ultimate destination for the L&D leader: no longer the head of a delivery function, but the steward of the enterprise’s core engine for building and deploying human capability.
Action 6 next steps
Translating the hub-and-spoke federated model from a strategic concept into an operational reality requires a disciplined approach. Executive teams must focus on architecting five core components that form the foundation of a successful rollout. These components are the key activities that span the Crawl and Walk phases of the deployment model we discussed in Action 5:
- Codify the strategic mandate. The process begins with strategy, not technology. Work with your initial pilot function to co-create the first Capability Mandate blueprint. This document formally codifies the specific business outcomes the function needs to achieve and the capabilities required to deliver them, ensuring the pilot is aligned with enterprise goals from day one.
- Launch the pilot spoke. With the mandate defined, formally launch the first functional Capability Academy as a spoke in the new model. This involves empowering a leader within the function as the accountable owner of capability and co-creating their charter of autonomy, which defines their roles, responsibilities, and decision rights.
- Deploy the enabling talent. Autonomy requires support. Embed one or more Performance Architects from the central hub to act as internal consultants and strategic partners to the new spoke. Their role is to provide the expert guidance needed to design intelligent interventions and ensure the pilot’s success.
- Provide the core infrastructure. The central hub must provide the spoke with access to the shared infrastructure of the agentic performance engine. This includes providing the necessary tools, templates, and data analytics to ensure the learning and support solutions they create are high-quality, consistent, and measurable from the start.
- Capture the playbook. The entire pilot process with its successes, failures, and lessons learned must be documented. This becomes the first chapter of your enterprise’s Capability Autonomy Playbook, creating a proven, data-driven model that can be refined and scaled across the organization.
The AI-powered workforce summary
The six strategic actions we outlined in this playbook represent a fundamental reimagining of how organizations should approach talent development in the modern era. Rather than treating AI as simply another technology to be adopted, successful enterprises recognize that AI transformation requires a complete rearchitecting of workforce capability systems. From establishing robust AI literacy and governance foundations to creating integrated capability academies and performance support engines, each action builds a cohesive learning ecosystem where human innovation and artificial intelligence connects to build a talent capability flywheel. The ultimate goal is not just workforce capability for AI, but the creation of truly adaptive organizations that can learn, evolve, and execute at the speed of change.
The transition from centralized control to distributed autonomy represents the culmination of this transformation journey. We believe that the organizations that successfully implement these six actions will find themselves equipped with a scalable learning infrastructure that empowers departments to innovate within established frameworks while maintaining strategic alignment and governance standards. This distributed model ensures that AI-powered workforce development becomes embedded in the fabric of business operations rather than remaining an isolated function tucked away in a cost-center operating model. The result is an organization where learning happens in the flow of work, performance is continuously optimized, and the talent capability flywheel spins ever faster, sustaining competitive advantage through the strategic deployment of human and technology working in harmony—truly crafting the AI-powered workplace and driving competitive advantage for the company.
Join the conversation
We hope this six-part series has provided a valuable and actionable framework for navigating the complexities of the AI-powered workplace. The conversation on this topic is dynamic and ongoing, and we welcome the thoughts, critiques, and real-world experiences of fellow leaders and practitioners. Share this story—and your insights—using the social share links below.
About the author
Brandon Carson
Brandon Carson is a globally recognized leader in learning and currently serves as Chief Learning Officer at Docebo. He has held prominent roles such as CLO at Starbucks, where he led their global Learning Academies and the Future of Work practice, and Vice President of Learning and Leadership at Walmart, where he was responsible for global leader development and corporate onboarding. Brandon is the author of Learning In The Age of Immediacy: Five Factors For How We Connect, Communicate, and Get Work Done and L&D’s Playbook for the Digital Age, both from ATD Press. He is also the founder of L&D Cares, a nonprofit that offers no-cost coaching, mentoring, and resources to L&D professionals, empowering them to grow and thrive in their career.