The turf war in corporate learning can be real. It happens for a variety of reasons from lack of awareness to gaps in support to outright operational rebellion. Whatever the reason, learning teams need to come to the place where distributing “control” is strategy and not chaos.
The team at Docebo has articulated something that our research at Brandon Hall Group™ validates with data: The organizations achieving universal success with their AI initiatives are those that have solved the tension between centralized governance and distributed autonomy. Their federated learning model doesn’t just describe a better organizational structure — it maps directly to what we observe in organizations reaching Phase 3 strategic progression, where capability building becomes infrastructure rather than programming.
The Data Behind Distributed Success
Our AI Progression Model research reveals a pattern that should matter to every learning leader: organizations achieving strategic alignment at Phase 3 and above (54%) report universal success with their AI initiatives. Not improved success rates. Not better outcomes. Universal success.
That finding led us to ask: what creates the conditions for universal success? The answer centers on how organizations treat capability development. Those stuck in Phases 1 and 2 approach learning as reactive support and something that responds to business needs. Phase 3 organizations flip this relationship. They build capability as strategic infrastructure that enables business objectives.
This is where Docebo’s federated model demonstrates its real value. The hub-and-spoke architecture they describe isn’t just an organizational chart exercise. It’s a recognition that capability building requires both centralized coherence and distributed responsiveness — and that tension between the two creates the conditions for breakthrough rather than compromise.
Why Centralization Fails and Decentralization Fragments
Docebo traces the familiar pendulum swing that learning organizations have experienced for decades. Centralized models promise alignment but deliver bottlenecks. Decentralized approaches enable speed but create fragmentation. The standard response has been to split the difference with federated structures that satisfy neither requirement particularly well.
What makes their current argument different is the forcing function of AI itself. As they point out, AI acts as both centralizing and decentralizing pressure simultaneously. Organizations need enterprise-wide governance to manage systemic risks, ethical frameworks, and strategic coherence. At the same time, AI enables functional teams to build and deploy solutions at speeds that centralized models can’t match.
Our research data supports this dual reality. Organizations in Phase 3 implement comprehensive AI governance frameworks with clearly defined roles and responsibilities. But they also achieve data-driven decision-making at all organizational levels. The critical insight: these aren’t competing priorities. They’re complementary capabilities that reinforce each other when properly structured.
The Hub-and-Spoke Charter as Capability Constitution
The strength of Docebo’s approach lies in how they’ve thought through decision rights and resource allocation. Their charter model creates what they call a “constitution” for the performance operating model: a clear social contract between the center and the functions.
This maps to a pattern we observe in Phase 3 organizations: they achieve strategic alignment by making explicit what most organizations leave implicit. Who owns what. Who decides what. How resources flow. How conflicts resolve.
Docebo’s framework splits ownership with precision:
The hub owns the infrastructure. The core technology platform, the governance frameworks, the universal user experience. This centralization prevents the fragmentation that kills enterprise-wide initiatives. It ensures stability, security, and the economies of scale that come from world-class shared systems.
The spokes own the domain expertise. The specific knowledge, the business-driven priorities, the contained experimentation. This distribution enables the speed and relevance that centralized models sacrifice.
The brilliance here is recognizing that both sides need real ownership, not just assigned responsibilities. The hub can’t be a service provider that functions dictate to. The spokes can’t be consumers who passively receive what the center builds. Both must invest, both must deliver, both must own outcomes.
Making Autonomy Real Through Resource Commitment
Most federated models fail at the resource question. They assign distributed responsibilities without providing distributed authority or funding. Docebo’s framework addresses this head-on with three pillars of empowerment that deserve attention.
Their co-investment model creates shared ownership through mutual financial commitment. Functions collectively fund the shared infrastructure, transforming them from passive consumers into active investors. The center then co-invests back into functional priorities, with the CLO acting as internal venture capitalist for high-potential initiatives.
This funding flow matters because it solves the accountability problem that plagues most matrix organizations. When the center provides services that functions consume, functions can always claim the center didn’t deliver. When functions invest in shared infrastructure, they own its success. When the center invests in functional priorities, it validates their strategic importance.
The support model prevents isolated struggle. Performance Architects from the central hub serve as internal consultants to the spokes, providing expertise in learning design, analytics, and change management. This ensures that distributed authority doesn’t mean distributed struggle.
The incentive alignment makes capability building a leadership responsibility rather than an add-on activity. When building team capability becomes a formal component of leadership scorecards, the time-for-learning paradox that Docebo identifies starts to dissolve. Leaders create space for development when their success depends on it.
The CLO as Ecosystem Orchestrator
Docebo positions the Chief Learning Officer role at the center of this transformation, and our research on Phase 3 organizations validates why. In organizations achieving strategic alignment, learning leadership has a seat at the leadership table. Decision-making becomes data-driven as standard practice. The learning function shifts from service provider to strategic partner.
The ecosystem orchestrator role that Docebo describes takes this evolution further. The CLO becomes a portfolio strategist for capability, making investment decisions about which functions to onboard and when. They manage the efficiency dividend as internal venture capitalist, seeding experiments and ensuring continuous improvement. They arbitrate conflicts between hub and spokes. They develop the talent pipeline for Performance Architects and spoke leaders.
This is leadership at the system level rather than the function level. It requires seeing the entire federated network as the product rather than individual programs or platforms.
Our data shows why this matters: HCM Excellence Award™ winning organizations leverage learning leadership to provide crucial governance and quality assurance support and alignment. Including 87% of winners directing QA processes, content validation procedures and stakeholder alignment mechanisms as well as 73# providing continuous improvement protocols (Source: 2025 Best Learning Strategy HCM Excellence Award™ winners).
Beyond Learning and Development
Here’s where Docebo’s framing helps us see the bigger pattern: this isn’t just about learning and development. Their argument is that AI transformation requires complete rearchitecting of workforce capability systems. That capability building must embed in business operations rather than remain an isolated function.
Our research on organizational progression bears this out. The transition to Phase 3 requires scaling pilot projects across multiple functions and demonstrating that AI applications deliver consistent value at organizational scale. Analytics capabilities must advance from basic reporting to predictive insights and strategic decision support. Organizations must demonstrate strategic alignment between AI initiatives and business objectives.
None of that happens if capability building stays in the learning silo. It requires the distributed-yet-aligned model that federated autonomy enables.
The hub-and-spoke architecture provides the structure for this broader transformation. The central hub ensures coherence, governance, and scale. The functional spokes ensure relevance, speed, and business integration. The charter defines how they work together. The resource model ensures both sides can execute. The incentive structure ensures leadership attention.
The Implementation Path
Docebo’s next steps framework provides a practical bridge from concept to execution. They recommend starting with a single pilot spoke to establish the model before scaling — a pattern our Phase 3 transition research strongly supports.
The sequence matters: codify the strategic mandate first, working with the pilot function to define specific business outcomes and required capabilities. Launch the pilot spoke by empowering a capability owner and co-creating their charter of autonomy. Deploy Performance Architects to provide expert support. Provide core infrastructure access. Capture the playbook for scaling.
This disciplined approach prevents the chaos that occurs when organizations try to transform everything simultaneously. It creates proof points that build confidence. It develops the talent and systems needed for broader rollout.
Organizations making the Phase 2 to Phase 3 transition typically take 12-24 months. That timeline aligns with building a federated model — fast enough to maintain momentum, slow enough to build real capability.
The Strategic Imperative
The broader pattern here extends beyond learning and development. Organizations are discovering that capability building can’t remain reactive support. It must become strategic infrastructure that enables business execution.
The federated model that Docebo articulates — with clear decision rights, mutual investment, aligned incentives, and ecosystem orchestration — provides a framework for making that shift. Not as organizational theory, but as operational architecture.
Our research shows that organizations reaching Phase 3 strategic alignment achieve universal success with their AI initiatives. They build comprehensive governance while enabling distributed decision making. They align capability development with business objectives. They transform the learning function from service provider to strategic partner.
That transformation doesn’t happen by accident. It requires the kind of intentional architecture that Docebo’s hub-and-spoke model provides. It requires treating capability building as infrastructure rather than programming. It requires distributing control while maintaining coherence.
The organizations that figure this out don’t just improve their learning effectiveness. They create the conditions for universal success with strategic initiatives. That’s worth building toward.
