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Here’s a problem most organizations won’t admit they have: They’re running 17 different AI training programs at once. Different teams, different frameworks, different terminology, all supposedly working toward the same goal. And the result? Confusion instead of competence.<\/span><\/p>\n This isn’t a hypothetical scenario. Brandon Hall Group\u2122 <\/span>Smartchoice\u00ae Preferred Provider<\/span><\/a>\u00a0<\/span>Docebo<\/span><\/a> recently highlighted a global financial services firm where multiple business units independently created training programs for generative AI, each with its own approach, language, and governance frameworks. Employees working across these units found themselves navigating contradictory mental models rather than building actual capability. If this sounds familiar, you’re not alone.<\/span><\/p>\n The fragmentation problem runs deeper than redundant training programs. What Docebo identifies in their latest analysis resonates strongly with the patterns we’ve observed at Brandon Hall Group\u2122: Organizations are experiencing a three-dimensional fragmentation crisis that threatens their ability to capitalize on AI investments.<\/span><\/p>\n <\/p>\n These three distinct sources of fragmentation each create their own set of business problems.<\/span><\/p>\n Vertical fragmentation<\/b> occurs when different organizational levels receive disconnected training. Executives develop one understanding of AI capabilities while frontline employees get a completely different picture. This misalignment between strategic vision and operational execution creates a gap that widens with each implementation decision.<\/span><\/p>\n Horizontal fragmentation<\/b> emerges when functional departments develop incompatible training approaches. Sales learns one set of AI tools and terminology, customer service learns another, and operations learns a third. The result isn’t just inefficiency \u2014 it actively prevents the cross-functional collaboration that AI-enabled work requires.<\/span><\/p>\n Technical fragmentation<\/b> happens when multiple AI tools demand different training approaches without a unified competency framework. Employees find themselves learning contradictory patterns, trying to reconcile different mental models for similar tasks across various tools.<\/span><\/p>\n Our <\/span>research<\/span><\/a> at Brandon Hall Group\u2122 confirms that organizations consistently struggle with siloed systems that limit effectiveness. We’ve documented how companies face data quality issues, inconsistent data formats, and legacy systems that can’t support modern applications. When training efforts mirror these technical silos, the problem compounds itself.<\/span><\/p>\n <\/p>\n AI implementation isn’t like previous technological shifts. Historical change management assumed a linear progression \u2014 introduce the innovation, integrate it, stabilize it, move on. Training could follow along methodically, one phase at a time.<\/span><\/p>\n AI adoption doesn’t work that way. It unfolds rapidly and simultaneously across organizations, creating a constant state of transformation rather than discrete events. This demands what Docebo calls “continuous change management” \u2014 an approach where human capability development must lead technological implementation, not follow it.<\/span><\/p>\n This shift from episodic to continuous change aligns with our findings about skills-based organizational structures. Traditional job-based structures give way to capability-focused models that organize work around skills rather than fixed roles. Brandon Hall Group\u2122 research shows organizations moving toward comprehensive skills inventories, project-based assignments, and performance management focused on skill development rather than task completion.<\/span><\/p>\n But here’s the challenge: you can’t build that kind of organizational agility with fragmented training. You need integration at scale.<\/span><\/p>\n <\/p>\n For decades, competitive advantage came from optimizing production and minimizing costs \u2014 the manufacturing-era model. AI fundamentally changes this equation. In Docebo\u2019s Scalable Learning model, business combines human innovation with artificial intelligence to transform how work gets done. AI takes over routine tasks. Humans focus on continuous learning, adaptation, and creative problem-solving. Speed of learning becomes the primary competitive differentiator.<\/span><\/p>\n The Brandon Hall Group\u2122 <\/span>AI progression model<\/span><\/a> reveals that organizations evolve through distinct phases \u2014 from manual processes to automated processes to intelligent processes. From traditional hierarchical structures to network structures to ecosystem structures. From experience-based decisions to data-driven decisions to AI-augmented decisions.<\/span><\/p>\n Each phase requires different capabilities, different skills, different training approaches. But \u2014 and this is the critical point \u2014 these evolutions happen simultaneously across different parts of the organization. You can’t manage this with traditional, sequential training programs. You need a unified approach that can flex and adapt across all these dimensions at once.<\/span><\/p>\n <\/p>\n Capability Academies \u2014 structured learning ecosystems centered around business-critical capabilities rather than traditional training initiatives offer the flexibility and adaptability necessary for this type of transformation. Unlike conventional programs, these academies deeply align with organizational goals and are often organized around functional areas of the business. What makes Capability Academies different? They create dynamic partnerships between L&D and core business functions, transcending fragmented approaches. They establish cohesive learning ecosystems that align with strategic imperatives while fostering continuous adaptation.<\/span><\/p>\n This approach addresses a reality we see repeatedly in our research: executives often view capability building through the lens of isolated initiatives\u2014training programs, pilot projects, knowledge-sharing activities. They fail to recognize the interconnected nature of these elements. Adaptive organizations cultivate these components as an integrated system of continuous growth.<\/span><\/p>\n Brandon Hall Group\u2122 data from winners of our\u00a0 <\/span>HCM Excellence Awards\u00ae<\/span><\/a> shows that organizations achieving strategic alignment with AI initiatives report universal success. But getting there requires more than good intentions. It requires infrastructure that supports three essential skill clusters that Docebo identifies:<\/span><\/p>\n Human skills<\/b> enable effective interpersonal interaction, including communication, conflict resolution, and emotional intelligence. As AI handles more routine tasks, these uniquely human capabilities become more valuable, not less.<\/span><\/p>\n Conceptual skills<\/b> allow employees to see entire concepts, analyze problems, and find creative solutions. Strategic thinking, systems thinking, and creative problem-solving enable people to navigate complexity that AI can’t easily replicate.<\/span><\/p>\n Technical skills<\/b> provide the knowledge to work with specific tools and systems. While AI changes which technical skills matter, the need for technical proficiency doesn’t disappear \u2014 it evolves.<\/span><\/p>\n Our competency frameworks at Brandon Hall Group\u2122 define four levels of AI capability: AI Aware, AI Enabled, AI Proficient, and AI Expert. Each level builds on the previous one, creating a progression that moves from basic understanding to strategic innovation. But you can’t build this progression effectively in silos. Different departments creating their own versions of these levels just recreates the fragmentation problem at a higher level.<\/span><\/p>\n <\/p>\n Docebo identifies three key integration models that leading organizations use in their Capability Academies:<\/span><\/p>\n Unified knowledge architecture<\/b> creates a single source of truth for AI competencies and use cases that spans organizational boundaries while allowing functional specialization. This resonates with our findings about integrated HRIS systems with analytics that connect all functions through API integrations and cloud-based infrastructure.<\/span><\/p>\n Federated governance<\/b> establishes clear decision rights for AI training that balance central coordination with functional autonomy. Our research shows that comprehensive AI governance frameworks with defined roles, responsibilities, and decision-making processes are essential at the strategic phase of maturity.<\/span><\/p>\n Cross-functional learning pathways<\/b> develop learning journeys that connect AI literacy foundations with function-specific applications and cross-functional collaboration scenarios. This addresses what we document as the shift from hierarchical structures to networked work models where AI enables coordination across traditional boundaries.<\/span><\/p>\n <\/p>\n Moving from fragmented training to integrated capability academies isn’t just a technical challenge\u2014it’s an organizational transformation. There are four key actions that align with the most successful implementations:<\/span><\/p>\n Establish clear integration ownership at the executive level. This isn’t something that can be delegated down the chain. It requires formal accountability, ideally through an expanded CLO role.<\/span><\/p>\n Conduct an integration audit. You can’t fix fragmentation you haven’t identified. Organizations need comprehensive evaluation of current training approaches to find critical gaps.<\/span><\/p>\n Develop a phased roadmap. Address immediate fragmentation risks while building toward sustainable integration. Our implementation frameworks at Brandon Hall Group\u2122 show that successful transformations move through assessment, foundation, and scale phases \u2014 typically 9 to 18 months for meaningful progress.<\/span><\/p>\n Model integration behavior. The executive team needs to demonstrate the cross-functional collaboration they’re asking the organization to embrace.<\/span><\/p>\n <\/p>\n What Docebo knows and what Brandon Hall Group\u2122 <\/span>research<\/span><\/a> consistently validates is that traditional training approaches are insufficient for the AI era. Organizations need learning systems that adapt quickly, provide ongoing support, and integrate seamlessly with daily work.<\/span><\/p>\nThe Three Faces of Training Fragmentation<\/b><\/h3>\n
Why Traditional Change Management Won’t Work<\/b><\/h3>\n
From Scalable Efficiency to Scalable Learning<\/b><\/h3>\n
The Capability Academy Solution<\/b><\/h3>\n
Three Models for Integration Success<\/b><\/h3>\n
The Implementation Reality<\/b><\/h3>\n
The Continuous Learning Imperative<\/b><\/h3>\n