This is the third article in our series exploring the six strategic actions that executive teams should take to prepare their organization for the age of AI. This article explores our second action and examines three essential components for successful implementation: The shift from scalable efficiency to scalable learning, the need for a continuous approach to change management, and the strategic implementation of Capability Academies.
Corporate efforts to rewire L&D operations towards skills-forward development and workforce growth have faced challenges, including insufficient data infrastructure, outdated HR technology, and a lack of resources in data science, analytics, and AI within HR and L&D functions. This fragmentation in capability and resources has made it difficult for HR and L&D to formulate actionable strategies to support much needed AI transformation. As a result, they struggle to accelerate workforce capability as advanced intelligence is being integrated into the workplace. The risk of slow, deficient, or stalled workforce development will lead companies to fall short in their ability to remain competitive as AI-related improvements continue to outpace L&D’s current operating model.
Moving from scalable efficiency to scalable learning
The digital transformation of the past three decades enabled the rapid incorporation of advanced intelligence into the workplace. This accelerated a significant shift away from the manufacturing-era operating model of Scalable Efficiency, which focused on driving cost out and standardizing processes, toward a new operating model based on Scalable Learning, where adaptability, innovation, and personalization define success.
Scalable Efficiency
- Focus on driving cost out of the business
- Designed for optimal productivity and standardization
- Tightly specified, routine tasks
- Predictability valued over adaptability
- Knowledge as a static resource to be leveraged
Scalable Learning
- Focus on how organizations learn faster at scale
- Create infrastructure for new value creation
- Delegate efficiency to machines; keep human focus on creativity
- Build systems where workers are motivated to learn
- Effectiveness as the primary human pursuit
Scalable Efficiency thrived when competitive advantage came from optimizing production and minimizing costs. However, AI has changed the nature of work and is redefining competitive advantage – the core reason why moving to Scalable Learning is crucial. With Scalable Learning, business combines the strengths of human innovation and artificial intelligence (HI+AI) to fuel a profound change in how work gets done by:
- Ensuring AI and automation take over routine tasks. AI performs repetitive tasks with greater speed, accuracy, and cost-effectiveness than humans
- Strategically navigating rapid change and uncertainty. The pace of technological change is accelerating, which means the workforce needs tools to constantly adapt to change
- Learning faster at scale. The primary focus for humans is to continuously acquire new knowledge and capabilities
Scalable Learning recognizes that in a world shaped by AI, the ability to learn, adapt, and innovate quickly and at scale will be the key differentiator for every organization regardless of size..
AI has changed the nature of work and is redefining competitive advantage—the core reason why moving to Scalable Learning is crucial.
Leading continuous adaptation
AI implementation differs fundamentally from previous technological shifts. Historically, change management progressed linearly: innovations were introduced, integrated, and eventually stabilized into new operational norms. Established learning approaches demanded sequential development, progressing methodically alongside technological changes as they were implemented. In contrast, AI adoption unfolds rapidly and simultaneously across organizations, generating complex talent development challenges. This necessitates a continuous evolution in change management, where human capability development must lead technological implementation. Adaptation becomes an ongoing process rather than a discrete event, requiring organizations and individuals to navigate a constant state of transformation. HR and L&D are uniquely positioned to collaborate on leading this company-wide evolution, ensuring a cohesive and strategically aligned transition.
Varied AI training throughout the organization can lead to misalignment, where strategic understanding differs from operational execution. This fragmentation substantially reduces the potential value of AI investments and has the potential to stall company efficiencies and success. Fragmentation can also create anxiety and fear across the workforce, as employees are prone to evaluate the impact of AI on their own jobs and future potential.
Successfully navigating AI integration also requires cultivating psychological safety throughout all organizational levels. Employees across the enterprise—from corporate offices to frontline operations—may experience apprehension or uncertainty about their professional futures, triggering subtle defensive responses. These manifestations of resistance frequently emerge not as overt opposition but as nuanced avoidance behaviors: redirecting AI-related responsibilities, preserving manual workflows, or establishing parallel processes that circumvent AI-augmented solutions. Recognizing and addressing these underlying concerns becomes essential for genuine adoption.
Forward-thinking organizations recognize that preserving and evolving the psychological contract requires transparent communication about how AI will transform roles, cross-functional involvement of employees in implementation decisions, and tangible commitment to reskilling pathways. Leaders who acknowledge the emotional component of AI adoption while simultaneously maintaining clarity about its strategic necessity create the psychological safety required for genuine experimentation and learning.
Building upon a foundation of psychological safety, organizations must actively cultivate change resilience. Change resilience refers to the capacity to navigate uncertainty, bounce back from challenges, and embrace continuous adaptation in a dynamic environment. Change resilience must now be a core element of organizational change processes.
Organizations that approach AI transformation through conventional change management frameworks today are invariably encountering resistance that transcends typical adoption barriers. In this dynamic landscape, developing human capability needs to precede and guide technological implementation, and will be continuous. Successful change management in the AI era requires organizations to reimagine the employee-technology relationship as a collaborative partnership rather than a sequential transition, where human judgment and AI capabilities mutually enhance each other’s contributions to organizational value.
Capability Academies in workforce development
To support this continuous adaptation and build change resilience at scale, organizations require a structured approach that goes beyond traditional training initiatives. This is where Capability Academies emerge as a critical component of the Scalable Learning model. By creating focused learning ecosystems around business-critical capabilities, these academies provide the infrastructure needed to systematically develop the skills required in an AI-transformed workplace.
Unlike traditional training initiatives, Capability Academies deeply align with organizational goals and are often centered around functional areas of the business. They provide targeted learning experiences, tools, and resources to build a future-ready workforce that can execute on the rapidly changing work models brought about by AI and automation.
Unlike traditional training initiatives, capability academies deeply align with organizational goals and are often centered around functional areas of the business.
Capability Academies emerged as a strategic evolution beyond conventional L&D methodologies, which historically struggled to cultivate deep organizational expertise and facilitate the accelerated skill development demanded by AI transformation. These academies establish dynamic partnerships between L&D and core business functions, transcending the fragmented approaches that impede change resilience. By orchestrating talent development across the enterprise, Capability Academies create cohesive learning ecosystems that align precisely with strategic imperatives while fostering the continuous adaptation necessary in an AI-transformed workplace. This integrated model provides the infrastructure needed to systematically develop critical capabilities at scale.
Executives frequently view capability building through the lens of isolated initiatives such as training programs, experimental projects, and knowledge-sharing activities, failing to recognize their interconnected nature. In contrast, truly adaptive organizations cultivate these elements as an integrated system of continuous growth, a talent flywheel that generates accelerating momentum directly linked to business outcomes. The strategic imperative now lies in harnessing Capability Academies to systematically develop the workforce across three essential skill clusters:
- Human skills. The ability to interact, work, or relate effectively with people and to motivate performance. These skills enable companies to direct the use of human potential in new ways driving better results. Examples of human skills include interpersonal communication, conflict resolution, and emotional intelligence.
- Conceptual skills. The ability to see an entire concept, analyze and diagnose a problem, and find creative solutions. These skills enable employees to effectively predict hurdles their department or the business may face. Examples of conceptual skills include strategic thinking, systems thinking, and creative problem solving.
- Technical skills. The knowledge and abilities required to perform specific tasks or duties in a particular field or industry. These skills are often related to the operation of machines, software, production tools, and pieces of equipment. Examples of technical skills include software proficiency, data storytelling, machine operation and maintenance, and fundamentals of programming and code.
The AI capability academy: An example
A rescoped L&D function should work directly with senior leadership and the enterprise functions to realize the true value from AI implementation through the development of an AI Capability Academy. Most organizations are experiencing three distinct types of AI training fragmentation today:
- Vertical fragmentation. Different levels of the organization receive disconnected AI training, creating misalignment between executive understanding and frontline implementation
- Horizontal fragmentation. Functional departments developing incompatible AI training approaches, preventing cross-functional collaboration and knowledge transfer
- Technical fragmentation. Multiple AI tools require different training approaches without a unified competency framework, forcing employees to navigate contradictory mental models
This fragmentation generates substantial business costs through reduced adoption rates, inconsistent application, compliance vulnerabilities, and diminished return on AI investments. Consider one global financial services organization where AI implementation teams across different business units independently created 17 separate training programs for generative AI. Each program featured unique terminology, use cases, and governance frameworks. Employees working across multiple units encountered confusion, instead of building competence.
Successfully addressing fragmentation requires a comprehensive strategy that goes well beyond merely consolidating existing training programs. Organizations must fundamentally reimagine how their learning functions operate in relation to AI implementation.mentally reimagine how their learning functions operate in relation to AI implementation.
Architectural models for integration success
Leading organizations have moved beyond fragmented approaches by implementing three key integration models into their AI Capability Academies:
- Unified knowledge architecture. Creating a single source of truth for AI competencies and use cases that spans organizational boundaries while allowing functional specialization
- Federated governance. Establishing clear decision rights for AI training that balance central coordination with functional autonomy, similar to the governance approaches outlined in our previous article
- Cross-functional learning pathways. Developing learning journeys that connect AI literacy foundations with function-specific applications and cross-functional collaboration scenarios
From fragmentation to integration
Transforming fragmented training into a cohesive Capability Academy for AI requires executives to prioritize four key actions:
- Establish clear integration ownership. Designate accountability for training integration at the executive level, ideally as a formal expansion of the CLO role
- Conduct an integration audit and capability alignment review. Evaluate your organization’s current training approaches to identify critical gaps and ensure alignment with business needs
- Develop your integration roadmap. Create a phased plan that addresses immediate fragmentation risks while building toward a sustainable integrated model
- Model integration behavior. Demonstrate cross-functional collaboration in how the executive team itself adopts and applies AI tools
Action 2 next steps
Rapidly implementing new work models and ways of working across the enterprise presents critical challenges for senior executives. To evolve L&D into a center of expertise that builds capabilities for a competitive edge, we recommend these three steps:
- Address the systemic changes needed when outdated work models collide with new ways of working. Balancing human intelligence and artificial intelligence (HI + AI) in work design should be the key priority
- Integrate continuous change management processes to facilitate a smooth transition for the workforce as you champion the adoption of AI-augmented workflows. Acknowledge the emotional component of this adoption with transparency and ensure psychological safety to encourage genuine experimentation and learning
- Rescope L&D to focus on building enterprise-wide capability academies instead of individual training programs. Enterprise L&D should first focus on the rapidly emerging areas where the business strategy calls for critical talent development needs. L&D operations will be able to access agentic systems to provide augmented performance support in the flow of the work, further removing itself from the need to constantly create task-based training programs and free itself to provide broad-scale talent development through capability academies.
Up next we explore Action 3: Aligning capability building with business strategy to generate measurable competitive advantage, including more detail on re-architecting L&D to modernize and optimize its role across the enterprise.
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.