Learning and development in the corporate world stands at a crossroads. Traditional approaches, characterised by standardised content, passive consumption, and potentially outdated legacy systems, are insufficient for today’s complex workplace demands. Meanwhile, artificial intelligence offers unprecedented capabilities to transform how information flows within organisations.
The Limitations of Traditional Corporate Learning
Corporate learning has historically followed a classroom-style paradigm. Employees sit through mandatory training sessions or click through e-learning modules with little consideration for their specific needs or how the information connects to their actual work. This approach suffers from several fundamental flaws:
Firstly, traditional methods constrained to a point in time and space are rarely connect learning to practical application, resulting in Ebbinghaus’ “knowledge transfer problem” where learning content is quickly forgotten when not immediately applied (Note, there are reliability and validity critiques of Ebbinghaus’ research, which I won’t explore here, but a good overview of learning and forgetting is here).
Secondly, standardised content fails to address the diverse learning needs and prior knowledge or context of different employees with different perspectives, potentially in different geographies or with different capabilities and languages.
Thirdly, conventional learning experiences emphasise objectivist rather than constructivist learning, emphasising procedural knowledge rather than developing critical thinking and problem-solving capabilities. Conceptually and historically, we treat people like empty vessels to be filled with information, or as machines that follow a process or algorithm, rather than entities with agency and judgement. A useful review on these concepts is here.
The key critique of traditional corporate learning is that it is often disconnected from the workflow itself, creating artificial boundaries between learning activities and actual work. For example, knowledge workers might spend their day in various Microsoft or Google productivity tools, and would need to move to a separate learning management system to access corporate training. Employees must interrupt their tasks to engage with learning platforms, creating cognitive work and friction that hinders adoption and effectiveness of interventions.
Time poverty is a critical consideration in modern workplace learning. In an age of constant digital connectivity and increasing workplace demands, employees are already stretched thin with expectations of immediate responsiveness and mounting workloads. Adding more mandatory training sessions or content consumption requirements only exacerbates this stress. Learning solutions must respect the limited time and cognitive bandwidth of modern workers, focusing on seamless integration with existing workflows rather than creating additional time burdens. The goal should be to enhance productivity and capability without contributing to workplace overwhelm.
The Promise of AI-Enabled Learning
Artificial intelligence introduces capabilities that can fundamentally reshape learning experiences:
- Personalisation at scale: AI can analyse individual performance, preferences, and career trajectories to create adaptive learning pathways for each employee. Rather than one-size-fits-all programmes, AI enables tailored experiences that meet employees where they are and guide them toward where they need to be. Although this is a hypothetical use case, corporate learning systems and departments are not typically able to leverage the granularity of data required to personalise. The extent of personalisation depends heavily on the availability of personal data.
- Workflow and agentic automation: Perhaps most transformatively, AI agents can deliver information at the moment of need, directly within work processes, responding to triggers and queries from the workforce and from company systems in a manner that is highly contextualised. There are significant movements in democratising automation, from established platforms such as Power Automate by Microsoft, to emerging platforms like Make and n8n, companies can structure their automation workflows including agents and assistants. Recent developments from OpenAI’s Operator and Manus demonstrate the capability of next generation AI technology stacks to take action directly in browser and operating system environments allowing agentic task completion. Further complex functionality is also possible with the emergence of Model Context Protocol (MCP) as a mechanism for standardising the connection between assistants and data, and Agentspace by Google. As well as enabling employees to seek out information, these systems can proactively provide relevant knowledge, instructions, or decision support precisely when needed. Your organisation’s governance and risk systems should be considered when selecting tools.
“Learning in the flow of work” has been a compelling vision in L&D for over a decade, promising seamless integration of learning experiences into daily work activities. However, traditional technologies have fallen short of delivering on this promise, often creating more friction than flow. AI (from agentic workflows to retrieval augmented generation) represents the first real opportunity to achieve this vision through intelligent, context-aware systems that can truly understand when, where, and how to deliver information in a multi-modal delivery manner without disrupting workflow. We’re moving from aspirational concept to practical reality.
The Implementation Challenge
Despite these promising capabilities, organisations face significant challenges in implementing AI-powered learning effectively:
- Building and maintaining agentic workflows and retrieval augmented generation systems requires specialised expertise and continuous refinement, placing a burden on organisations to maintain and evolve software systems. Ensuring the accuracy of AI-generated responses demands robust quality assurance processes. Organisations must also navigate change management complexities as employees and L&D professionals adapt to new ways of designing and experiencing learning.
- Legacy content and LMS platforms often aren’t designed to work with emerging AI capabilities (although many providers have been very agile in adding AI functions to their systems). This creates a gap between technological potential and practical implementation that organisations must bridge through thoughtful planning and investment.
- As AI is an emerging disruptive space, it is often difficult in an organisational setting to know who is implementing or leading AI, as multiple departments or disciplines rush to be the first to implement innovations.
The Path Forward
To successfully integrate AI into corporate learning, organisations should:
- Start with clearly defined use cases that address specific learning challenges rather than implementing AI for its own sake (e.g. specific workflows, or specific retrieval use cases).
- Understand existing technology systems (e.g. your LMS or authoring tools) and the AI functionality that is offered (e.g. virtual assistants, workflows, or data capabilities)
- Focus on quality data as the foundation for AI systems, remembering that even the most sophisticated AI can’t compensate for poor underlying information (e.g. understand the data that is being used for your use case to make it contextually relevant)
- Take an iterative approach, beginning with pilot programmes that demonstrate value before scaling more broadly
- Invest in building AI and data literacy among relevant professionals, equipping them to design with AI capabilities in mind (this could be L&D teams, curating data for retrieval or agentic workflows, working with IT or technology teams to develop agentic workflows and associated databases)
- Develop thoughtful governance frameworks that maintain human oversight whilst leveraging AI’s capabilities (acting within the risk appetites of your business)
Learning in the AI Era
The integration of artificial intelligence into corporate learning represents much more than technological advancement: it’s an opportunity to fundamentally rethink how knowledge flows through organisations. By breaking from traditional paradigms and embracing AI’s capabilities, organisations can create learning experiences that are more personalised, effective, and seamlessly integrated into the flow of work, and possibly most importantly, learner-centric.
The future of corporate learning isn’t about replacing human expertise with artificial intelligence but about creating intelligent systems that amplify the value of human capabilities, acting as a force multiplier for businesses to leverage their competitive advantage in the market more effectively, and, making information more accessible precisely where and when it’s needed.
To learn more about being part of the AI revolution in corporate learning and development, and to discuss your AI in Learning Strategy, contact us.