โญ๏ธ AI in L&D has been dominated by gimmicky ideas and surface-level conversations.
๐ Organizational context is crucial for using AI in learning effectively.
๐ Understanding the wider environment, including culture and motivation, is important in AI implementation.
๐ค Many organizations are not ready for rapid AI adoption due to privacy concerns, risk aversion, and lack of infrastructure.
๐ก The use of AI in learning requires considering the organizational context and integration with existing procedures.
๐ The inconsistency of AI answers and the potential legal exposure pose challenges for organizations.
๐ Adopting AI in learning requires changes in workflows and the development of prompt-writing skills.
๐ AI adoption in learning is often driven by the business and depends on AI strategies at the organizational level.
โฉ Only a few organizations are actively piloting and deploying AI in learning, with most still in the experimentation phase.
โ๏ธ AI should be used purposefully to solve real learning problems, not just for the sake of using it.
๐ AI is not just a gimmick but has true business value when there is a direct correlation to performance.
๐ก Adopting AI in workflows is essential for tech companies to stay competitive and work at a faster pace.
๐ง Motivation and relevance are crucial for effective learning and AI cannot replace the human element.
โ Clear guidance and encouragement from the top is essential for successful adoption of AI technology.
๐ Ethical considerations in the use of AI include privacy, bias, and overall ethical questions.
๐ผ AI can provide insights and opportunities for career development.
๐ก Using AI tools like chat GPT can help in preparing for performance reviews by identifying skills gaps and alternative pathways.
๐ฎ The current use of AI in L&D is beneficial, but the focus should be on future advancements and ethical considerations.
๐ค Algorithm aversion is a challenge in human-AI collaboration, where people tend to discount AI recommendations, even if they outperform humans.
โ๏ธ AI should be viewed as a tool, and careful thought should be given to how it is used in the learning industry.
๐ฅ Understanding the perceptions and hesitations of employees towards using AI is a crucial starting point for effective implementation.
๐ AI can be used to experiment and test things at scale, allowing organizations to generate content and see if it works with people.
๐งฉ Using AI can help organizations test faster, build up evidence, and create a bank of knowledge.
๐ก AI is not a silver bullet and should be implemented strategically as part of organizational culture.
๐ค AI in L&D has different levels of application: content production, learning design, skills intelligence, and business intelligence.
๐ฎ The future of AI in L&D is moving from reactive to anticipatory, which raises ethical concerns and the need for user-centricity.
๐ก Being problem-solving focused rather than tech-focused is essential when using AI in L&D to support user development and performance.