White paper
The Defining Role of CLOs in the AI Economy
As the impact of generative AI continues to transform work and accelerate employee productivity worldwide, chief learning officers (CLOs) are in a critical position as change agents, leading the adoption of AI within organizations.
By upskilling workers in high-impact technologies like generative AI, CLOs can reskill some of the 61% of workers predicted to need retraining by 2027.* At the same time, successful skills development can enable companies to tap into a potential $4.4 billion in productivity gains.**
The Defining Role of CLOs in the AI Economy explores how CLOs can help employees grow and organizations become agile and resilient, creating an engaged workforce that achieves business goals.
The white paper is available in English, Spanish, French, German, Arabic, Indonesian, Thai, or Portuguese. See the English preview below.
*The Future of Jobs Report 2023, World Economic Forum, 2023
**The economic potential of generative AI: The next productivity frontier, McKinsey, 2023
In this white paper, you will learn about:
- The leading role of CLOs in driving genAI transformation
- Demand for genAI skills among employees today
- Top genAI skills for your organization
- AI learning innovations to enhance upskilling and reskilling
Surging interest in AI
445% YoY increase
Searches for genAI content among Coursera for Business learners (Q3 2023)
157% YoY increase
GenAI content enrollments among Coursera enterprise learners (Q3 2023)
800+
AI courses on Coursera
Just as electricity transformed industries a century ago, AI is reshaping our global economy. In this rapidly evolving world, leaders have a responsibility to equip the individuals they serve with the necessary AI skills, not only to enhance their employability, but also to empower them to reshape the nature of their work.
Essential generative AI skills for your organization, curated by Andrew Ng
Download the article for the complete skills list
Prompt Engineering: crafting effective input instructions for generative AI models to understand and respond to
Generative AI project lifecycle: the stages involved in developing and managing generative AI projects
Natural Language Processing (NLP): a field of AI focused on enabling computers to understand, interpret, and generate human language
Deep Learning: a subset of machine learning that uses neural networks with multiple layers to represent complex patterns and features in data
Diffusion models: a class of generative models used for tasks like denoising and generating images
Responsible AI: ensuring that AI systems are developed and used ethically, fairly, and transparently
Download the full article to learn more
Every CEO and CFO should be talking to their CLO right now about how to adapt to ChatGPT and generative AI. This new technology will change every industry, every business, and how every employee does their job. At stake is more than $4 trillion of potential productivity gains and the risk of losing to more agile competitors. A well-crafted L&D strategy fosters talent agility, equipping your team to not only meet challenges but also rise to the opportunities created by an AI-first world.