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The gap between the supply and demand for generative AI-literate employees can be attributed to several factors: ### **Reasons for the Gap** 1. **Rapid Advancement of Technology**: Generative AI has evolved at a breakneck pace, and many education systems and training programs haven't kept up with the speed of change. 2. **Specialized Knowledge Requirements**: Generative AI involves complex concepts such as neural networks, prompt engineering, large language models (LLMs), and domain-specific adaptations, which require a strong foundation in mathematics, programming, and machine learning. 3. **Limited Expertise Pool**: The field of AI is relatively new, and there are fewer professionals with advanced expertise in generative AI as compared to traditional software development or data science roles. 4. **High Demand Across Industries**: As more industries recognize the transformative potential of generative AI, demand for these skills has skyrocketed, leading to competition for the limited available talent. 5. **Education Lag**: Academic programs and certifications often take time to develop and adapt, meaning there are fewer graduates with direct generative AI training. --- ### **How Organizations Can Address This Gap** 1. **Invest in Upskilling Current Employees**: - **Workshops and Bootcamps**: Conduct intensive training programs focused on generative AI tools, technologies, and practical applications. - **Online Learning Platforms**: Encourage employees to complete courses on platforms like Coursera, Udemy, and edX, which offer specialized AI tracks. - **Internal Mentorship**: Create mentorship programs where experienced AI professionals within the organization can train less experienced staff. 2. **Foster a Learning Culture**: - Encourage experimentation with generative AI tools like ChatGPT, DALL·E, or MidJourney for day-to-day tasks to build familiarity. - Provide incentives for employees to innovate and explore AI applications relevant to their roles. 3. **Partner with Academic Institutions**: Collaborate with universities and research institutions to offer customized training programs or internships that align with organizational needs. 4. **Leverage No-Code and Low-Code Platforms**: Provide employees with access to user-friendly AI tools that don’t require deep technical expertise, allowing non-technical staff to integrate generative AI into their work. 5. **Cross-Disciplinary Training**: Since generative AI intersects with various fields, encourage employees from diverse backgrounds (e.g., marketing, HR, and design) to understand how generative AI can apply to their domains. 6. **Build AI Awareness at All Levels**: Offer high-level sessions for leadership and strategic teams to understand the potential and limitations of generative AI, enabling better decision-making and strategic alignment. By adopting a multifaceted approach, organizations can close the skills gap and build a workforce capable of leveraging the full potential of generative AI.

Had good foundational concepts about AI, but I think too many conversational videos with IBM engineers presenting their personal opinions about the future of AI. I found most of them to be overly opiniated with elementary analogies and some even with almost no relevance or value to actual learning about AI. For example "comparing AI > to the > Horse & Buggy" come on really, how about using the "Computer" with modern relevance? Highly recommend replacing one or two of these all-talk videos with some real cool and interesting use case examples showing AI in action. A practical and applied presentation in real world scenarios like technical medicine, industrial production, scientific experiment, etc.

a bit too much marketing, filled with ibm motherhood about productivity, efficiency, and improved customer service. Some technical descriptions failed to properly differentiate between traditional application development and AI application development. Also much of the youve got to have ai misses the point that business use applications and processes to improve performance, these can be implemented manually, traditionally programmed, purchased off the self, or developed with AI. It is the process improvement that matters, alternative implementations need to be evaluated. AI can help with some, not others, but you never choose AI just because it is AI.

My expectations for this cource were higher to be honest. The last asignment for both IBM Cloud and Watson Studio wasn't so clear and informative, but very dry and just "do this and press here...". There was no a signle explanation of how we use these tools and why, and how exactly these work, and which other tools we can use and how. For sure I will continue with the second part, because I'm expecting to learn more. In the other hand the speakers were using clear and simple language and made all the information shared easily understood.