Researchers from MIT Sloan and Cornell University have found that when people encounter doubts about data and the bias around it, chatting with a large language model reduces trust issues by about 20%.  I found this to be a fascinating study that shows that the cause of data bias issues (AI, in this case) can also be a solution.  This is why developing future versions of jobs and skills is critical, especially for rapidly evolving enterprises.

I was discussing with some HR leaders whether the workforce can be categorized into AI Consumers, AI Builders, and AI Enablers

Looking at the workforce through the lens of AI Consumers, AI Builders, and AI Enablers provides a structured way to understand how different roles engage with AI technologies.

• AI Consumers are individuals who use AI-powered tools to enhance productivity. Skills required include data literacy, proficiency in using AI-driven platforms (like CRM or HR systems, All workflow systems, SaaS applications), and the ability to interpret AI outputs effectively.
• AI Builders are responsible for developing AI systems and solutions. These roles require deep technical expertise, including skills in machine learning, data science, software development, and AI algorithm design.
• AI Infrastructure and Implementers are responsible for optimally scaling and optimizing the AI infrastructure  
• AI Enablers support and integrate AI into the broader organizational structure. Their skills include understanding AI governance, policy-making, change management, and the ability to bridge technical teams with business operations.

Across these layers, some of the skills are becoming more important.

Responsible AI, specifically for generative AI: This skill focuses on ensuring AI systems, particularly generative AI, are designed and implemented ethically and safely, with considerations for bias, fairness, and transparency.

API Utilization, Optimization, & Security: This involves efficiently using and securing APIs (Application Programming Interfaces) to connect systems, ensuring smooth communication between AI models and applications while optimizing performance and protecting data.

Cost Optimization: This skill emphasizes managing the financial efficiency of AI deployments, ensuring resources are used effectively to minimize costs while maintaining the optimal performance of AI systems.

Model Deployment Management: This skill involves overseeing deploying AI models into production environments, ensuring they are integrated smoothly into existing systems and function effectively in real-world applications. It also includes monitoring performance post-deployment.

Draup has introduced a feature called Skills Architecture, which allows you to easily see how peers are approaching various occupations, the skills and technology stacks they’re utilizing, and the soft skills they prioritize.

Here is a snapshot of the Skills architecture of the Software and Mathematics Occupation and the AI Job family.  You can select from a rich data corpus of over 1 Million companies across the Globe.  (The Procter and Gamble is shown as an example).  You can see Roles, Skills, Tech Stack, Soft Skills, Sample Titles, etc.  We will also add the workloads to this.  Kindly experiment with this and let us know your feedback.  You can select any Occupation and the Job family.

Summary:  Draup offers valuable datasets to help enterprises prepare for future skills needs.