Bob Pulver and Gordon Ritchie, Principal Consultant and Skills Architect at Skill Collective, discuss the challenges and complexities of skills in the workplace. They explore topics such as skills assessments, skills ownership, skills taxonomy, and the role of AI in skills inference. They also touch on the importance of durable skills and the need for a shift in how skills are evaluated and matched in the hiring process. The conversation explores the challenges and considerations of skills-based hiring, the impact of automation on talent acquisition, and the importance of internal mobility and reskilling. It also delves into the use of assessments in evaluating human skills and the need for responsible AI practices. Bob and Gordon discuss the limitations of AI assessments and the importance of multiple sources of research. The conversation concludes with advice to explore and experiment with AI tools and to embrace continuous learning and adaptation. Keywords skills, assessments, ownership, taxonomy, AI, durable skills, hiring process, skills-based hiring, automation, talent acquisition, internal mobility, reskilling, assessments, responsible AI, research, experimentation, continuous learning Takeaways Skills assessments can help individuals and organizations identify aptitudes and capabilities. Ownership of skills is a challenge, as different HR verticals and solution providers vie to be the system of record for skills. Skills taxonomy and global skill standards are difficult to create and maintain, and may not be the most effective approach. Skills inference using AI can help identify skills based on descriptions and tasks, but it is important to consider the limitations and biases of AI. Durable skills, including critical thinking and problem-solving, are essential in a rapidly changing work environment. Skills-based hiring often falls short, with skills-based sourcing being more common. Inconsistencies in skills evaluation and matching persist in the hiring process. Skills-based hiring may not always lead to better outcomes and behavior. Hiring managers should focus on the tasks that need to be done, rather than just the skills required. Automation should be approached with consideration for the impact on culture, engagement, and retention. Internal mobility and reskilling can provide opportunities for employees and help retain valuable talent. Assessments in the AI space vary in reliability and validity, and caution should be exercised in their use. Continuous learning and experimentation with AI tools can help individuals elevate their AIQ. Sound Bites "Skills assessments can help individuals and organizations identify aptitudes and capabilities." "Skills taxonomy and global skill standards are difficult to create and maintain, and may not be the most effective approach." "Skills inference using AI can help identify skills based on descriptions and tasks, but it is important to consider the limitations and biases of AI." "And so that looks like a great vanity metric to measure the success of skills-based hiring on, but it hasn't actually changed the outcome and behavior." "What is it that needs doing? Because that's the business of the business. And that's what a job architecture needs to mirror or mimic." "You don't automate jobs, you automate tasks." Chapters 00:00 Introduction and Background 09:48 Challenges of Skills Taxonomy and Standards 19:12 Skills Inference with AI 25:19 The Importance of Durable Skills 33:27 Skills-Based Hiring Challenges 35:37 The Limitations of Skills-Based Hiring 39:23 Considering the Impact of Automation on Talent Acquisition 45:22 The Value of Internal Mobility and Reskilling 58:53 The Challenges of Assessments in the AI Space 01:06:07 Embracing Continuous Learning and Experimentation with AI Gordon Ritchie: https://www.linkedin.com/in/gordon-m-ritchie Skill Collective: https://skillcollective.co.uk
