Summary Notes: Beyond the Hype, Making the Skills-Based Organisation Real with a Little Help from AI

On 16th July, CRF hosted an online session for its Talent, Leadership & Learning (TLL) community exploring the evolving intersection of skills, AI and technology. Catalina Schveninger, HR consultant and former Chief People Officer, shared insights on skills-based organisations, the implications of generative AI and the shifting role of HR and L&D. Key highlights from the session are summarised below.

Rethinking Skills-Based Organisations

• Skills-based organisations have been a key aspiration for many HR teams, but progress has often fallen short of expectations. The traditional promise – that mapping, measuring and developing the right skills would unlock productivity – hasn’t fully materialised.
• The limitation lies in focusing too narrowly on skills; tasks matter just as much. Without anchoring skills in the real work people do, skills based organisations risk becoming little more than expensive, underused frameworks.
• AI tools such as ChatGPT can now help deconstruct work into both tasks and skills, based on real interactions.

AI: Potential and Pitfalls

• The promise of AI is significant, but so are the risks. A study from Harvard Business School and BCG found that AI helped professionals complete simple and moderately complex tasks faster and better. However, on more complex or strategic tasks, performance declined when AI was introduced. This is due to the nature of GenAI tools, which predict patterns rather than understand context.
• There’s a broader concern that AI may erode deep thinking, just as GPS has reduced our ability to navigate independently.
• Slack’s Workforce Index found that 48% of desk workers would hide their use of AI to avoid appearing lazy or replaceable. This was especially pronounced among women.
• A great deal of hype surrounds so-called “agentic AI,” or autonomous AI assistants. However, according to Gartner, out of 1,000 claimed “AI agents,” only 130 were real – the rest were basic automation tools with new labels. Failure rates also remain high.
• It’s time to move beyond “AI-enhanced” and begin designing AI-first organisations, where AI is embedded into workflows, job design and decision-making from the outset.

Skills and AI: Practical Applications

• There has been a noticeable shift in demand within technical roles. While traditional software engineering skills have seen a dip, AI-related engineering roles are growing and command an 18% salary premium.
• In fast-evolving fields like engineering, skills frameworks need to be granular and mapped to career levels. Overly broad competency frameworks don’t work – technical roles demand precision.
• While self-assessment models often fail, observed work (either by managers or AI) can be used to infer skills. However, even with the best tools and data, you can’t force people to grow or take on new roles. Managers also play a gatekeeping role and career mobility will stall if they are not willing to release talent.
• Participants on the call shared real-world applications, including using AI to help people navigate HR independently (freeing up HR from time-consuming queries), identifying underutilised skills, nudging people toward career development and working with third-party vendors offering built-in skills assessments.

L&D’s New Role in an AI-Driven World

• AI is fundamentally changing the role of the learning and development function. The focus is shifting from delivery and facilitation to tasks such as shifting mindsets, driving adoption of new technologies and measuring ROI.
• Skills governance must also evolve. The most effective approach brings together HR or L&D (to provide the frameworks, systems and templates), Subject Matter Experts (to ensure the right skills are included and updated), and the business (as the overall owner). SMEs must be supported to contribute, which means giving them time, recognising their expertise and embedding this work into their roles.
• Focusing effort on ‘soft’ or power skills can be a useful place to start, alongside prioritising the handful of technical or functional skills that are most critical to your business.
• As AI changes early-career pathways and automates routine entry-level tasks, organisations are also shifting to longer and more structured onboarding, such as multi-month programmes that combine online learning, shadowing and project work.

Resources
CRF. On Demand Learning Course: Integrated Talent Management
CRF. 2020. Careers, Development and Succession in a Changing Landscape
CRF. 2024. Reskilling for Sustainable Growth