Talk of chatbots, natural language processing, machine learning and automation might be exciting, but there are some critical foundations required to ensure the effectiveness of these applications.
That critical foundation is competencies. Many new AI technologies depend on these fundamentals of talent management, and here’s why: AI works like most objective and analytical human minds, but at a scale and speed that humans cannot match. With effective use of AI, HR professionals can be freed to spend time on the high-impact, strategic initiatives that organizations value most. To achieve the necessary speed and scale for impact, many AI applications in HR depend in one way or another on competency frameworks.
As those of you working in HR will know, competencies are taxonomies of knowledge, skills, abilities and other attributes (KSAOs) required for the successful performance of jobs in an organization. They may include expected skills that the worker should have (based on education and experience) as well as personal characteristics of the worker (such as cognitive abilities, traits and interests).
Given the importance of competencies for AI success, here are three ways to get your competency house in order.
Know Which KSAOs Are Most Important by Role
Many organizations are processing thousands of job applications every year. Start with the most critical positions in your organization and ensure that the necessary KSAOs are clearly defined. You may not need to start from scratch since competency frameworks are commercially available, but you’ll need to ensure they’re comprehensive (covering all the roles you need) and updated regularly. Once defined, the KSAOs can be used within an AI application to rank and prioritize candidates ready for a final human decision.
Establish Competency Thresholds for Internal Mobility
Making the most of your organization’s talent means being open to employee desires for career progression and development. Historically this has been a rather hit-or-miss affair, with employees struggling to know what opportunities might be open to them in the organization. By combining models of employment patterns with competency data, AI solutions can improve internal talent mobility by suggesting opportunities that are suitable for individual workers.
Use Competencies to Laser-Guide Learning
AI solutions can offer a much more personalized and tailored learning experience. With more insights into what an employee needs in order to progress, AI solutions are able to recommend the right training at the right time. To be able to generate those insights and tailor content to skills gaps and individual learning preferences, AI technologies need competency data. An AI solution informed by KSAOs will ensure the education is both appropriate and highly relevant.
Whether you’re considering AI adoption in the area of talent acquisition, talent mobility or learning, competencies are critical. A competency audit is probably the most important first step in AI adoption and will set you up for long-term success.
This content was provided by one of our UNLEASH sponsors. Learn more about competencies in the cognitive era and see what IBM Watson Talent Frameworks can do for your business.