Competency models can provide a central framework for defining the skills and behaviors essential to an organization’s success. A well-built competency model provides a strong support structure for leadership development, talent, and performance management. A robust competency model outlines the behaviors needed to create the organization’s desired culture.
But how effective is your competency model? Does it clearly and effectively identify the skills and behaviors that enable everyone at all levels to be more effective? Many don’t. Less effective competency models fall into these traps:
- Complexity – different competencies for different levels and different behaviors for the same competency too often confuse and confound.
- Unconnected to results – many nice-to-do competencies don’t reliably predict outcomes like engagement, turnover, profitability, sales, safety, and other key outcomes.
- Vague targets – an effective competency model helps everyone find their development sweet spot at the intersection of his or her passion, organizational need, and competence.
- “Smooshing” – to simplify their models, some organizations are combining several competencies (sometimes positive and negative) to form one competency.
- Missing competencies – some models focus on interpersonal or leadership behaviors and missing critical skills like getting results, technical expertise, or problem solving.
- Improving everything – 3 to 5 relevant, results-oriented competencies can be leveraged to boost performance to the 90th percentile.
These are some of the common pitfalls and traps of competency models that Joe Folkman addresses in this 45 minute webinar 5 Factors in Effective Competency Models.
Joe is a leading expert in psychometrics or measuring psychological factors with nine books he’s authored or co-authored. He’s spent over 30 years working with AT&T, General Motors, General Mills, Wells Fargo, Yale University, and many other global leaders on competencies and leadership development. And he’s a really nice guy! Watch the webinar now.