In the war for talent, being able to provide personalized career paths is a clear differentiator for your employer brand. A McKinsey study revealed that a major driver of why people leave their jobs is the lack of career development. Like many aspects of work, career pathing is becoming intelligent. What used to be a time-consuming, intensive task on both sides is being made more efficient and accurate by technology. But what does intelligent mean?
Designing an effective career path begins with understanding an individual’s skills, interests and professional objectives. It is highly tailored to each individual because careers are deeply personal things. People and talent teams can have a significant, lasting impact on an individual’s life through career pathing exercises — so it’s vital to lead them on the best path for their needs and aspirations.
High-quality skill data provides a strong foundation for the 90 percent of companies shifting towards a skill-based workforce. It needs to be complete, accurate, real-time and unbiased. Your AI models and data-driven decisions are only as good as the data ingested in them. Inaccurate data leads to inaccurate results. At scale, this becomes a serious issue as any inaccuracies and biases will quickly spread throughout your workforce decision-making. A data audit can help you assess how accurate and representative your existing data is — from this, you can understand what data sets are missing.
Most organizations will have incomplete, outdated data. Traditional approaches to data collection include lengthy consultative exercises, surveys, and consolidating your HR, recruiting and learning data. In isolation, these don’t give a comprehensive enough picture for your career pathing to be personal and effective.
You need to capture everything happening across your organization as employees go about their daily work. They generate skill signals (indications of their skills) every time they complete a project, share their insights, learn, give and get peer feedback and more. Of course, with such a glut of possible data to collect, it’s little surprise that you’ll need to work to structure and clean the data to make it usable. When this data is refined appropriately, it complements and enriches information in recruitment, learning and HR systems. Better still, you can augment this with public data sources such as market and labor data to understand how your workforce skills compare within your industry.
That’s what’s going to give you the insights needed to offer career opportunities that align with your people’s interests, skills and career goals. This data will also help you understand what organizational capabilities are strong and which are areas for improvement. This is what will prepare your company for future challenges and transformation.
AI makes managing skill data a lot simpler and a lot more scalable because it constantly enhances your existing data sets, plugging skill profiles with missing information, and it can make recommendations based on the available data. For instance, connected skills that someone may have that they haven’t put in their skill profile, or work and learning opportunities that can build new skills.
It also plays a fundamental role in building a skill framework. Understanding your workforce skills is a critical step, according to Josh Bersin, and a bespoke skill framework is how you’re going to achieve this. For it to have a tangible impact on your business and to withstand the test of time, it needs to be actionable, granular (to give you the right insights) and dynamic so it evolves with business and individual needs.
Although this might feel like a big shift, it will be worth it in the long run as companies that actively offer opportunities for career growth, aligned with business needs, are better able to navigate the many changes on the horizon. Or as Deloitte’s “2023 Global Human Capital Trends” report states, “those who partner with workers and experiment with what’s possible will create sustainable work models and elevated outcomes.”
