Understanding employees’ competencies and skills in relation to job requirements has always been important in effective talent management. However, as cognitive computing and artificial intelligence are deployed across organizations to achieve speed and scale, the cost of poor decision-making due to weak competency systems is likely to be magnified.
In a recent IBM Smarter Workforce Institute white paper, "Competencies in the Cognitive Era," my team found three examples of how effective cognitive computing relies on strong competency frameworks to improve efficiency in HR: recruitment, career coaching and learning.
A Clear Standard for Candidates
In recruitment, the move to online job applications has led to a greater number of candidates per job than humans can objectively process. However, once the rules humans would use to make decisions are understood, those rules can be coded, automated and executed in a cognitive application with unprecedented speed. Decision quality can be assessed, and results of the analysis can be integrated into algorithms so that the cognitive application learns from the data. Competencies provide the necessary foundation for standardized rules to score candidates against.
A Path Forward for All Performers
In career coaching, because development opportunities are so resource intensive, they are generally offered unevenly, and usually only to high performers. Cognitive applications are designed to address this resource intensity hurdle, where new applications model the shortest paths between where workers are now and where they want to be, using historical data. AI tools then offer workers new opportunities, when competency assessments indicate that the worker has the skills for the new role. The majority of previous workers with similar skill profiles have made successful transitions.
A Personalized Learning Curriculum
Finally, in learning, cognitive curation applications can meet the challenge of conducting training needs analysis across an organization. Cognitive content curation technology can now organize vast sets of learning materials and present these to workers in intuitive ways, according to individualized assessments of a learner’s needs and competency requirements for specific job roles. The content can also be tailored to a worker’s learning preference, and to a schedule that reflects whether the learning is for an immediate application (training) or for some yet to be determined point (development).
A Competent Foundation
None of these three examples can achieve their desired goals without a valid competency framework to work from. Competencies are critical to effectively leveraging AI because practically every artificial intelligence application in HR depends on the standardization and ease of scaling that competencies permit. If you’re a Chief Human Resources Office (CHRO) or HR leader wanting to prepare your business to leverage cognitive computing, there is one essential activity you need to undertake now: audit the quality of your organization’s competency frameworks.
Interested in learning about utilizing skill inventories as a strategy for balancing talent needs? Sign up for the webinar now.
*Cognitive computing apps in HR augment rather than replace human decision making. Cognitive apps will not achieve consciousness or agency. Cognitive apps are transparent in that they clearly indicate when and how AI is being deployed, and they respect ownership of data and intellectual property. IBM Talent Management Solutions provides enablement programs to prepare workers with the skills they need to interact with cognitive apps.
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