It’s coming, whether we like it or not. Look at the topics and exhibitors at any HR conference. It’s coming.
Artificial intelligence has the potential to relieve overburdened HR teams from decisions that can be made more easily and more scientifically through decision science.
When you think about filling a job, you are solving an equation. On one side are the requirements of the job. On the other side are the skills, knowledge and traits of the employees who fill or may fill the job. The decision to hire is a gap analysis between the two points – demand and supply.
That’s what technology does to enable more qualified candidates for jobs; matches the need to the supply of candidates. It works in a similar manner to provide employee directed growth and learning opportunities as well.
But, before we can use AI to make strategic decisions we must ensure that the data is accurate. Bad data wastes time and may lead to poor decisions. Technology doesn’t really know the data is bad; it solves for the match you asked it to solve. Now is the time to make sure that future technology has credible, timely and accurate data so that it really can help make good decisions.
The ‘job’ side of the equation
Take a collective look at the information about jobs that the HR team maintains. By information, I mean the knowledge, skills and abilities that have been defined. Review job descriptions, job postings, competencies used in performance management and learning paths.
By collective look, I mean working together as an HR team to review all of the job data, produced by each team, for each purpose. Ask these questions:
- Do the job requirements match across all HR platforms? If not, should they?
- Do the job requirements actually describe the skills, knowledge and abilities specifically enough, or have the jobs been clustered and combined for simplicity so that they are too generic to really explain the job?”
- Is the data accurate? How do you know, and who is responsible for timely maintenance?
The ’employee’ side of the equation
Employee and manager self-service takes a huge administrative burden off HR, but places it in the hands of people who don’t understand the consequences of inaccurate and untimely data. Now is the time to educate those who enter the data on the importance of accuracy, and to implement audit trails to catch and resolve outliers.
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Employee and manager self-service offers a menu of choices, allowing a data field to be completed with predefined data. Are those choices clear and well-defined? Job codes are a key element of matching an employee to a job; are managers too hurried to use the right code?
Termination codes are important to understand retention efforts. Do you offer so many choices that the hurried manager gives up and grabs the first code that seems to fit?
Now is also a great time to make sure that the data codes you use are current, and exactly what you need. If you put 100 codes out there just in case you might need them, that’s 99 possible errors a manager can make.
Conferences and vendors paint a rosy picture of capabilities we haven’t even thought of. Let’s be ready.
This originally appeared on Carol Anderson’s blog @the intersection of learning & performance.