In this two-part article series I’ll discuss a leading-edge hiring tool that can have a major impact on hiring: artificial intelligence. In Part One, I’ll cover the basics. In Part Two, I’ll discuss how AI was used to solve a client problem. What are artificial intelligence (AI) programs? Artificial intelligence (AI) programs are special types of computer programs that mimic the function of the human brain. Without going into too many technical details, AI excels at finding patterns among information. How are AI programs now used? AI is a relatively new field and its uses are still being discovered. At present, AI applications include predicting stock market movements, identifying credit card fraud, evaluating loan applications, planning airline schedules, diagnosing cancer, day trading, sales forecasting, financial forecasting, product design, fault tracing, and quality control, among others. Most AI applications work like this:
- AI finds hidden patterns associated with performance.
- The patterns are stored.
- the stored patterns are used to make predictions.
Why be concerned? Hiring research consistently shows the “resume, interview, and background-check” process is about 50/50 accurate. In other words, once a recruiter has “screened out” blatantly unqualified people, he or she would be just as correct flipping a coin for the rest. This practice has some major consequences:
- The cost of recruiting, training, and developing employees drains cash reserves.
- Organizational training programs that were intended to enhance employee skills, not repair hiring mistakes, waste money.
- A manager’s major responsibility is production, coaching weak employees to perform hampers productivity.
- Financial differences in personal productivity are astonishingly high. They are estimated to be 19% of average salary for semi-skilled workers, 32% for skilled workers and 48% for managers/professionals. Let’s translate these percentages into dollars. If you have twenty $60,000/year managers, a 48% productivity difference would amount to an estimated $576,000 per year; twenty skilled workers paid an average of $40,000 would be $152,000; and, twenty semi-skilled workers at $50,000 would be $320,000 annually.
Hiring professionals who want to compute their own differences in productivity can use the following chart:
|Position||Number of People||x Average Salary||x Productivity Difference||= Position Totals|
How can AI be used to improve hiring? To answer that question, we need to break down hiring into two parts: 1) applicant information, 2) performance data. For example: Part 1. Applicant Information Organizations typically use a wide range of hiring tools to decide whether an applicant is qualified for the job, such as:
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- Application forms
- Personal interests
- Amount of education
- Job experience
- Reference checks
- Conscientious tests
- Biographical data
- Assessment centers
- Traditional interviews (unstructured)
- Integrity tests
- Job tryouts
- Prior job knowledge
- Structured interviews (behavioral or situational)
- Mental ability tests
- Work samples
Part 2. Performance Data Organizations usually have a wide range of performance measures that indicate how well their current employees are doing, like:
- Personal productivity
- Job accuracy
- Customer service
- Problem solving
- Project planning
Our objective using AI is discovering patterns in the hiring data that predict turnover. We begin by collecting actual turnover data from current employees. That becomes our “target.” Then, we collect significant pre-hire information gathered from interviews, application forms, tests, etc. We examine all this data using some AI algorithms. When we find the pattern associated with employee turnover, we store it. Whenever a new candidate applies for a job, we use the stored pattern to predict the probability the applicant will quit prematurely. We could do the same with any other measures of performance such as sales, cold calling, job accuracy, personal productivity, integrity, customer service, problem solving, project planning, or any other measure an organization considered important. What if there are no patterns? If there were no patterns associated with success or failure, then we would quickly learn that hiring data had nothing to do with job performance. This would start an investigation of other factors that might affect performance such as economic conditions, management practices, or the quality of data used in the analysis. We might even want to replace low-accuracy hiring tools with high-accuracy ones. What does an AI prediction look like? AI predicts the probability an applicant will fall into one of several groups. If, for example, we built an AI algorithm that predicted “High,” “Medium,” or “Low” performance, AI would predict an applicant’s “probability” as something like “10% High, 35% Medium, 55% Low”. Should AI be applied at the “job level” or the “organization level”? You conduct a “job-level” study when you want to predict job-specific performance such as sales or problem-solving ability. You analyze data at the organization-level if you are looking for generic performance factors like turnover, teamwork or customer service attitudes. Can AI be used with resumes? You can use AI with any kind of applicant data; however, AI predictions are only as accurate as the data they use. The universal rule of “garbage in, garbage out” applies to any analytical technique. Resumes are among the best examples of mixed data ? some good and some garbage. Resumes, for example, often include verifiable names, dates, and duration of employment and education received; however, they seldom contain reliable data about an applicant’s ability to do a specific job. Using all the information from a resume would mix “garbage” data with “good” data ? something that should be avoided. The more accurate and more predictive the hiring tool, the better AI will be at predicting performance. Benefits of Using AI as a Hiring Tool There are several major benefits from using AI in the hiring process. It allows you to:
- Reduce long application forms and processes to a few critical questions
- Use a whole-person approach to predict performance
- Identify both effective and ineffective hiring tools
- Base predictions on current employee performance
- Conforms with the intent of the “Uniform Guidelines” for conducting validity studies
- Reduce hiring mistakes
- Reduce hiring manager guesswork
- Give important information more weight in the hiring decision
- Improve overall individual productivity
- Reduce training time and training expense
- Reduce manager “coaching for improvement
In Part Two, we’ll discuss in detail an example of how AI was used to improve hiring quality in an organization that supplies Medical Technician services.