The manager who leaves you shaking your head after every call, wondering how ‘they’ ever gave ‘that guy’ so much responsibility. The colleague who seemed like a do-it-all superstar right up until her last promotion, but now can’t organize her own inbox. The team leader who calls endless meetings, but always seems more than a little confused about the big deliverable.
If you’ve worked in corporate America you’ve probably encountered the ‘Peter Principle’- though perhaps you didn’t realize it.
What is the ‘Peter Principle’?
Named for Canadian sociologist, Dr. Laurence J. Peter, the ‘Peter Principle’ holds that a competent employee will move up in an organization until promoted into a position for which he or she isn’t qualified. And that, while some people do a great job rising to the occasion, the majority will simply struggle along as middling to poor managers, supervisors, and directors.
Let’s look at an example. BigCorp needs a new Regional Sales Director and wants to hire from within. Lee, the best performer in the company’s biggest territory is promoted to the top slot. However, he barely meets his targets for three quarters before moving on under a cloud of disappointment.
What went wrong?
While Dr. Peter presented the Peter Principle as satire, studies have shown that he was on to something. Companies tend to promote the wrong people. Rather than assessing candidates on the skills and traits needed for the open position, HR professionals focus on a candidate’s performance in his or her current role – whether or not the positions have much in common.
In our example above, the attributes that made Lee a great salesman – charm, spontaneity, and a big appetite for socializing – didn’t do much to help him deliver in a role that required spending hours alone analyzing market data and reviewing strategy memos.
Avoid the Peter Principle with AI
Artificial intelligence can identify the best internal candidates and evaluate potential external hires based on the actual requirements for success. Ensuring that you hire the best candidate for the open role.
To revisit our example, what if instead of relying on past performance BigCorp had pre-hire assessment data on every employee? HR would have flagged immediately that Lee’s low score didn’t support moving him up and would have learned why from the reporting.
On paper, Maya, a marketing manager for a company in an unrelated sector, would seem an odd choice. But her AI-based assessment results couldshow she has both the practical know-how and the emotional intelligence required to manage effectively, so the choice to interview her would be clear. Under Maya’s leadership turnover would drop while morale and profits soar.
Why? Because AI identified that she (and not Lee) had the right building blocks for success.
Whether your aim is an internal promotion or to source external talent, AI can help you objectively evaluate which candidates best suit your open role.
Want to guarantee you’re hiring for success? Cangrade’s AI-based Pre-Hire Assessments help ensure you identify the best candidate for the job. Find out more.