The case for using SI (smart intelligence) instead of AI (artificial intelligence) is demonstrated in the chart. It summarizes how people get jobs. The first thing that’s clear is that not many people who apply directly to job postings get hired – far less than 1%. The second point that’s less clear, but as important, is that acquaintances get hired more frequently than strangers if they're reasonably strong, even if they don't have all the skills required, because there's less risk and it's more convenient.
Here are some of the big reasons acquaintances get the better deal:
They're seen more often if referred by a trusted person and get to the top of the resume list when referred by anyone in the company.
They're hired more frequently. One survey from Lever ATS indicated that once a referred candidate is interviewed the chance the person will be hired is 20% vs. only 10% for a stranger. This double-whammy increases the chance a referred person will be hired from one in 125 to one in 12!
Their on-the-job performance is more predictable. This doesn't mean the person is a better candidate though. They’re generally good candidates but frequently there are better candidates who applied but there too many other risk factors – temperament, style, attitude, fit and the like – preventing these other people from being hired.
It’s a lot faster to hire referrals and acquaintances. For one thing they’re often contacted before the job requisition is formally approved. Getting someone on board more quickly is a huge advantage.
Over the years in numerous books, a new Lynda.com course, blog posts and presentations to business groups I have made the point that we should hire strangers the same way we hire acquaintances. I developed Performance-based Hiring with this idea in mind. The problem I noticed throughout my executive search career was that top people I personally knew were typically assessed improperly by people who didn’t know them at all. As a result some remarkable people were judged as incompetent and some decent people were judged as remarkable.
Here are some ideas on how to bridge the gap and evaluate strangers and acquaintances exactly the same way:
First, define the job as a series of 5-6 performance objectives. I refer to this as a performance-based job description. A typical performance objective describes the task, the action required and a measurable result. For example, it’s better to say, “Collaborate with the engineering team to develop the product specs for the XYZ product within 60 days,” rather than, “Be responsible for product marketing” or ”Must have 6-8 years of experience, an MBA in marketing and an undergraduate engineering degree”.
Third, eliminate pre-interview screening tests. DISC and PI-like personality tests are not predictive. For one thing they only assess preferences not competencies. While they are somewhat confirming if used too early in the process they screen out passive candidates and all superior candidates who want to explore a situation before getting serious. This puts a lid on quality of hire.
Fourth, proactively control interviewer bias. Lack of job knowledge opens the door to bias, perceptions, bad judgement and intuition to become the deciding factor when interviewing candidates. One of 12 ways to reduce bias is to script the first 30 minutes of the interview. Another way is to use a well-organized panel interview with 2-3 people.
Fourth, assess accomplishments, not skills. Ask all candidates – strangers and acquaintances – to describe in detail an accomplishment that best compares to each of the performance objectives in the performance-based job description. This post (now read by over 1.5 million LinkedIn members) describes this technique.
Fifth, organize the interview and debriefing session. Here’s a simple form you can use to organize the interview around the factors that best predict on-the-job performance. By having each interviewer justify his or her ranking using evidence for each factor you’ll eliminate yes/no gladiator voting which inadvertently glorifies bias and intuition.
AI was introduced into the hiring process to solve a problem that was self-created: Letting people apply to jobs they’re not qualified to handle. Rather than eliminating the problem at the source companies have invested unnecessary resources to weed out the unqualified rather than figure out better ways to attract and assess the most qualified whether they’re strangers or acquaintances. Regardless, the above five steps are a useful short-term workaround to a problem that doesn’t even need to exist.
Imagine it’s January 2020. You get an email from LinkedIn with the following headlines:
You’re not as happy as you could be in your job. (This is based on your recent search history including articles read, shopping patterns, how often you update your LinkedIn Profile, your credit report, health records, the quality of your group discussion and your life satisfaction score compared to your peers.)
The companies you’re looking at won’t make you any happier.
Your career growth is being stymied by too much (something) and too little (something else). (This is based on your rate of change, your documented accomplishments, how you spend your time online and what people are saying about you and what you’re saying on social media, among others.)
Here’s your current individual balance sheet of strengths and weaknesses (like a career credit report) and what you must do in the next 12 months to meet your personal and professional goals.
Here are a few jobs your personal AI advisor suggests you check out. Exploratory phone calls have been arranged and the hiring manager is anxious to chat with you.
Artificial Intelligence is becoming more commonplace, so this type of career email is not that farfetched, especially since recruiters do much of this now manually. However, the dumb way to do it is to use some statistical engine duct-taped to some poorly designed algorithm on top of an assessment test to predict whom to see and hire. Companies are starting to build AI systems like this that, in most cases, will make things worse, not better. For the record, let me suggest a smarter AI approach.
During the holidays I watched Ex Machina describing how AI would take over the world. It seemed pretty realistic although unsettling. A few days later I read an article in Businessweek on how George Hotz developed a driverless car in about 90 days using a combination of expert system modeling and deep learning techniques. This is different and potentially superior to the bottoms up techniques Google and Tesla are using to develop driverless cars.
Collectively this caused me to check out my old copy of Tony Robbins’ 1987 classic, Unlimited Power. In the book Robbins described how he quickly developed a vastly superior training system for the U.S. Army by modeling their best shooters. This shortcut seems parallel to the approach Hotz has taken. Both seem to be examples of using expert systems based on the modeling of best practices rather than starting from scratch. This is where I believe HR should head – modeling how the best people perform, how they get hired and why they get promoted rather than continuing to hire people based on their skills, experience, personality traits and some form of gladiator-based interviewing.
As I’m finishing up this post about using AI for hiring, I get a notification from Google (I think) suggesting I also read this article in the LA Times: Venture capitalist is investing in firms that use computers to improve healthcare. It’s about combining expert systems, deep learning and behavioral patterns to predict healthcare outcomes and treatments. The article itself is about developing AI systems by modeling how the best doctors make decisions.
While this future might sound scary for some job seekers and for those doing the hiring, it actually should be welcomed. How people now go about changing jobs and how companies currently select new hires is even scarier.
Given this, here’s my recommendations on how AI for hiring should be developed:
Start by replacing traditional skills and competency-based job descriptions with performance profiles. These performance-based job descriptions define success as a series of 5-6 performance objectives. Google and Gallup already have proven this is the right way to maximize performance, job satisfaction and retention.
To build more cohesive and stronger teams focus on factors that predict group over individual success. For benchmarking, model how the best coaches and managers build great teams. Note also that coaches get fired for failing. Maybe hiring managers should be, too.
Rather than force fit a person into an ill-defined job, customize the job to better fit the person’s motivating needs. This is what Prof. Todd Rose of Harvard is proving in his new book, The End of Average, as a means to maximize individual and company performance.
Properly executed, artificial intelligence offers great promise for raising the overall talent level of a company while providing people with more intrinsically motivating and satisfying jobs and careers. However, this is an unlikely outcome if HR continues its focus on automating existing and fundamentally flawed hiring processes. Ultimately, that’s the difference between smart and dumb AI regardless of the function. For HR, the first offers great promise, the latter self-generating and unstoppable bureaucracy.