If your firm wants to be dominant, it should follow the lead of the world’s most successful firms. So corporate leaders should take note that each of the CEO’s of the five most valuable firms (Apple, Google, Microsoft, Facebook and Amazon) have all publicly declared that artificial intelligence will be the foundation of their future business success and for all other firms that expect to even remain competitive in their industry. It’s not much of a stretch to assert that every medium size or larger firm will also have to develop advanced capabilities in artificial intelligence and its advanced cousin machine learning. In fact, Amazon’s Jeff Bezos recently stated that “There’s no institution in the world that cannot be improved with Machine Learning.”
WOW … Even Apple Struggles With AI Recruiting
Unfortunately, AI and ML engineers and computer scientists are both scarce and extremely picky about where they work. As a result, most firms that rely on traditional recruiting practices will simply not be able to meet their AI hiring goals. To illustrate this point, even the most valuable firm in the world, Apple with its top-five rank employer brand image is currently having difficulty recruiting top AI talent. In fact, in order to let potential recruits see some of the exciting machine-learning work that is occurring at Apple, it recently had to alter its incredibly secretive culture and to offer a publicly visible Apple Machine Learning Journal.
Unfortunately, Apple is not alone in its recruiting struggles. Because, even though, artificial intelligence is No. 2 on the list of the most desirable skills, I find that no more than a handful of corporations have a specialized recruiting sub-process that is designed specifically to attract AI and ML talent successfully.
Tips for Successfully Recruiting Top AI and ML Talent
If you expect to successfully land even a single top AI expert, you will need a targeted and data-driven recruiting effort. The top eight most effective components that I recommend to be included in an AI focused recruiting effort are:
- Develop an AI focused recruiting sub-process — almost all experienced AI/ML talent already has a job. So that means that a standard recruiting process that is designed to primarily attract “active job seekers” simply won’t work on them. And because AI candidates have multiple job options, the recruiting process must emphasize the all-important selling component. And finally, only recruiters who are strongly versed in the field of AI should be allowed to recruit for jobs in this area.
- Learn the best ways to identify top AI and ML talent – because most talent in this area has specialized degrees, finding them is actually relatively easy. By far the most effective sourcing approach is seeking employee referrals from your technical employees who are already involved in the area. Because of the shortage of experienced talent, most hires come from University computer science programs. If you want to find the best, ask computer science professors and their grad assistants to be referral sources. And because most of the talent in the area has a strong interest in the academic approach, you can often also find the best through their publications in academic journals.
- A marketing research approach is needed to identify their attraction factors — once a top prospect is identified, you won’t be able to convince them to apply unless you fully understand each of their needs and expectations. Start by identifying the “general attraction factors” of AI talent. You can identify those general factors by surveying your own AI employees and past AI candidates. But since every recruiting prospect is different, you must then, after building a trust relationship over time, ask each individual prospect to identify their own personal “dream job criteria.” And also what they would need to see before they will even come in for an interview.
- Poaching from their current firm is required — because the best in AI is almost always working at another firm, you need a proactive poaching strategy and process for drawing them away to your firm. Obviously, the trust building, relationship-building, and selling components of your recruiting process have to be extremely effective because your targets already have a job and they are probably treated pretty well. In addition, successfully recruiting them is more difficult because their current boss is likely to counter offer and fight extremely hard to keep them.
- You need an “evergreen” continuous sourcing approach — because of the high demand, AI talent is seldom on the job market. So you need a recruiting process that can react immediately when a target becomes available. The best strategy is a sourcing pipeline approach. Which continually seeks out and builds relationships with target AI prospects, whether your firm has a current job opening or not. And when a target is ready, you need an evergreen requisition, which allows you to hire them immediately whether there is a current job opening or not.
- The timing of the recruiting is critical — because your employed recruiting targets are likely to be well treated, they are unlikely to leave their current firm unless they experience a negative event at their company. That requires recruiters to be constantly on the lookout for negative events at their current firms which may cause them to immediately question whether they want to stay. Some of those negative events can include project cancellation, reduced budgets, hiring freezes, layoffs, a key leader leaving, a merger with another firm, or when they are required to physically relocate.
- Do most candidate assessment outside of the interview process — AI professionals seldom like being interviewed by individuals who are not experts in AI. They also often have large egos. So when possible, do candidate assessment outside of the normal interview process by checking their work, their Internet visibility, and their references.
- A “candidate centric” selling approach — rather than holding formal interviews, instead, try to use informal two way “professional conversation” with them. A “candidate centric selling approach” asks them specifically what information they need and who they need to meet with before they can say yes. Interview scheduling can’t be arrogant, and instead, it may need to fit their availability outside of work hours. Bring in senior executives to help sell them. Perhaps reach out and influence their family and their references in order to improve your chances of a yes to your offer. And finally, actually change the job itself so that their actual job better fits what they want to do next.
- And don’t forget employee retention — after a successful hire, the first step in retaining them should be to welcome them with a spectacular onboarding process. After that, realize that recruiters from other firms will be continually trying to recruit your own AI experts. So continually communicate with them to ensure that they are satisfied. One of the best ways to do that is to periodically hold “why do you stay?” interviews with them to ensure that you are continually reinforcing and adding to their reasons for staying.
The Difference Between AI and Machine Learning
If you’re not familiar with the difference between the two approaches … under the older artificial intelligence approach, you teach a machine what humans know and how we do things. Under machine learning, you simply program the machine how to think and learn like humans, and then with access to big data, the machine will continually figure out on its own the best way to make optimized decisions. This latter approach is more advanced, so it makes sense to focus your recruiting on those who specialize in machine learning.
The Recruiting Function Needs Machine Learning Capabilities Too
BTW, like every other business function, the recruiting function must also adopt machine learning technologies to its own processes. So as part of our overall recruiting strategies, also look for data scientists who are also interested in applying their knowledge to recruiting.
Final Thoughts
There is no doubt that artificial intelligence, and to a lesser extent machine learning, are extremely hot topics among business leaders today. But they are not “a fad.” Instead, they are literally the future foundation of business operations because in many cases these programs provide results that are superior to those provided by humans. They cheaply and rapidly allow for continuous improvement by identifying and understanding cause-and-effect relationships and the complex interrelationships between business factors that no human could ever spot. Unfortunately, these programs can’t develop themselves, so failing to recruit top AI talent becomes the prime limiting factor.
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