Marketing AI Today: 3 Links in 3 Minutes (Feb. 9, 2018)
At the Marketing AI Institute, we read dozens of articles on artificial intelligence every week to uncover the most valuable ones for our subscribers (become a subscriber today), and we curate them for you here. We call it 3 Links in 3 Minutes. Enjoy!
1. Robotic Recruiters
Forbes writes that recruiting new employees is one of the biggest difficulties businesses face today. Hiring can be a very expensive process for every company, but it gets even more costly if the new hire is the wrong fit. However, as with many other aspects of business, artificial intelligence solutions are beginning to improve the efficiency and effectiveness of recruiting.
Several AI tools have been created to lend a helping hand at the top of the funnel processes. Meeting scheduler X.ai uses candidate and employer data to schedule interviews, Clearfit sorts through hundreds of applications to find the most qualified candidates, and Filtered uses coding challenges to weed out engineers.
While these quick-win solutions do save time, the bigger problem businesses are trying to solve is efficiency. To combat the massive amounts of candidate data that goes unused once a job posting is closed, AI solution Wade & Wendy is designed to be your personal AI hiring assistant, keeping track of every touchpoint candidates have with your business and intelligently resurfacing candidates when applicable.
Other AI startups such as Textio and Engage Talent are leveraging public data to serve relevant job postings to qualified candidates at the right time and on the right platform. Harver tests applicants on the skills they’ll have to perform in desired roles and Ansaro is building predictive candidate models from data collected across hundreds of companies in an effort to hire smarter.
Since recruiting is largely humanistic, it will probably always require some involvement from the human resources team. Still, it is evident that artificial intelligence can be used to interpret data and guide smarter recruiting.
2. Good Data, Bad Data
By now, we are all aware that in order for artificial intelligence and machine learning to operate, they require mounds of data. What is not as well known is that the cleanliness of the data plays a big part in the equation too. While many organizations may be sitting on enormous piles of data, their ability to use this data as a competitive advantage is up for debate, according to Entrepreneur.
Machine learning engineer Sergey Zelvenskiy explains:
"The data that companies have may not necessarily be bad, it is just likely incomplete to solve the problem. There is a chicken and egg problem here. The original system is usually built to collect the data needed for human-driven solutions and moving it to an AI-driven solution might require filling of the gaps. While a human can quickly assess these and fix the problem, the automated system needs automated ways to wrangle the data."
For businesses looking to leverage their data, there are a few tips to keep in mind while mining data.
First, start with the product. In order to collect good data, products should be designed with the right incentives for users to contribute their data, such as vocalizing a question or concern with Alexa.
Next, businesses need to consider the type of data required to bring their product to life. Targeting the right data from the beginning of a project saves time and resources.
Equally important to understand is the limitations of data. Simply waving a magic wand will not create the right data or machine to solve a problem. Companies will need to find the right set of engineers to bring their idea fruition.
3. If You Can’t Beat Them, Join Them
Last year, Elon Musk stated, “Robots will be able to do everything better than us.” While some may fear this means a loss of jobs, VentureBeat argues that the rise of artificial intelligence could actually be an opportunity to create a smarter, more skilled workforce of humans.
It’s no secret that AI systems are now researching and completing tasks faster and more efficiently than humans. As businesses shift operations to better utilize robots and automation for menial, repetitive tasks, workers will be forced to innovate and reshape their skill set.
According to McKinsey, jobs that are least threatened by artificial intelligence are “knowledge” jobs, such as management, decision making, relationship building and creative work. In fact, the rise of automation in the workplace should make human emotions and cognitive skills more valuable. Moving forward, workers with strong emotional intelligence, open-mindedness, self-awareness and adaptability will be coveted by organizations.
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