Whether you're talking baseball or business, building a high-performance team is a difficult feat. Even the most advanced sabermetricians have a hard time calculating a winning formula. Not only are there tangibles to consider like skills, metrics, and past performance, but there are also intangibles such as life experiences, passions, and personas.

It's very rare for an organization to compile a "Moneyball" caliber team right off the bat, but these startups may have found ways to make that happen. By leveraging artificial intelligence (AI), machine learning, predictive analytics, and algorithms, you can decrease inefficiencies when it comes to identifying high potentials, hiring the right people, and building better performing teams.

Identifying High Potentials (Koru)

Organizations like Microsoft, Apple, and Google, receive hundreds-of-thousands of resumes each year. It's unworkable for them to look at every one. Historically, to narrow the field, factors like GPA, major, and college determined who made the short list. However, Google's former SVP of People, Laszlo Bock, discovered that these factors did little to predict their employees' future success and performance. Today, a good percentage of Googlers don't have college degrees, suggesting that many of these traditional factors have been replaced by other intangible factors like emotional intelligence.

One of the organizations that accepted the challenge of quantifying these intangibles was Koru. Starting with the largest consumers of talent (employers), Koru analyzed vast amounts of performance data, plugged numerous scenarios into an algorithm, and found that there are seven predictors of success. They use these traits to determine which candidates possess the desired potential. Through assessment, Koru leverages machine learning to help organizations measure these traits and zero in on the best talent for the job.

Hiring the "Right" Person (HireVue)

Once you have the right list of candidates to interview, it's even more labor intensive to interview them and pick the best one. Video interviewing is on its way to replacing traditional phone screens. Companies have discovered that rather than putting their applicants through the awkward conference call or forcing them to speak with multiple individuals, they can now record those interviews and share them with multiple stakeholders. It's made the process less taxing on all parties involved. Well, HireVue didn't stop there. They also use analytics and machine learning to look at behavioral attributes derived from those digital interactions. Yup, predictive analytics. Their process builds data models by linking things like verbal responses, nonverbal communication, and intonation (plus 25,000 other data points) to business outcomes like performance, turnover, and safety. When it's all said and done, their data models provide information to help you make faster, smarter hiring and developmental decisions.

Building Better Performing Teams (Collaboration.Ai)

But it's not enough to just fill your organization with top talent. To get the fullest potential out of your workforce, you'll have to find ways to cultivate engagement and activate your employees' capabilities through collaboration.

Using AI, Collaboration.Ai takes your existing HR data, survey input, objectives and social media insights and links them to employees' skills, networks, and feedback generating real-time intelligent teams. The result of which is extremely powerful.

For example, the World Economic Forum is widely known for its annual meeting in Davos. Thousands of private and public business leaders, economists, and international political leaders gather at Davos to tackle worldwide issues. With this many people, it's almost impossible for you to network with that "right" someone. So Collaboration.Ai partnered with The Value Web, a nonprofit global network of facilitators, to improve the experience for some of the attendees. They used social network analysis and machine learning to probe for hidden interests and critical connections between participants to link them together.

Through this effort, two founders from different countries were matched up and went on to build MUrgency, a global medical response network that serves hundreds of thousands of people.

Now, I'm not saying that shifting to AI will solve all your problems. These organizations and others like them run the risk of missing important nuances in the process -- especially when you're dealing with intangibles. While people can think on their feet, the machines' results will only be as good as the information that goes into them. I love the idea of leveraging science and concrete data to predict outcomes, but people aren't consonant. Until we can run every variation of every possible situation through algorithms, there's a possibility for errors.

Although the talent acquisition/management process is undoubtedly human, we can scale our efforts and improve efficiencies by leveraging AI. The benefits of lowering your time-to-fill ratios alone can save your organization thousands. For some revenue-generating positions, the cost could be as much as $5,000 per day the role goes unfilled.