3 essential questions to ask about deep-learning Talent Intelligence

Here’s how to use AI to deeply understand your workforce and transform hiring, managing, and developing talent using talent intelligence.

3 essential questions to ask about deep-learning Talent Intelligence

If you work in HR, chances are you’re familiar with AI in HR technology if you’re not using it already. According to Eightfold AI’s 2022 Talent Survey, 92 percent of HR executives plan to increase their use of AI in at least one area of their talent programs.

AI can profoundly impact an organization’s bottom line by driving efficiency and accuracy in decision-making. But nearly a quarter of business leaders report struggling to achieve meaningful results, according to a 2022 Deloitte report

That’s because the breadth and depth of AI platform capabilities vary. For example, when it comes to managing, acquiring, and developing talent, AI capabilities span from surface-level Boolean search job matching to deep-learning AI that predicts what people are capable of learning.

Talent leaders often struggle to keep up with labor market trends and evolving skills. It’s almost impossible for anyone to have a granular understanding of the skills and capabilities of everyone across their entire organization. Today’s talent professionals need a more intuitive, in-depth, and dynamic way to get that information so they can fill critical skills gaps fast, today and in the future. 

Given the current state of the economy, many talent leaders are being asked to do more with less, all while navigating hybrid work, converging industries, and creating an inclusive workplace. As a result, there’s a renewed focus on tactics like internal mobility, upskilling, and hiring contingent workers to retain top performers and maintain efficiency. 

But having a deep understanding of skills and learnability, career trajectories, and labor market trends for every employee in the organization — and every candidate in the talent pool — requires massive amounts of data.

Only deep-learning AI can effectively sort through billions of data points to inform decisions about workforce planning. Here are the top questions and answers about deep-learning AI and true talent intelligence so you know the difference.

How does AI reduce reliance on résumés?

Many job matching and internal talent marketplace services claim to use AI to filter résumés and identify the top candidates or employees for the job. However, rudimentary filtering that relies on keywords can exclude many qualified candidates. 

“There are people qualified with the right skills, but they get spit out because they don’t have the degree,” said Dane Linn, SVP of Corporate Initiatives for Immigration at Business Roundtable.

For example, an organization might include a job or project posting that says a bachelor’s degree is preferred but not required. As a result, applicants with the skills and experience needed might be pushed to the bottom of the list because they lack the education title on their résumé. 

Applicants shouldn’t be penalized for not including a keyword. Instead, deep-learning AI should use résumés as a base outline, fill in the gaps for talent leaders, and identify top candidates even if they don’t list a series of buzzwords. This way, organizations can spend less time scanning résumés and more time talking with candidates and learning about their skills.

3 Essential Questions to Ask About Deep-Learning Talent Intelligence

“The traditional résumé is in the process of being disrupted, but I don’t think it’s necessarily clear yet what the outcome will be,” said Kathryn Minshew, Founder and CEO at The Muse.  

Minshew said many companies rely on résumés because nothing better has come around. It’s hard to disrupt something without a better solution that appeals to hiring teams. 

How does AI distinguish between niche job titles?

The skills and responsibilities required to carry out a chief technology officer role at a 10-person startup will be drastically different from a CTO role at a Fortune 500 company. In another example, where job titles change over time, the skills and responsibilities of a senior engineer and a “coding ninja” could be identical. 

These may be obvious to talent professionals reviewing applications one by one. But many AI applications cannot distinguish between the skills and capabilities of two candidates with the same job title. So instead, they “check the box” on job titles and other qualifications, including education, which is a problem when talent leaders rely on AI to automate the sourcing function and identify qualified talent.

“Job titles have always changed with the times,” wrote reporter Emma Goldberg, who covers the future of work for The New York Times. “The growth of new technologies in the 1980s gave rise to chief information officers. The flow of political figures into tech turned everybody into a chief of staff. Competition for talent in recent years has morphed heads of human resources into chief people officers.” 

Goldberg said the COVID-19 pandemic contributed to several job title modifications. Candidates use titles like “remote manager or hybrid supervisor” to alert hiring managers about how they lead their teams. AI systems that can’t keep up with job title trends might filter out qualified candidates because they lack an exact match. 

3 Essential Questions to Ask About Deep-Learning Talent Intelligence

Inflated job titles are another issue affecting the job market. Many startups and small companies use titles to recruit employees as part of their benefits. Those organizations “weren’t necessarily able to compensate competitively, but they were handing out inflated titles,” said Shawn Cole, President and Co-Founder of national executive search firm Cowen Partners. 

This further highlights why it’s essential for AI in HR to look beyond job titles. Effective AI systems provide a three-dimensional look into experience levels and knowledge, not one-dimensional labels and keywords.  

How does AI identify potential and upskilling opportunities for employees?

At its core, succession planning is the process of identifying talent with a strong likelihood of succeeding in a role based on a combination of skills, experiences, and capabilities. In other words, someone who hasn’t done the job before but shows signs of high potential. 

For example, a VP-level engineering leader with experience managing a large team and complex products may be an excellent fit for a CTO role. But understanding and identifying those signs of potential — and proactively flagging which employees and candidates have it — requires deep-learning AI.

Deep-learning AI platforms excel at expanding opportunities for choice by considering how an adjacent skill set can transfer to an in-demand role.

“You can probably find examples in your own company of people who had far less experience than you’re requesting in a job description, but they learned the job and did very well,” said Omar L. Harris, author of Be a J.E.D.I. Leader, Not a Boss: Leadership in the Era of Corporate Social Justice, Equity, Diversity, and Inclusion. “You can take people from all walks of life and help them thrive.”

Hiring and promoting based on potential is highly valuable when organizations form new departments and growth areas. The key is also to identify upskilling opportunities to fill critical skill gaps. 

Consider a retail business that creates an e-commerce arm or digital-marketing team. The company might need to shift workers internally to develop these income channels. However, if the organization looks only for an exact combination of skills and experiences within its talent pool, it might come up empty-handed.

3 Essential Questions to Ask About Deep-Learning Talent Intelligence

Additionally, most employees are willing and excited to take a job that allows them to grow their skills. According to a 2022 report from Hays, 74 percent of employees say they would apply for a job even if they didn’t have the required skills, believing they would quickly learn them on the job.

“Beyond the task-specific skills, look for candidates with a growth mindset,” wrote Margaret M. Luciano and Max Watson in Harvard Business Review. “People with this mindset believe knowledge and abilities can be developed with effort.”

Not all AI is created equal

So how do you know if your AI platform is helping? One of the best ways to check its effectiveness is to look at the candidates and employees the system rejects. It could be time to reassess the platform if rejected candidate profiles are well-suited for the role, or could easily upskill to prepare for the position. 

How a platform sources data also highlights the potential effectiveness of AI. Siloed views of existing talent that don’t include labor market or competitor insights can’t give HR leaders the comprehensive understanding of all talent required to make informed decisions about talent processes and workforce planning. 

With deep-learning AI identifying high-potential candidates even if they don’t fit the exact mold, organizations can open up doors and completely transform the future of hiring and succession planning.

Check out our guide to deep-learning AI to help differentiate between the many offerings in the market today and understand what it can do for you.

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