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Posts from the ‘Human Resource Managrement’ Category

What’s Next For You? How AI Is Transforming Talent Management

Bottom Line: Taking on the talent crisis with greater intelligence and insight, delivering a consistently excellent candidate experience, and making diversity and inclusion a part of their DNA differentiates growing businesses who are attracting and retaining employees. The book What’s Next For You? by Ashutosh Garg, CEO and Co-Founder and Kamal Ahluwalia, President of eightfold.ai provide valuable insights and a data-driven roadmap of how AI is helping to solve the talent crisis for any business.

The Talent Crisis Is Real

  • 78% of CEOs and Chief Human Resource Officers (CHROs) say talent programs are important, with 56% say their current programs are ineffective.
  • 83% of employees want a new job yet only 53% want to leave for a new company.
  • 57% of employees say diversity and inclusion initiatives aren’t working, and 40% say their companies lack qualified diverse talent.
  • Nearly 50% of an organizations’ top talent will leave their jobs in the first two years of being hired.
  • 28% of open positions today won’t be filled in the next 12 months.

The above findings are just a sample of the depth of data-driven content and roadmap the book What’s Next For You? delivers. Co-authors Ashutosh Garg’s and Kamal Ahluwalia’s expertise in applying AI and machine learning to talent management problems with a strong data-first mindset is evident throughout the book. What makes the book noteworthy is how the authors write from the heart first with empathy for applicants and hiring managers, supporting key points with data. The empathetic, data-driven tone of the book makes the talent crisis relatable while also illustrating how AI can help any business make better talent management decisions.

“Businesses are having to adapt to technology changes and changes in customer expectations roughly every 10 years – a timeframe that is continuing to shrink. As a result, business leaders need to really focus on rethinking their business strategy and the associated talent strategy, so they have the organizational capability to transform and capitalize on the inevitable technology shifts,” writes John Thompson, Venture Partner, Lightspeed Venture Partners and Chairman of the Board at Microsoft in the forward.

The book cites talent management researchers and experts who say “our current knowledge base has a half-life of about two years, and the speed of technology is outperforming us as humans because of what it can do quickly and effectively“ (p.64). John Thompson’s observations in the forward that the time available for adapting to change is shrinking is a unifying thread that ties this book together. One of the most convincing is the fact that using today’s Applicant Tracking Systems (ATS) and hiring processes prone to biases, there’s a 30% chance a new hire will not make it through their first year. If the new hire is a cloud computing professional, this equates to a median salary of $146,350 and taking best-case 46 days to find their replacement. The cost and time loss of losing just one recruited cloud computing professional can derail a project for months. It will cost at least $219,000 or more to replace just that one engineer. Any manager who has lost a new hire within a year can relate to how real the talent crisis is and how urgent it is to solve it.

The Half-Life Of Skills Is Shrinking Fast

The most compelling chapter of the book illustrates how today’s talent crisis can be solved by taking an AI-enabled approach to every aspect of talent management. Chapter 4, The Half-Life Of Skills Is Shrinking Fast, delves into how AI can find candidates who can unlearn old concepts, and quickly master new ones. The book calls out this attribute of any potential new hire as being essential for them to adapt.  Using higher quality data than is available in traditional ATS systems, the authors illustrate how AI-based systems can be used for evaluating both the potential and experiences of applicants to match them with positions they will excel in. The authors make a convincing argument that AI can increase the probability of new candidate success. They cite a well-known Leadership IQ statistic of 46% of all new employee hires failing to adapt within 18 months, and the Harvard Business Review study finding between 40% to 60% of new upper management hires fail within 18 months. The authors contend that even Leonardo Da Vinci, one of the primary architects of the Renaissance, would have trouble finding work using a traditional resume entered into an ATS system today because his exceptional capabilities and potential would have never been discovered. When our existing process of recruiting is based on practices over 500 years old, as this copy of Leonardo Da Vinci’s resume illustrates, it’s time to put AI to work matching peoples’ potential with unique position requirements.

When Employees Achieve Their Potential, Companies Do Too   

Attracting the highest potential employees possible and retaining them is the cornerstone of any digital business’ growth strategy today and in the future. The book addresses the roadblocks companies face in attaining that goal, with bias being one of the strongest. “For example, McKinsey & Co., a top consulting agency, studied over 1,000 companies across 12 countries and found that firms in the top quartile of gender diversity were a fifth more likely to have above-average profits than those in the bottom quartile,” (p. 105). Further, “diverse executive boards generate better financial returns, and gender-diverse teams are more creative, more productive and more confident.” (p. 105).

In conclusion, consider this book a roadmap of how hiring and talent management can change for the better based on AI. The authors successfully illustrate how combining talent, personalization at scale, and machine learning can help employees achieve their potential, enabling companies to achieve theirs in the process.

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Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

  • Hiring companies nationwide miss out on 50% or more of qualified candidates and tech firms incorrectly classify up 80% of candidates due to inaccuracies and shortcomings of existing Applicant Tracking Systems (ATS), illustrating how faulty these systems are for enabling hiring.
  • It takes on average 42 days to fill a position, and up to 60 days or longer to fill positions requiring in-demand technical skills and costs an average $5,000 to fill each position.
  • Women applicants have a 19% chance of being eliminated from consideration for a job after a recruiter screen and 30% after an onsite interview, leading to a massive loss of brainpower and insight every company needs to grow.

It’s time the hiring process gets smarter, more infused with contextual intelligence, insight, evaluating candidates on their mastery of needed skills rather than judging candidates on resumes that reflect what they’ve achieved in the past. Enriching the hiring process with greater machine learning-based contextual intelligence finds the candidates who are exceptional and have the intellectual skills to contribute beyond hiring managers’ expectations. Machine learning algorithms can also remove any ethic- and gender-specific identification of a candidate and have them evaluated purely on expertise, experiences, merit, and skills.

The hiring process relied on globally today hasn’t changed in over 500 years. From Leonardo da Vinci’s handwritten resume from 1482, which reflects his ability to build bridges and support warfare versus the genius behind Mona Lisa, Last Supper, Vitruvian Man, and a myriad of scientific discoveries and inventions that modernized the world, the approach job seekers take for pursuing new positions has stubbornly defied innovation. ATS apps and platforms classify inbound resumes and provide rankings of candidates based on just a small glimpse of their skills seen on a resume. When what’s needed is an insight into which managerial, leadership and technical skills & strengths any given candidate is attaining mastery of and at what pace.  Machine learning broadens the scope of what hiring companies can see in candidates by moving beyond the barriers of their resumes. Better hiring decisions are being made, and the Return on Investment (ROI) drastically improves by strengthening hiring decisions with greater intelligence. Key metrics including time-to-hire, cost-to-hire, retention rates, and performance all will improve when greater contextual intelligence is relied on.

Look Beyond Resumes To Win The War For Talent

Last week I had the opportunity to speak with the Vice President of Human Resources for one of the leading technology think tanks globally. He’s focusing on hundreds of technical professionals his organization needs in six months, 12 months and over a year from now to staff exciting new research projects that will deliver valuable Intellectual Property (IP) including patents and new products.

Their approach begins by seeking to understand the profiles and core strengths of current high performers, then seek out matches with ideal candidates in their community of applicants and the broader technology community. Machine learning algorithms are perfectly suited for completing the needed comparative analysis of high performer’s capabilities and those of candidates, whose entire digital persona is taken into account when comparisons are being completed. The following graphic illustrates the eightfold.ai Talent Intelligence Platform (TIP), illustrating how integrated it is with publicly available data, internal data repositories, Human Capital Resource Management (HRM) systems, ATS tools. Please click on the graphic to expand it for easier reading.

The comparative analysis of high achievers’ characteristics with applicants takes seconds to complete, providing a list of prospects complete with profiles. Machine learning-derived profiles of potential hires meeting the high performers’ characteristics provided greater contextual intelligence than any resume ever could. Taking an integrated approach to creating the Talent Intelligence Platform (TIP) yields insights not available with typical hiring or ATS solutions today. The profile below reflects the contextual intelligence and depth of insight possible when machine learning is applied to an integrated dataset of candidates. Please click on the graphic to expand it for easier reading. Key elements in the profile below include the following:

  • Career Growth Bell Curve – Illustrates how a given candidate’s career progressions and performance compares relative to others.

  • Social Following On Public Sites –  Provides a real-time glimpse into the candidate’s activity on Github, Open Stack, and other sites where technical professionals can share their expertise. This also provides insight into how others perceive their contributions.

  • Highlights Of Background That Is Relevant To Job(s) Under Review Provides the most relevant data from the candidate’s history in the profile so recruiters and managers can more easily understand their strengths.

  • Recent Publications – Publications provide insights into current and previous interests, areas of focus, mindset and learning progression over the last 10 to 15 years or longer.

  • Professional overlap that makes it easier to validate achievements chronicled in the resume – Multiple sources of real-time career data validate and provide greater context and insight into resume-listed accomplishments.

The key is understanding the context in which a candidate’s capabilities are being evaluated. And a 2-page resume will never give enough latitude to the candidate to cover all bases. For medium to large companies – doing this accurately and quickly is a daunting task if done manually – across all roles, all the geographies, all the candidates sourced, all the candidates applying online, university recruiting, re-skilling inside the company, internal mobility for existing employees, and across all recruitment channels. This is where machine learning can be an ally to the recruiter, hiring manager, and the candidate.

Five Reasons Why Machine Learning Needs To Make Resumes Obsolete

Reducing the costs and time-to-hire, increasing the quality of hires and staffing new initiatives with the highest quality talent possible all fuels solid revenue growth. Relying on resumes alone is like being on a bad Skype call where you only hear every tenth word in the conversation. Using machine learning-based approaches brings greater acuity, clarity, and visibility into hiring decisions.

The following are the five reasons why machine learning needs to make resumes obsolete:

  1. Resumes are like rearview mirrors that primarily reflect the past. What needed is more of a focus on where someone is going, why (what motivates them) and what are they fascinated with and learning about on their own. Resumes are rearview mirrors and what’s needed is an intelligent heads-up display of what their future will look like based on present interests and talent.
  2. By relying on a 500+-year-old process, there’s no way of knowing what skills, technologies and training a candidate is gaining momentum in. The depth and extent of mastery in specific areas aren’t reflected in the structure of resumes. By integrating multiple sources of data into a unified view of a candidate, it’s possible to see what areas they are growing the quickest in from a professional development standpoint.
  3. It’s impossible to game a machine learning algorithm that takes into account all digital data available on a candidate, while resumes have a credibility issue. Anyone who has hired subordinates, staff, and been involved in hiring decisions has faced the disappointment of finding out a promising candidate lied on a resume. It’s a huge let-down. Resumes get often gamed with one recruiter saying at least 60% of resumes have exaggerations and in some cases lies on them. Taking all data into account using a platform like TIP shows the true candidate and their actual skills.
  4. It’s time to take a more data-driven approach to diversity that removes unconscious biases. Resumes today immediately carry inherent biases in them. Recruiter, hiring managers and final interview groups of senior managers draw their unconscious biases based on a person’s name, gender, age, appearance, schools they attended and more. It’s more effective to know their skills, strengths, core areas of intelligence, all of which are better predictors of job performance.
  5. Reduces the risk of making a bad hire that will churn out of the organization fast. Ultimately everyone hires based in part on their best judgment and in part on their often unconscious biases. It’s human nature. With more data the probability of making a bad hire is reduced, reducing the risk of churning through a new hire and costing thousands of dollars to hire then replace them. Having greater contextual intelligence reduces the downside risks of hiring, removes biases by showing with solid data just how much a person is qualified or not for a role, and verifies their background strengths, skills, and achievements. Factors contributing to unconscious biases including gender, race, age or any other factors can be removed from profiles, so candidates are evaluated only on their potential to excel in the roles they are being considered for.

Bottom line: It’s time to revolutionize resumes and hiring processes, moving them into the 21st century by redefining them with greater contextual intelligence and insight enabled by machine learning.

 

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