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How To Reduce The Unemployment Gap With AI

How To Reduce The Unemployment Gap With AI

It’s time for AI startups to step up and use their formidable technology expertise in AI to help get more Americans back to work now.

Bottom Line: A.I.’s ability to predict and recommend job matches will help get more Americans back to work, helping to reduce the 22 million unemployed today.

One in ten Americans is out of work today based latest U.S. Department of Labor data. They’re primarily from the travel and hospitality, food services, and retail trade and manufacturing industries, with many other affected sectors. McKinsey & Company’s recent article, A new AI-powered network, is helping workers displaced by the coronavirus crisis provides context around the scope of challenges involved in closing the unemployment gap. McKinsey, Eightfold A.I., and the FMI – The Food Industry Association combined efforts to create the Talent Exchange, powered by Eightfold.ai in a matter of weeks. McKinsey insights across a broad base of industries to help Eightfold and FMI create the Talent Exchange in record time. “In talking with clients across the U.S., it became very clear that there is a huge labor mismatch, and individuals are being affected very differently—from retailers furloughing tens of thousands of workers to other organizations needing to hire more than 100,000 workers quickly. We’re excited to help bring a scalable offering to the market,” said McKinsey partner Andrew Davis. McKinsey and FMI collaborating with Eightfold speak volumes to how Americans are coming together to combat the COVID-19 fallout as a team.

And with the food & agriculture, transportation, and logistics industries considered essential, critical infrastructure by Cybersecurity and Infrastructure Security Agency (CISA), demand for workers is more urgent than ever. Eightfold’s Talent Exchange launched last weekend and already has more than 600,000 jobs uploaded that employers need to fill and is available in 15 languages. Eightfold is making the Talent Exchange available free of charge through the COVID-19 epidemic. The Talent Exchange is also being extended to other industries and eco-systems, illustrating how the Eightfold A.I. platform can provide transferability of skills across roles and industries.

Getting Americans Back To Work Using A.I.

Earlier this week Eightfold, FMI – The Food Industry Association and Josh Bersin, the noted global research analyst, public speaker, and writer on many aspects of human resources and talent management, hosted the webinar, COVID-19: Helping the food industry on the front lines with A.I. It’s available to watch here and includes a walk-through of the Eightfold Talent Exchange. The following graphic explains how the Talent Exchange addresses the needs of downsizing companies, impacted workers and hiring companies:

How To Reduce The Unemployment Gap With AI

Eightfold’s Talent Exchange Is A Model For How To Use A.I. For Good

Eightfold’s Talent Exchange uses A.I. algorithms to match candidates with available roles, based on each individual’s skills and previous experience.

Current employers who have to furlough or lay off employees can invite employees to participate in the program. Eightfold also designed in a useful feature that enables employers to add lists of impacted employees and send them a link to register for the Exchange. Employers can view their entire impacted workforce in a single dashboard and can filter by role, department, or location to see details about the talent needs from hiring companies and how their impacted employees are getting placed in new roles. The following is the Talent Exchange dashboard  for current employers showing progress in placing employees with furlough and outplacement partners, including the number of offers accepted by each:

How To Reduce The Unemployment Gap With AI

Employees impacted by a furlough or lay-off can create and update profiles free on the Talent Exchange, defining their job preferences, skills, and experience. That’s invaluable data for hiring companies relying on the platform to make offers and fill positions quickly.  How current employers handle furloughs and lay-offs today will be their identity for years to come, a point John Bersin made during the webinar saying “employers who thrive in the future are going to build long-term relationships with employees today.” Employees receive the following when their current employer adds their name to the Eightfold Talent Exchange. The fictional Company Travel Air is used for this example:

Hiring companies see candidate matches generated by the Exchange, so they can contact these prospects or immediately offer them new jobs. Eightfold’s A.I. engineering teams have automated and personalized this contact as well, expediting the process even further. Hiring companies can add onboarding instructions to allow new hires to start as soon as they are ready and have real-time views of their hiring dashboard shown below:

How To Reduce The Unemployment Gap With AI

Conclusion

Combining A.I.’s innate strengths with H.R. and talent management professionals’ expertise and insights is closing the unemployment gap today. Employers furloughing or laying off employees need to look out for them and get their profile data on the Talent Exchange, helping them find new jobs with hiring companies. As was so well-said by Josh Bersin during the webinar this week, “smart employers should think of their hourly workers as talent, not fungible, replaceable bodies.” For hiring companies in a war for proven employees with talent today, that mindset is more important than ever.

How AI & Machine Learning Are Redefining The War For Talent

These and many other fascinating insights are from Gartner’s recent research note, Cool Vendors in Human Capital Management for Talent Acquisition (PDF, 13 pp., client access reqd.) that illustrates how AI and machine learning are fundamentally redefining the war for talent. Gartner selected five companies that are setting a rapid pace of innovation in talent management, taking on Human Capital Management’s (HCM) most complex challenges. The five vendors Gartner mentions in the research note are AllyO, Eightfold, jobpal, Knack, and Vettd. Each has concentrated on creating and launching differentiated applications that address urgent needs enterprises have across the talent acquisition landscape. Gartner’s interpretation of the expanding Talent Acquisition Landscape is shown below (please click on the graphic to expand):

Source: Gartner, Cool Vendors in Human Capital Management for Talent Acquisition, Written by Jason Cerrato, Jeff Freyermuth, John Kostoulas, Helen Poitevin, Ron Hanscome. 7 September 2018

Company Growth Plans Are Accelerating The War For Talent

The average employee’s tenure at a cloud-based enterprise software company is 19 months; in the Silicon Valley, this trends to 14 months due to intense competition for talent according to C-level executives leading these companies. Fast-growing enterprise cloud computing companies and many other businesses like them need specific capabilities, skill sets, and associates who know how to unlearn old concepts and learn new ones. Today across tech and many other industries, every company’s growth strategy is predicated on how well they attract, engage, screen, interview, select and manage talent over associates’ lifecycles.

Of the five companies Gartner names as Cool Vendors in the field of Human Capital Management for Talent Acquisition, Eightfold is the only one achieving personalization at scale today. Attaining personalization at scale is essential if any growing business is going to succeed in attracting, acquiring and growing talent that can support their growth goals and strategies. Eightfold’s approach makes it possible to scale personalized responses to specific candidates in a company’s candidate community while defining the ideal candidate for each open position.

Gartner finds Eightfold noteworthy for its AI-based Talent Intelligence Platform that combines analysis of publicly available data, internal data repositories, HCM systems, ATS tools, and spreadsheets then creates ontologies based on organization-specific success criteria. Each ontology, or area of talent management interest, is customizable for further queries using the app’s easily understood and navigated user interface. Gartner also finds that Eightfold.ai is one of the first examples of a self-updating corporate candidate database. Profiles in the system are now continually updated using external data gathering, without applicants reapplying or submitting updated profiles. The Eightfold.ai Talent Intelligence Platform is shown below:

Taking A Data-Driven Approach to Improve Diversity

AI and machine learning have the potential to remove conscious and unconscious biases from hiring decisions, leading to hiring decisions based on capabilities and innate skills. Many CEOs and senior management teams are enthusiastically endorsing diversity programs yet struggling to make progress. AI and machine learning-based approaches like Eightfold’s can help to accelerate them to their diversity goals and attain a more egalitarian workplace. Data is the great equalizer, with a proven ability to eradicate conscious and unconscious biases from hiring decisions and enable true diversity by equally evaluating candidates based on their experience, growth potential and strengths.

Conclusion

At the center of every growing business’ growth plans is the need to attract, engage, recruit, and retain the highest quality employees possible. As future research in the field of HCM will show, the field is in crisis because it’s relying more on biases than solid data. Breaking through the barrier of conscious and unconscious biases will provide contextual intelligence of an applicant’s unique skills, capabilities and growth trajectories that are far beyond the scope of any resume or what an ATS can provide. The war for talent is being won today with data and insights that strip away biases to provide prospects who are ready for the challenges of helping their hiring companies grow.

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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